Q2 2026 Amplitude Inc Earnings Call

August 5, 2026

Speaker #2: Head of Investor Relations, and joining me today are Spenser Skates, CEO and co-founder of Amplitude, and Andrew Casey, Chief Financial Officer. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for the third quarter and full year 2026, the expected performance of our products, our expected quarterly and long-term growth, investments, and our overall future prospects.

John Streppa: Of investor relations, joining me today are Spenser Skates, Chief Executive Officer and Co-founder of Amplitude, and Andrew Casey, Chief Financial Officer. During today's call, management will make forward-looking statements, including statements regarding our financial outlook for Q3 and full year 2026, the expected performance of our products, our expected quarterly and long-term growth, investments, and our overall future prospects. These forward-looking statements are based on current information, assumptions, and expectations, and are subject to risks and uncertainties, some of which are beyond our control, that could cause actual results to differ materially from those described in these statements. Further information on the risks that could cause actual results to differ is included in our filings with the Securities and Exchange Commission.

Speaker #2: These forward-looking statements are based on current information assumptions and expectations, and are subject to risks and uncertainties, some of which are beyond our control.

Speaker #2: That could cause actual results to differ materially from those described in these statements. Further information on the risks that could cause actual results to differ is included in our filings with the Securities and Exchange Commission.

Speaker #2: You are cautioned not to place undue reliance on these forward-looking statements, and we assume no obligation to update these statements after today's call, except as required by law.

John Streppa: You are cautioned not to place undue reliance on these forward-looking statements, we assume no obligation to update these statements after today's call, except as required by law. Certain financial measures used on today's call are expressed on a non-GAAP basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be used in isolation from or as a substitute for financial information prepared in accordance with GAAP. Additional information regarding these non-GAAP financial measures and a reconciliation between these GAAP and non-GAAP financial measures are included in our earnings press release and the supplemental financial information, which can be found on our investor relations website at investors.amplitude.com. With that, I'll hand the call over to Spenser.

Speaker #2: Certain financial measures used on today's call are expressed on a non-GAAP basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends, and for internal planning and forecasting purposes.

Speaker #2: These non-GAAP financial measures have limitations and should not be used in isolation from or as a substitute for financial information prepared in accordance with GAAP.

Speaker #2: Additional information regarding these non-GAAP financial measures and a reconciliation between these GAAP and non-GAAP financial measures are included in our earnings press release and the supplemental financial information, which can be found on our investor relations website, at investors.amplitude.com.

Speaker #2: And with that, I'll hand the call over to Spenser.

Spenser Skates: Thanks, John, good afternoon, everyone. Welcome to Amplitude's Q2 2026 earnings call. Today, I'll cover three things. First, our Q2 results. Second, how we transformed Amplitude into an AI company and why every company I talk to now wants to learn how they can do the same. Third, a look at our product, and a spotlight on our customers. Let me start with the numbers. Q2 revenue was $101 million, up 21% year over year. Total annual recurring revenue was $410 million, up 22% year over year and up $36 million from last quarter. That was made up of two parts: inorganic ARR from Statsig of $17 million and organic ARR growth of $19 million. Andrew will walk through the details. Non-GAAP operating loss was $1.5 million. Customers with more than 100,000 in ARR grew to 824, an increase of 30% year over year.

Speaker #3: everyone. Welcome to Amplitude's second quarter Thanks, John, and good afternoon, 2026 earnings call. Today, I'll cover three things: first, our Q2 results; second, how we transformed Amplitude into an AI company and why every company I talk to now wants to learn how they can do the same; third, a look at our product and a spotlight on our customers.

Speaker #3: Let me start with the numbers. Q2 revenue was $101 million, up 21% year over year. Total annual recurring revenue was $410 million, up 22% year over year, and up 36 million from last quarter.

Speaker #3: That was made up of two parts: inorganic ARR from Statsig of $17 million and organic ARR growth of $19 million. Andrew will walk through the details.

Speaker #3: Non-GAAP operating loss was $1.5 million. Customers with more than 100K in ARR grew to $824, an increase of 30% year over year. Both AI natives and large enterprises are driving this growth.

Spenser Skates: Both AI natives and large enterprises are driving this growth. Let me step back and tell you about our transformation and then how we are helping customers along their AI journeys. We help companies build better products. Every company wants to transform to deliver software products in an AI native way. We have made that transformation at Amplitude over the last two years, and now our customers are looking to learn from us. Becoming an AI company starts with the organization. Two years ago, we first transformed our engineering team by bringing in AI engineers who built with it for years. We moved into adjacent functions like product management, design, and the more technical parts of go-to-market. We also brought in AI expertise through acquisition. Founders and other members of the team from these companies have taken leadership roles across Amplitude.

Speaker #3: Let me step back and tell you about our transformation and then how we're helping customers along their AI journeys. We help companies build better products.

Speaker #3: Every company wants to transform to deliver software products in an AI-native way. We've made that transformation at Amplitude over the last two years, and now our customers are looking to learn from us.

Speaker #3: Becoming an AI company starts with the organization. Two years ago, we first transformed our engineering team by bringing in AI engineers who built with it for years.

Speaker #3: Then we moved into adjacent functions like product management, design, and the more technical parts of go-to-market. We also brought in AI expertise through acquisition.

Speaker #3: Founders and other members of the team from these companies have taken leadership roles across Amplitude. I have focused on bringing in leaders who are former founders and who have a technical background.

Spenser Skates: I have focused on bringing in leaders who are former founders and who have a technical background. Gab, our Chief Product Officer, started multiple companies, including Loom Systems, which sold to ServiceNow in 2020. Nate, our Chief Commercial Officer, has a degree in math and physics and started his career as an engineer, programming in C++ and Java, and building databases. Most recently, we added Angela Ferrante as SVP of Marketing. Angela founded Laudable, which went through Y Combinator Summer 2021, sold it in 2025, and is a technical marketing leader who builds apps with AI in her spare time. To all of this, we are continually re-educating everyone at Amplitude through initiatives like AI Week, unlimited token spend, and a living token leaderboard. This has all resulted in 3 times the number of pull requests in 6 months.

Speaker #3: Gab, our Chief Product Officer, started multiple companies, including Loom Systems, which sold to ServiceNow in 2020. In addition, Nate, our Chief Commercial Officer, has a degree in math and physics and started his career as an engineer, programming in C++ and Java, and building databases.

Speaker #3: Most recently, we added Angela Ferrante as SVP of Marketing. Angela founded Laudable, which went through Y Combinator summer 2021, sold it in 2025, and is a technical marketing leader who builds apps with AI in her spare time.

Speaker #3: In addition to all of this, we're continually re-educating everyone at Amplitude through initiatives like AI Week, Unlimited Token Spend, and a Living Token Leaderboard.

Speaker #3: This is all resulted in three times the number of pull requests in six months. We've reduced our pull request cycle from five hours to 44 minutes.

Spenser Skates: We have reduced our pull request cycle from 5 hours to 44 minutes. Bug reports are down 55%. 5% of our pull requests are submitted from designers and product managers with no engineering involvement. We have leveraged AI to shorten our closing process by a day. We built customer health dashboards that enable our sellers and leaders to track customer usage, bringing our own Amplitude data alongside Salesforce data and data from other sources. When I talk with our customers, they are all focused on how they can transform their business to be AI native like we have done at Amplitude. The AI landscape is changing rapidly, and they want to learn how to adapt. Our customers are on a spectrum of AI adoption. Our job is to meet them where they are and then educate them on how to take the next step.

Speaker #3: Bug reports are down 55%. Five percent of our pull requests are submitted by designers and product managers without engineering involvement. We've leveraged AI to shorten our closing process by a day.

Speaker #3: We built customer health dashboards that enable our sellers and leaders to track customer usage, bringing our own Amplitude data alongside Salesforce data and data from other sources.

Speaker #3: When I talk with our customers, they are all focused on how they can transform their business to be AI-native like we have done at Amplitude.

Speaker #3: The AI landscape is changing rapidly, and they want to learn how to adapt. Our customers are on a spectrum of AI adoption. Our job is to meet them where they are and then educate them on how to take the next step.

Speaker #3: We work with leading AI companies to learn what the bleeding edge in product development looks like. We use that knowledge to educate the rest of the market, including the largest enterprises deploying at scale.

Spenser Skates: We work with leading AI companies to learn what the bleeding edge in product development looks like. We use that knowledge to educate the rest of the market, including the largest enterprises deploying at scale. More than 40 AI native companies now pay us over $100,000 a year. Those customers include Harvey, Midjourney, Character.ai, and one of the leading foundational AI model companies. On the enterprise side, enterprises are now more than 68% of our ARR. This quarter included agreements with Paramount, Jaguar Land Rover, and Domino's Pizza. We have improved our pricing and packaging. We reduced down to a single meter to make it simpler for enterprises to add additional products. We increased the amount of data on our free plan so we are the best for those just getting started. Amplitude has the best pricing, whether you are a startup or a large enterprises.

Speaker #3: More than 40 AI-native companies now pay us over $100,000 a year. Those customers include Harvey, Midjourney, Character.AI, and one of the leading foundational AI model companies.

Speaker #3: On the enterprise side, enterprises are now more than 68% of our ARR, this quarter included agreements with Paramount, Jaguar Land Rover, and Domino's Pizza.

Speaker #3: We've improved our pricing and packaging. We reduced down to a single meter to make it simpler for enterprises to add additional products. We increased the amount of data on our free plan, so we're the best for those just getting started.

Speaker #3: Amplitude has the best pricing, whether you're a startup or a large enterprise. One of the biggest changes with building an AI-native company that we're seeing at Amplitude and with our peers in the private markets is in the cost structure.

Spenser Skates: One of the biggest changes with building an AI native company we are seeing at Amplitude and with our peers in private markets is in the cost structure. A lot of inference spend is required in order to deliver AI native products, which increases the amount spent on cost of goods sold. On the other hand, you do not need to add as much operating expense to continue to grow a business at scale. We are embracing this change in cost structure as part of our transition to an AI native company. For now, we expect gross margins to stay in the low 70s. We will offset that with a commensurate reduction in operating expenses. That allows us to continue to show the same leverage in operating income as we have planned. I am continuing to drive Amplitude to a 20%+ operating margin business over the long term.

Speaker #3: A lot of inference spend is required in order to deliver AI-native products, which increases the amount spent on cost of goods sold. On the other hand, you do not need to add as much operating expense to continue to grow a business at scale.

Speaker #3: We are embracing this change in cost structure as part of our transition to an AI-native company. For now, we expect gross margins to stay in the low 70s.

Speaker #3: We will offset that with a commensurate reduction in operating expenses. That allows us to continue to show the same leverage as in operating income as we have planned.

Speaker #3: I am continuing to drive Amplitude to a 20%-plus operating margin business over the long term. We offer three products to meet customers wherever they are on their AI journey.

Spenser Skates: We offer three products to meet customers wherever they are on their AI journey. Amplitude gives you the deepest understanding of how people use your product. Our agents increasingly do that discovery for you. Statsig gives you feature flagging and experimentation built on the world's most advanced stats engine with an engineering-first view. It's also integrated natively with data warehouses. Wave is the future of product development, self-improving products where we automatically recommend what to build next based on signals from users. While we're early here, I'm actually excited to show you a demo today. Together, these three products close the product development loop: understand what's happening, measure what ships, and ship what matters. That loop is how AI native business is built. Let me go deeper on Amplitude. Global Chat is becoming the primary way our customers interact with their product data.

Speaker #3: Amplitude gives you the deepest understanding of how people use your product, our agents increasingly do that discovery for you. Statsig gives you feature flagging and experimentation, built on the world's most advanced stats engine with an engineering-first view.

Speaker #3: It's also integrated natively with data warehouses. Wave is the future of product development. Self-improving products where we automatically recommend what to build next based on signals from users.

Speaker #3: While we're early here, I'm actually excited to show you a demo today. Together, these three products close the product development loop. Understand what's happening, measure what ships, and ship what matters.

Speaker #3: That loop is how AI-native businesses build. Let me go deeper on Amplitude. Global Chat is becoming the primary way our customers interact with their product data.

Speaker #3: You ask it a question in plain language, and it does the analysis, no dashboard-building required. It's become the de facto way many companies do product analytics.

Spenser Skates: You ask it a question in plain language, it does the analysis, no dashboard building required. It's become the de facto way many companies do product analytics. Global Agent finds the root cause behind 75% of customer questions and hands you the answer. There are 1.3 million Global Agent interactions every week, and root cause discovery rates are improving by 1 percentage point every month. As of today, over 40% of all insights come from AI agents as opposed to humans, and we expect this to continue to grow. Today for our demo, I want to show you Custom Agents, Statsig, and Wave. Let's start with Custom Agents. Custom Agents are teammates that automate recurring workflows on your product data and push that work to other tools and systems. This is our chat interface. An increasing number of users are interacting with Amplitude mostly through chat and agents.

Speaker #3: Global Agent finds the root cause behind 75% of customer questions and hands you the answer. They are 1.3 million global agent interactions every week and root cause discovery rates are improving by 1 percentage point every month.

Speaker #3: As of today, over 40% of all insights come from AI agents as opposed to humans, and we expect this to continue to grow. Today, for our demo, I want to show you custom agents, Statsig, and Wave.

Speaker #3: Let's start with custom agents. Custom agents are teammates that automate recurring workflows on your product data and push that work to other tools and systems.

Speaker #3: This is our chat interface. An increasing number of users are interacting with Amplitude mostly through chat and agents. I'll ask a question. Which group of users are most likely to purchase next week?

Spenser Skates: I'll ask a question. Which group of users are most likely to purchase next week? Chat can now write its own code to perform this analysis. This unlocks the ability to run deeper analysis and create powerful new graphs and artifacts, including diagrams like you see here, out of time decile lift, an ROC curve, segment propensity. You can dig in by seeing the actual code used and step-by-step analysis. This type of deep analysis has never been available before in analytics tooling. We are no longer bound by the constraints of a UI. We can also create automatic and recurring agents that run in the background. I give it these instructions. I want this analysis run every Monday morning, cross-reference with marketing activity and Confluence. DM me the results in Slack. Amplitude then creates the agent that you see here.

Speaker #3: Chat can now write its own code to perform this analysis. This unlocks the ability to run deeper analysis and create powerful new graphs and artifacts, including diagrams like you see here out of time decile lift, an ROC curve, segment propensity.

Speaker #3: You can dig in by seeing the actual code used and step-by-step analysis. This type of deep analysis has never been available before in analytics tooling.

Speaker #3: We are no longer bound by the constraints of a UI. We can also create automatic and recurring agents that run in the background. I give it these instructions.

Speaker #3: I want this analysis run every Monday morning. Cross-reference with marketing activity and Confluence, DM me the results in Slack. Amplitude then creates the agent, that you see here.

Speaker #3: This is the entire prompt, including connectors to Atlassian and Slack. It will run regularly every Monday and push the results to me. We are building the best analytics agent across all data sources.

Spenser Skates: This is the entire prompt, including connectors to Atlassian and Slack. It will run regularly every Monday and push the results to me. We are building the best analytics agent across all data sources. Statsig is the leading product for experimentation and feature management. Statsig runs experiments natively on your cloud data warehouse, whether that is Snowflake, BigQuery, Databricks, or Redshift. Let me show you what this looks like. Here is the results page for one of hundreds of experiments that an e-commerce customer is running. This experiment is testing a larger product image versus the default size. There's a lot of statistical machinery behind a good experiment, but the UI makes it simple for an engineer to run. Up top, they can monitor exposure, which is saying the experiment is healthy or not.

Speaker #3: Statsig is the leading product for experimentation and feature management. Statsig runs experiments natively on your cloud data warehouse, whether that is Snowflake, BigQuery, Databricks, or Redshift.

Speaker #3: Let me show you what this looks like. Here is the results page for one of hundreds of experiments that an e-commerce customer is running.

Speaker #3: This experiment is testing a larger product image versus the default size. There's a lot of statistical machinery, behind a good experiment, but the UI makes it simple for an engineer to run.

Speaker #3: Up top, they can monitor exposure, which is saying if the experiment is healthy or not. We expect to see a 50/50 split, so we're doing good.

Spenser Skates: We expect to see a 50/50 split. We're doing good, and as you can see over here, we're getting a healthy check. We move to the scorecard that has the results. This has a confidence interval of 95%. Statsig uses advanced techniques like CUPED and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, we see specific events we're tracking for this experiment. We're seeing positive results. The checkout event is up by 27.4% ±2.3%. Cart conversion is up, total purchase dollars is up, while carts per session is down. For the rollout of this feature, we have a progressive rollout, starting with employees, moving to early access users, then early release, and a scheduled rollout for everyone else. Statsig has a variety of advanced experimentation capabilities for rollout, like feature gating, dynamic configs, and automatic rollbacks.

Speaker #3: And as you can see, over here, we're getting healthy check. We move to the scorecard that has the results. This has a confidence interval of 95%.

Speaker #3: Statsig uses advanced techniques like QPID and sequential testing that allows engineers to speed up time to decision. We have those turned on. In monitoring, we see specific events we're tracking for this experiment.

Speaker #3: We're seeing positive results. The checkout event is up by 27.4%, plus or minus 2.3%. Cart conversion is up, total purchase dollars is up, while carts per session is down.

Speaker #3: For the rollout of this feature, we have a progressive rollout starting with employees, moving to early access users, then early release, and a scheduled rollout for everyone else.

Speaker #3: Statsig has a variety of advanced experimentation capabilities for rollout, like feature gating, dynamic configs, and automatic rollbacks. Together, these are the mechanisms that a team uses to ship a change gradually, tune it while live, and pull back automatically it goes wrong.

Spenser Skates: Together, these are the mechanisms that a team uses to ship a change gradually, tune it while live, and pull back automatically if it goes wrong. Last, I want to show you Wave, the future of product development. Wave allows for self-improving products that automatically recommend what to build next based on signals from your users. Wave is magical. Wave looks across all the different data sources you have, analytics, experimentation, Session Replay, Guides and Surveys, feedback, and many others. It synthesizes that data into a set of product recommendations, plans those recommendations, and then helps you create those changes in your product. I'm going to walk you through a real example Wave suggested and built for Amplitude's documentation site. On our documentation site, Wave found a spike in failed searches through looking at Session Replay and analytics data.

Speaker #3: Last, I want to show you Wave the future of product development. Wave allows for self-improving products that automatically recommend what to build next based on signals from your users.

Speaker #3: Wave is magical. Wave looks across all the different data sources you have. Analytics, experimentation, session replay, guides and surveys, feedback, and many others. It then synthesizes that data into a set of product recommendations, plans those recommendations, and then helps you create those changes in your product.

Speaker #3: I'm going to walk you through a real example Wave suggested and built for Amplitude's documentation site. On our documentation site, Wave found a spike in failed searches through looking at session replay and analytics data.

Speaker #3: The core problem was that search on our docs page fired on every keystroke. Typing a single letter to start a search returned an empty no result state before the person finished typing their search, leading to a bad experience for users.

Spenser Skates: The core problem was that search on our docs page fired on every keystroke. Typing a single letter to start a search returned an empty no result state before the person finished typing their search, leading to a bad experience for users. Wave explains the reach of this issue. Every user who uses search, it has an expected impact of decreasing total search failures by 80%. Wave has automatically created a visual example of the problem below so it's easy to understand. It also has a full explanation of the evidence. For the plan, Wave sketches a wireframe of the recommended update, setting a three character minimum and a 200 millisecond debounce to trigger the search. Wave can also drive execution. It automatically created the pull request and Cursor wrote the code. Mark, our technical writer, was able to merge this pull request and ship this.

Speaker #3: Wave explains the reach of this issue. Every user who uses search, it is an impact, expected impact of decreasing total search failures by 80%.

Speaker #3: Then Wave has automatically created a visual example of the problem below, so it's easy to understand. It also has a full explanation of the evidence.

Speaker #3: For the plan, Wave sketches a wireframe of the recommended update, setting a three-character minimum and a 200 millisecond debounce to trigger the search. Wave can also drive execution.

Speaker #3: It automatically created the pull request and cursor wrote the code. Mark, our technical writer, was able to merge this pull request and ship this.

Speaker #3: No engineers, no designers, and no product manager. Finally, Wave measures the results of the change. There is a massive decrease in total search failures.

Spenser Skates: No engineers, no designers, and no product manager. Finally, Wave measures the results of the change. There is a massive decrease in toll search failures. Simply amazing. Now, let's talk about some of our customers. We had a great quarter with both new lands and expansions. We added or expanded our relationship with customers, including Paramount Global, Jaguar Land Rover, Teladoc Health, Chime, Disney Ad Platforms, F5 Networks, Coursera, Grammarly, Kraken, and Crunch Fitness, among others. I want to tell you three stories about how these customers are leveraging our platform. First is Coca-Cola FEMSA, which sells to hundreds of thousands of small shops across Latin America. Every shop is different, but for years, they had to run the same broad campaign to everyone because there was no way to tailor a message to that many retailers by hand. AI changed that.

Speaker #3: Simply amazing. Simply amazing. Now, let's talk about some of our customers. We had a great quarter with both new lands and expansions. We added or expanded our relationship with customers, including Paramount Global, Jaguar Land Rover, Teladoc Health, Chime, Disney Ad Platforms, F5 Networks, Coursera, Grammarly, Kraken, and Crunch Business, among others.

Speaker #3: I want to tell you three stories about how these customers are leveraging our platform. First is Coca-Cola FEMSA, which sells to hundreds of thousands of small shops across Latin America.

Speaker #3: Every shop is different. But for years, they had to run the same broad campaign to everyone because there was no way to tailor a message to that many retailers by hand.

Speaker #3: AI changed that. They began sending each retailer its own recommendation every week, written by AI. Their own teams were actually skeptical. A different message for every shop every week felt risky, and no one knew if it was going to work.

Spenser Skates: They began sending each retailer its own recommendation every week, written by AI. Their own teams were actually skeptical. A different message for every shop every week felt risky, and no one knew if it was going to work. They used Amplitude to find out. Their AI campaigns actually had an 11% click-through rate, four times higher than their previous approach. Our cohort analysis also showed that this lift lasted. Once a retailer engaged, its revenue stayed higher in the weeks that followed. That evidence turned skeptics at FEMSA into believers, and they scaled from a 2,500 store pilot to 690,000 retailers. The second is Replit. Replit is an AI app builder that allows non-technical builders to turn an idea into an app using AI. Replit has a large global user base of passionate builders that provide feedback.

Speaker #3: They used Amplitude to find out. Their AI campaigns actually had a 11% click-through rate four times higher than their previous approach. Our cohort analysis also showed that this lift lasted.

Speaker #3: Once a retailer engaged, its revenue stayed higher in the weeks that followed. That evidence turned skeptics at FEMSA into believers, and they scaled from a 2,500-store pilot to 690,000 retailers.

Speaker #3: The second is Replit. Replit is an AI app builder that allows non-technical builders to turn an idea into an app using AI. Replit has a large global user base of passionate builders that provide feedback.

Speaker #3: Replit is using Amplitude AI feedback to understand how customers are engaging with their agents. They've connected AI feedback to Zendesk, App Store reviews, Twitter, and Reddit, and surfaced and prioritized what problems should be solved to increase their retention and engagement.

Spenser Skates: Replit is using Amplitude AI Feedback to understand how customers are engaging with their agents. They've connected AI Feedback to Zendesk, App Store reviews, Twitter, and Reddit, and surfaced and prioritized what problems should be solved to increase their retention and engagement. It changed weeks of manual work on their end into a simple click with Amplitude. This is the next generation of product development at work. Third is The Economist. The Economist is a print magazine that's in the midst of a transition to digital delivery and subscription. Their research arm built an AI assistant called Lens that answers questions for analysts and strategists using The Economist's content. Their normal analytics could show what users did, but not whether the AI's answers were any good. The team was reading sessions by hand, but they couldn't keep up. Amplitude Agent Analytics now scores every answer Lens gives automatically.

Speaker #3: It changed weeks of manual work on their end into a simple click with Amplitude. This is the next generation of product development at work.

Speaker #3: Third is the Economist. The Economist is a print magazine that's in the midst of a transition to digital delivery and subscription. Their research arm built an AI assistant called Lens that answers questions for analysts and strategists using the Economist's content.

Speaker #3: Their normal analysts could show what users did, but not whether the AI's answers were any good. The team was reading sessions by hand, but they couldn't keep up.

Speaker #3: Amplitude agent analytics now scores every answer Lens gives automatically. They went from reading a handful of sampled sessions to being able to see across all of them.

Spenser Skates: They went from reading a handful of sample sessions to being able to see across all of them. Today, Lens holds a 96.9% task success rate, and weekly failures are down 84%. That is the loop working. Build with AI, measure whether it is good, and fix what is not. To wrap up, the companies on the bleeding edge are choosing Amplitude. We've transformed Amplitude to be AI native, and we're building the future on what can be done in analytics. Self-improving products are closer than ever with Wave. Our pace of innovation continues to accelerate, and we're building in a way that can scale with leverage. I am extraordinarily excited about what's ahead. With that, I'll hand it over to Andrew to walk you through the financials.

Speaker #3: Today, Lens holds a 96.9% task success rate and weekly failures are down 84%. That is the loop working. Build with AI, measure whether it is good, and fix what is not.

Speaker #3: To wrap up, the company's on the bleeding edge are choosing Amplitude. We've transformed Amplitude to be AI native, and we're building the future on what can be done in analytics.

Speaker #3: Self-improving products are closer than ever with Wave. Our pace of innovation continues to accelerate, and we're building in a way that can scale with leverage.

Speaker #3: I am extraordinarily excited about what's ahead. With that, I'll hand it over to Andrew to walk you through the financials.

Speaker #2: Thank you, Spencer. This was a strong quarter and a clear step forward in our execution. Bringing our vision of how products will increasingly be developed and improved.

Andrew Casey: Thank you, Spenser. This was a strong quarter and a clear step forward in our execution, bringing our vision of how products will increasingly be developed and improved. We crossed $100 million in quarterly revenue. ARR reached $410 million, growing over 22% with the addition of the ARR assumed from the Statsig business, and free cash flow was a record quarterly high of $23.7 million. We also returned $69 million in capital during the quarter as part of our share repurchase program. We accomplished these milestones while integrating the Statsig technology and customers, managing through our own AI native evolution, and implementing our new pricing and packaging strategy. AI is changing how customers use Amplitude. The more our customers build with AI, the more they need to measure.

Speaker #2: We crossed 100 million in quarterly revenue. ARR reached 410 million, growing over 22% with the addition of the ARR assumed from the stat sig.

Speaker #2: Business and free cash flow was a record quarterly high of 23.7 million. We also returned 69 million in capital during the quarter as part of our share repurchase program.

Speaker #2: We accomplished these milestones while integrating the stat sig technology and customers, managing through our own AI native evolution and implementing our new pricing and packaging strategy.

Speaker #2: AI is changing how customers use Amplitude. The more our customers build with AI, the more they need to measure. Customers that adopt our AI into their workflows run nearly 10x the number of analyses compared to those that are running things manually.

Andrew Casey: Customers that adopt our AI into their workflows run nearly 10X the number of analyses compared to those that are running things manually. This increases the value that customers receive from the data ingested into our platform and makes it more likely that they will both ingest larger amounts of data and expand into additional products, which is the basis of our growth. Our new pricing and packaging is working. It supports our market consolidation strategy by providing customers with a lower overall cost if they consolidate applications onto our platform. It provides customers greater cost predictability and simplifies the quoting process for our sellers. In Q2, 70% of the ARR we closed was on the new model, up from 25% in Q1. Now, 28% of our total ARR is on the new pricing and packaging.

Speaker #2: This increases the value that customers receive from the data ingested into our platform and makes it more likely that they'll both ingest larger amounts of data and expand into additional products, which is the basis of our growth.

Speaker #2: Our new pricing and packaging is working. It supports our market consolidation strategy by providing customers with a lower overall cost if they consolidate applications onto our platform.

Speaker #2: It provides customers greater cost predictability and simplifies the quoting process for our sellers. In the second quarter, 70% of the ARR reclosed was on the new model.

Speaker #2: Up from 25% in the first quarter. Now 28% of our total ARR is on the new pricing and packaging. This is leading to average ARR increasing, higher multi-product attach, and longer contract duration, which all contribute to greater durability of our revenue.

Andrew Casey: This is leading to average ARR increasing, higher multi-product attach, and longer contract duration, which all contribute to greater durability of our revenue. Our margins reflect a choice. These are investments we are making to drive future growth with increasing profitability. Our gross margin was down over 1 point versus Q1 due to the integration of Statsig. We are working to optimize the new hosting environment and cloud structure, but it will take some time to improve from the low 50s gross margin closer to our expectation of 70-plus for the Statsig business. We are also experiencing higher customer adoption of AI capabilities and greater data ingestion into our platform, which combined has increased our costs and reduced our gross margins by an additional 2 points versus Q1. We have long maintained that we will grow with leverage.

Speaker #2: Our margins reflect a choice. These are our investments we are making to drive future growth with increasing profitability. Our gross margin was down over 1 point versus Q1 due to the integration of stat sig.

Speaker #2: We are working to optimize the new hosting environment and cloud structure, but it will take some time to improve from the low 50s gross margin closer to our expectation of 70 plus for the stat sig business.

Speaker #2: We're also experiencing higher customer adoption of AI capabilities and greater data ingestion into our platform. Which combined has increased our cost and reduced our gross margins by an additional 2 points versus Q1.

Speaker #2: We have long maintained that we will grow with leverage. This investment in the cost of revenue places greater emphasis on the management of our operating expenses to a lower level in order to achieve the leverage.

Andrew Casey: This investment in the cost of revenue places greater emphasis on the management of our operating expenses to a lower level in order to achieve the leverage. In Q2, we have managed down our sales and marketing to below 40% of revenue and G&A to the low teens, which is contributing to an increase in operating margins. We will continue to manage both areas lower as percentage of revenue over time, and we will continue to invest in R&D to drive innovation. We are instrumenting our business to accelerate growth, capture market share, and show leverage. One key metric we monitor is the usage of data compared to the entitlement for our customers, as this is a primary monetization metric. Today, that metric is at an all-time high. This is the output from better pricing, packaging, and more usage driven by our AI features.

Speaker #2: In Q2, we've managed down our sales and marketing to below 40% of revenue and GNA to the low teens. Which has contributed to an increase in operating margins.

Speaker #2: We will continue to manage both areas lower as percentage of revenue over time, and we will continue to invest in R&D to drive innovation.

Speaker #2: We are instrumenting our business to accelerate growth, capture market share, and show leverage. One key metric we monitor is the usage of data compared to the entitlement for our customers, as this is a primary monetization metric.

Speaker #2: Today, that metric is at an all-time high. This is the output from better pricing, packaging, and more usage driven by our AI features. We have increased the durability of our business through our RPO growth and reinvented our internal processes to capture scalability that AI offers.

Andrew Casey: We have increased the durability of our business through our RPO growth and reinvented our internal processes to capture scalability that AI offers. We are running to the AI opportunity and taking share as we go. Turning to our Q2 results. As a reminder, all financial results that I'll be discussing, with the exception of revenue, are non-GAAP. Our GAAP financial results, along with a reconciliation between GAAP and non-GAAP results, can be found in our earnings press release and supplemental financials on the investor relations page of our website. Q2 revenue was $100.9 million, up 21% year-over-year and 8% quarter-over-quarter. Total ARR increased to $410 million exiting the Q2, an increase of 22% year-over-year and $36 million sequentially.

Speaker #2: We are running to the AI opportunity and taking share as we go. Turning toward our second quarter results, as a reminder, all financial results that I'll be discussing with the exception of revenue are non-GAAP.

Speaker #2: Our GAAP financial results, along with a reconciliation between GAAP and non-GAAP results, can be found in our earnings press release and supplemental financials on the Investor Relations page of our website.

Speaker #2: Second quarter revenue was 100.9 million, up 21% year over year, and 8% quarter over quarter. Total ARR increased to 410 million exiting the second quarter and increased of 22% year over year and 36 million sequentially.

Speaker #2: This includes 17 million of incremental ARR from the stat sig business compared to the 16 million we expected to add when we shared our first quarter earnings.

Andrew Casey: This includes $17 million of incremental ARR from the Statsig business compared to the $16 million we expected to add when we shared our Q1 earnings. Total remaining performance obligations grew 35% year-over-year to $483 million. Current RPO was up 30% year-over-year, and long-term RPO was up 47% year-over-year. Here are more details on the key elements of the quarter. We had a strong quarter for both new and expansion deals in the enterprise, and platform sales were again particularly strong. 48% of our customers now have multiple products with 80% of our ARR coming from that cohort. We have over 26% of our ARR from customers with 5 or more products, up 2x since the Q2 last year. In period, net dollar retention was 105% on a pro forma basis, led by cross-sell expansions across our customer base.

Speaker #2: Total remaining performance obligations grew 35% year over year to 483 million. Current RPO was up 30% year over year and long-term RPO was up 47% year over year.

Speaker #2: Here are more details on the key elements of the quarter. We had a strong quarter for both new and expansion deals in the enterprise and platform sales were again particularly strong.

Speaker #2: 48% of our customers now have multiple products with 80% of our ARR coming from that cohort. We have over 26% of our ARR from customers with five or more products, up 2X since the second quarter last year.

Speaker #2: In period net dollar retention was 105% on a pro forma basis, led by cross-sell expansions across our customer base. This pro forma basis includes stat sig and Amplitude customers.

Andrew Casey: This pro forma basis includes Statsig and Amplitude customers. Gross margin was 71% for the Q2, down approximately 4 points from the Q2 of last year and down 4 points sequentially. This was driven by continued growth in inference costs as customer adoption of our edge was accelerated along with the integration of the Statsig business and its hosting environment. Sales and marketing expenses were 39% of revenue, down from 44% in the Q2 of last year. G&A was 13% of revenue, down 1 point from the Q2 of last year. R&D was 21% of revenue, up approximately 3 points from the Q2 last year, reflecting investment to scale the Statsig opportunity and support for those customers. Total operating expenses were $73 million or 72% of revenue. Operating loss was $1.5 million or 1.4% of revenue.

Speaker #2: Gross margin was 71% for the second quarter, down approximately 4 points from the second quarter of last year and down 4 points sequentially. This was driven by continued growth in inference costs as customer adoption of our AI tools accelerated along with the integration of the stat sig business and its hosting environment.

Speaker #2: Sales and marketing expenses were 39% of revenue, down from 44% in the second quarter of last year. GNA was 13% of revenue, down 1 point from the second quarter of last year.

Speaker #2: R&D was 21% of revenue, up approximately 3 points from the second quarter of last year, reflecting investment to scale the stat sig opportunity and support for those customers.

Speaker #2: Total operating expenses were 73 million, or 72% of revenue. Operating loss was 1.5 million, or 1.4% of revenue. Net loss per share was negative 1 cent based on 129.4 million basic shares compared to 1 cent a year ago.

Andrew Casey: Net loss per share was -$0.01 based on 129.4 million basic shares compared to $0.01 a year ago. Free cash flow in the quarter was $23.7 million or 24% of revenue, compared to $18.2 million or 22% of revenue during the same period last year. We ended the quarter with approximately $162 million in cash and investments. We have conviction in the long-term value of our platform and have used and will use our cash to minimize the impacts of dilution. Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D roadmap when appropriate. Now, turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution.

Speaker #2: Free cash flow in the quarter was 23.7 million, or 24% of revenue, compared to 18.2 million, or 22% of revenue during the same period last year.

Speaker #2: We ended the quarter with approximately 162 million in cash and investments. We have conviction in the long-term value of our platform and have used and will use our cash to minimize the impacts of dilution.

Speaker #2: Our balance sheet position remains strong and allows us the opportunity to be more aggressive in our M&A strategy to accelerate our R&D roadmap when appropriate.

Speaker #2: Now, turning to our outlook. As a reminder, the philosophy of how we set guidance is through the lens of execution. We are pleased with our overall progress on consolidating point solutions to our core platform and the adoption of our different AI technologies.

Andrew Casey: We are pleased with our overall progress on consolidating point solutions to our core platform and the adoption of our different AI technologies. We've instrumented our business and selling process to make it easier to use more of our platform. We believe that we are well-positioned to continue to accelerate our growth in a profitable way. For Q3 2026, we expect revenue to be between $105.6 to 108 million, representing an annual growth rate of 21% at the midpoint. We expect non-GAAP operating income to be between $2.5 to 4.5 million, and we expect non-GAAP net income per share to be between $0.02 and $0.03, assuming a weighted average shares outstanding of approximately 133 million as measured on a fully diluted basis.

Speaker #2: We've instrumented our business and selling process to make it easier to use, more of our platform. We believe that we are well positioned to continue to accelerate our growth in a profitable way.

Speaker #2: For the third quarter of 2026, we expect revenue to be between 105.6 and 108 million, representing an annual growth rate of 21% at the midpoint.

Speaker #2: We expect non-GAAP operating income to be between $2.5 million and $4.5 million. And we expect non-GAAP net income per share to be between $0.02 and $0.03, assuming a weighted average shares outstanding of approximately 133 million, as measured on a fully diluted basis.

Speaker #2: For the full year, 2026, we are raising our expectation for full year revenue based on the performances in the second quarter to be between 407.2 and 411.2 million, an annual growth rate of 19% at the midpoint.

Andrew Casey: For the full year 2026, we are raising our expectation for full year revenue based on the performances in Q2 to be between $407.2 to 411.2 million, an annual growth rate of 19% at the midpoint. We are also raising our expectation for the full-year non-GAAP operating income due to performances in Q2 and actions taken in H1 to be between $6.3 to 9.3 million. We expect non-GAAP net income per share to be between $0.06 and $0.08, assuming weighted average shares outstanding of approximately 137.1 million as measured on a fully diluted basis. In closing, we are accelerating our pace of innovation, and we're growing the value that we can deliver to our customers. We have confidence in our ability to scale a durable and growing business while also bringing agentic analytics to the world.

Speaker #2: We are also raising our expectation for the full year non-GAAP operating income due to performances in the second quarter and actions taken in the first half to be between 6.3 million and 9.3 million.

Speaker #2: We expect non-GAAP net income per share to be between 6 cents and 8 cents, assuming weighted average shares outstanding of approximately 137.1 million as measured on a fully diluted basis.

Speaker #2: In closing, we are accelerating our pace of innovation, and we're growing the value that we can deliver to our customers. We have confidence in our ability to scale a durable and growing business while also bringing agentic analytics to the world.

Speaker #2: With that, we'll open up for Q&A over to you, John.

Andrew Casey: With that, we'll open up for Q&A. Over to you, John.

Speaker #1: Thank you, Andrew. Going to Q&A. For the sake of time, please limit yourself to one question and one follow-up. Our first question today will come from the line of Mark Cash from Raymond James, followed by Jackson Ader at KeyBank.

John Streppa: Thank you, Andrew. Going to Q&A. For the sake of time, please limit yourself to one question and one follow-up. Our first question today will come from the line of Mark Cash from Raymond James, followed by Jackson Ader at KeyBank. Mark, your line is now open.

Speaker #1: Mark, your line is now open.

Speaker #2: Thanks, John. Yeah, if I could start with Spenser, I really wanted to ask around wave. I appreciate it's still limited beta, though I think you've been using internally for several months now.

Mark Cash: Thanks, John. If I could start with Spenser. I really wanted to ask around Wave. I appreciate it's still limited data, but I think you've been using internally for several months now.

Spenser Skates: Yeah.

Speaker #2: I guess, do you see wave if it caused maybe a shift company shifting away from using bespoke agents for specific use cases towards a broader AI native product development platform from what you're seeing?

Mark Cash: Do you see Wave, it could cause maybe a company shifting away from using bespoke agents for specific use cases towards a broader AI native product development platform from what you're seeing? If so, how could that change your buyer, maybe the budgets you see in the addressable market over time?

Speaker #2: And if so, how could that change your buyer, maybe the budgets you see and the addressable market over time?

Spenser Skates: When you say bespoke, say more on that.

Speaker #3: When you say bespoke, say more on that.

Speaker #2: Yeah, instead of using particular agents to do a specific task underlying, because you have a swarm of agents doing things underneath for wave, so.

Mark Cash: Instead of using particular agents to do a specific task underlying, because you have a swarm of agents doing things underneath the Wave.

Speaker #3: I see what you're saying. I see. Okay. So yeah, let me separate out a few different things. What we have on the Amplitude side and I showed with custom agents is you have these agents that can look across your data and find insights for you and get to the root cause of questions and do that on a regular basis and kind of send it out.

Spenser Skates: I see what you're saying. I see. Okay. Yeah, let me separate out a few different things. What we have on the Amplitude side, and I showed with Custom Agents, is you have these agents that can look across your data and find insights for you and get to the root cause of questions and do that on a regular basis and kind of send it out. With what Wave is doing in particular, to your point is, it's looking at all your data all the time and then saying, here are points of friction. Here's something that's not working how it should be. Here's a feature that I think you should emphasize more. Here's something that I think is a best practice that you're not doing. It's operating at a higher level.

Speaker #3: What Wave is doing in particular, to your point, is it's looking at all your data all the time and then saying, "Hey, here are points of friction."

Speaker #3: Here's something that's not working how it should be. Here's a feature that I think you should emphasize more. Here's something that I think is a best practice that you're not doing." And so it's operating at a kind of higher level.

Speaker #3: In terms of the persona, I think what we're seeing as a convergence between engineers, product managers, and designers into this AI builder persona, it's not really like you have engineers who are thinking about what to build and you have product managers who are also just chipping code.

Spenser Skates: In terms of the persona, I think what we're seeing is a convergence between engineers, product managers, and designers into this AI builder persona. It's not really like you have engineers who are thinking about what to build, and you have product managers who are also just chipping code. The best, if you look at where the AI native teams that everyone's aspiring to be, these roles are melding.

Speaker #3: And so the best if you look at where the AI native teams that everyone's aspiring to be, these roles are melding. So it's still the same problem we're solving, which is how do we help you build a better product, but we're just automating more of it because we're saying, "Hey, we're going to look at all the data, all the time, and then suggest recommendations." That I'm I've been we've been talking about self-improving products here at Amplitude for about nine years.

Spenser Skates: It's still the same problem we're solving, which is how do we help you build a better product, but we're just automating more of it because we're saying, we're going to look at all the data all the time and then suggest recommendations. We've been talking about self-improving products here at Amplitude for about 9 years, I'm actually been blown away by what is possible with the technology today, where it's the perfect problem for AI in a lot of ways. The data sets are massive and complex, you can't get any human to look at them. The synthesis of, okay, here's what I think could be better and best practices is actually extraordinarily impressive. What that means is that just by the fact that someone is using your software, it's getting better because it's just translating recommendations.

Speaker #3: And so I'm actually been blown away by what is possible with the technology today, where it's just it's the perfect problem for AI in a lot of ways.

Speaker #3: The data sets are massive and complex. So you can't get any human to look at them. And then the synthesis of, "Okay, here's what I think could be better and best practices," is actually extraordinarily impressive.

Speaker #3: And so what that means is that just by the fact that someone is using your software, it's getting better because it's just translating your recommendations.

Speaker #3: You no longer need someone to go into an Amplitude or to any data system and say, "Oh, here's what my interpretation of these results." So I do think in terms of budget and persona, I do think, again, that that means instead of having these distinct roles, you have engineering, product management, and design merge you're still doing digital product development and that still rolls up to some leader.

Spenser Skates: You no longer need someone to go into an Amplitude or to any data system and say, oh, here's what my interpretation of these results. I do think, in terms of budget and persona, I do think, again, that means instead of having these distinct roles, you have engineering, product management, and design merge. You're still doing digital product development, and that still rolls up to some leader, the same executive before. Yeah, the way you do it looks different. Did I hit on what you're looking for?

Speaker #3: The same executive before. But yeah, the way you do it looks different. Did I hit on what you're looking for?

Speaker #2: Yeah, absolutely. Thank you for that. And if I could follow up with Andrew real quick—if my math is correct, the guidance for the year was raised at more than two times the beat for revenue and operating income.

Mark Cash: Yeah, absolutely. Thank you for that. If I could follow up with Andrew real quick. If my math is correct, the guidance for the year was raised at more than two times the beat for revenue and operating income. I was wondering if you could just go through the key drivers of lifting for expectations, why you saw some pressure on pro forma expansion sequentially there in the quarter, and then what you consider regarding margin leverage or levers while you're facing COGS pressure and ramping token spend internally. Thank you.

Speaker #2: So I was wondering if you could just go to the key drivers of lifting growth expectations while you saw some pressure on pro forma expansion sequentially there in the quarter.

Speaker #2: And then what you're considering regarding margin leverage or levers while you're facing COGS pressure and ramping token spend internally. Thank you.

Speaker #4: Yeah, sure. So, a couple of things. One, when we look at our ability to actually generate revenue in the out quarters, we start with the strong balances we're booking that are showing up in our RPO.

Andrew Casey: Yeah, sure. A couple things. One, that when we look at our ability to actually generate revenue in the out quarters, we start with the strong balances we're booking that are showing up in our RPO. When you've got commitments from customers for a longer term duration, you start to have better and better predictability about your future revenue. That's the first thing, and it's one of the reasons why we emphasize that metric so much. The second thing is we look at how much our customers are actually responding to some of the initiatives we're putting out, and that comes in the form of our new product capabilities, our new pricing and packaging, areas where our sales team is running new promotions and activities.

Speaker #4: When you've got commitments from customers for a longer-term duration, you start to have better and better predictability about your future revenue. So that's the first thing.

Speaker #4: It's one of the reasons why we emphasize that metric so much. The second thing is we look at how much are customers actually responding to some of the initiatives we're putting out in.

Speaker #4: And that comes in the form of our new product capabilities, our new pricing new packaging, areas where our sales team is running new promotions and activities.

Speaker #4: All those are bolstering our ability to see a stronger and stronger pipeline. And that pipeline progresses faster through its stages, which gives us greater and greater confidence that we'll add more and more in new ARR.

Andrew Casey: All those are bolstering our ability to see a stronger and stronger pipeline, and that pipeline progresses faster through its stages, which gives us greater and greater confidence that we'll add more and more in net new ARR. Now, from a revenue perspective, as you know, the predominance of our business is all coming from our subscription revenue. Those key factors on understanding, what's the baseline, what can you see in your pipeline, what you expect to convert is what I refer to as our ability to go execute against the plans that are in front of us. Sales teams have been doing a really good job of driving consolidation in the market, and that alone with our products is driving great conversions. That's the first thing.

Speaker #4: Now, from a revenue perspective, as you know, the predominance of our business is all coming from our subscription revenue. So those key factors on understanding what's the baseline, what can you see in your pipeline, what you expect to convert, is what I refer to as our ability to go execute against the plans that are in front of us.

Speaker #4: And sales teams have been doing a really good job of driving consolidation in the market. And that alone with our products is driving great conversions.

Speaker #4: So that's the first thing. On some of the margin areas, I would tell you, look, we just in the case of the Google environment that we got for the stat sig, we're going to be focused on driving optimizations in that environment over a period of time.

Andrew Casey: On some of the margin areas, I would tell you, look, in the case of the Google environment that we got where the stats say, we're going to be focused on driving optimizations in that environment over a period of time. It's definitely lower. We said in the low 50s from a gross margin perspective. That comes from us taking on a whole new environment. Most of Amplitude, all of it, in fact, is on AWS. We took on a whole new cloud and hosting environment, and you have to go through the paces of really optimizing how you run those environments for customers. Our first objective was integrating, making sure there were no disruption in service. Now we're moving quickly into how we can optimize those environments.

Speaker #4: It's definitely lower. We said in the low 50s from a gross margin perspective. That comes from us taking on a whole new environment. Most of Amplitude all of it, in fact, is on AWS.

Speaker #4: So we took on a whole new cloud and hosting environment. And you have to go through the paces of really optimizing how you run those environments for customers.

Speaker #4: Our first objective was integrating, making sure there were no disruption of service. Now we're moving quickly into how we can optimize those environments. So that's one big labor on the gross margin side.

Andrew Casey: That's one big labor on the gross margin side, and we're constantly looking at how we can make investments to go drive greater efficiencies across all of our operating expense areas.

Speaker #4: And we're constantly looking at how we can make investments to go drive greater efficiencies across all of our operating expense areas.

Speaker #1: Great. Thank you, Mark. Our next question will come from the line of Jackson Ader from KeyBank, followed by Scott Berg. Go ahead, Jackson.

John Streppa: Great. Thank you, Mark. Our next question will come from the line of Jackson Ader from KeyBank, followed by Scott Berg. Go ahead, Jackson.

Speaker #5: Hey, thanks, guys. Good to see you. I was curious on, I guess, Andrew, kind of sticking with you and talking about rather than on the COGS side, just on the operating expense side.

Jackson Ader: Hey, thanks guys. Good to see you. I was curious on, I guess, Andrew, kind of sticking with you and talking about rather than on the COGS side, just on the operating expense side. We've seen really nice acceleration in organic ARR from the business. If I take a longer-term view, even on a non-GAAP basis, we're still around breakeven. Right? I'm curious, as you're thinking about driving more leverage and more incremental margin that you talked about before on the income statement, what kind of impact should we expect that to have on the organic growth number, if at all?

Speaker #5: We've seen really nice acceleration in organic ARR, from the business. longer-term view, even on a non-gap basis, we're still around break even, right? And so I'm curious, as you're thinking about driving more leverage and more incremental margin that you talked about before, on the income statement, what kind of impact should we expect that to have on the organic growth number, if at all?

Speaker #4: Well, I would tell you that, one, we still expect from an organic perspective, we've got a great set of products. Spenser just walked through a number of them that are brand new to the market.

Andrew Casey: Well, I'd tell you that, one, we still expect from an organic perspective, we've got a great set of products. Spenser just walked through a number of them that are brand new to the market. We think they have enormous total addressable market that we can go after. Revenue growth will be the predominance where we'll see increasing operating income. As far as leverage as a percentage of what that would be, percentage of operating income, I do expect over time that we'll be able to drive better and better cost of revenue and increase gross margins over time. It just takes time to go do those things, especially when you're seeing such a demand inflection from customers and increasing data lines. As I mentioned, we're at an all-time high for the amount of data ingested in the platform versus entitlements.

Speaker #4: We think they have enormous total addressable market that we can go after. So revenue growth will be the predominance where we'll see increasing operating income.

Speaker #4: As far as leverage as a percentage of what that would be percentage of operating income, I do expect over time that we'll be able to drive better and better gross cost of revenue increase gross margins over time.

Speaker #4: It just takes time to go do those things, especially when you're seeing such a demand inflection from customers. And increasing data lines. As I mentioned, we're at an all-time high for the amount of data ingestion in the platform versus entitlements.

Speaker #4: When I first joined, that was in the low 60s. We're well into the 80s now, as far as the percentage of what customers have ingested versus what their entitlements are.

Andrew Casey: When I first joined, that was in the low 60s. We're in well into the 80s now as far as percentage of what customers have ingested versus what their entitlements are, and that portends increasing expansions on upsell, which is usually where we've had a lot of problems in the past of overselling and having to right-size contracts. For the first time, we're past those things, and we're starting to see really good upsell, not just cross-sell, driving growth. Revenue growth is the predominance of the first aspect of driving improving profitability. As far as the leverage goes, I think gross margins will improve over time. It's just going to take a while, and we still have a long way to go on sales and marketing, is reducing that as a percentage of revenue.

Speaker #4: And that portends increasing expansions on upsell, which is usually where we've had a lot of problems in the past with overselling and had to do right-size contracts.

Speaker #4: The first time we're past those things and we're starting to see really good upsell, not just cross-sell, driving growth. So revenue growth is the predominance of the first aspect of driving improving profitability.

Speaker #4: As far as the leverage goes, I think gross margins will improve over time. It's just going to take a while. And we still have a long way to go on sales and marketing as reducing that as a percentage of revenue.

Speaker #4: I think G&A has room and I do think that over time we'll see greater and greater efficiencies with the R&D organization as they adopt more and more capabilities to build products that are faster rate.

Andrew Casey: I think G&A has room, and I do think that over time, we'll see greater and greater efficiencies with the R&D organization as they adopt more and more capabilities to build products at a faster rate.

Speaker #5: Okay. And then just a quick follow-up. Can you remind us, should there be any now that we're on a different kind of pricing and packaging model, a little bit more variable, I guess, if you will, but should there be any difference in terms of the seasonality of your revenue ramp or recognition as we move forward with the new packaging?

Jackson Ader: Okay. Just a quick follow-up. Can you remind us, now that we're on a different kind of pricing and packaging model, a little bit more variable, I guess, if you will, should there be any difference in terms of the seasonality of your revenue ramp or recognition as we move forward with the new packaging?

Speaker #4: So on revenue, I'd say you get a fairly predictable pattern under which revenue is recognized because, like I said, most of our revenue in the future periods is designated by our RPO, the committed contracts.

Andrew Casey: On revenue, I'd say you get a fairly predictable pattern under which revenue's recognized, because as I said, most of our revenue in the future periods is designated by our RPO, the committed contracts. ARR will follow a very typical seasonal pattern. My expectations, a bit more on the enterprise selling basis. Q1 will always be our weakest as far as net new ARR adds, as we're adding new territories, adding new reps, implementing new strategic initiatives. This year in particular, we're educating the sales teams on not only the new pricing and packaging, but a lot of the new products we have. Every year, you're going to have that, and it'll be a slow start and then pick up.

Speaker #4: But ARR will follow a very typical seasonal pattern. My expectation is that more on the enterprise selling basis, Q1 will always be our weakest as far as net new ARR ads as we're adding new territories, adding new reps, implementing new strategic initiatives.

Speaker #4: This year in particular, we're educating the sales teams on not only the new pricing and packaging, but a lot of the new products we have.

Speaker #4: So every year you're going to have that. And so it'll be a slow start and then pick up. This year in two, just to remind everybody, we also had some big changes in our sales and marketing leadership, which is predominance of what you see now flowing through and a cost benefit from a lower sales and marketing as a percentage of revenue.

Andrew Casey: This year, too, just to remind everybody, we also had some big changes in our sales and marketing leadership, which is predominance of what you see now flowing through and a cost benefit from a lower sales and marketing as a percentage of revenue. That's from efficiencies we're driving.

Speaker #4: And that's from efficiencies we're driving.

Speaker #1: Great. Thank you, Jackson. Our next question will come from the line of Scott Berg from Needham, followed by Billy Fitzsimmons. Go ahead, Scott.

John Streppa: Great. Thank you, Jackson. Our next question will come from the line of Scott Berg from Needham, followed by Billy Fitzsimmons. Go ahead, Scott.

Speaker #6: Hi, Spenser and Andrew, nice quarter. Thanks for taking my questions. I wanted to follow up on sales enablement that Andrew was chatting about there.

Scott Berg: Hi, Spenser and Andrew. Nice quarter. Thanks for taking my questions. I wanted to follow up on sales enablement that Andrew was just chatting about there. We did a couple different customer checks in the quarter, and the one thing that we came back is, I don't think your existing customers are quite aware of all the different modules and innovation that you've rolled out this year.

Speaker #6: We did a couple of different customer checks in the quarter. And the one thing that we came back is I don't think your existing customers are quite aware of all the different modules and innovation that you've rolled out this year.

Spenser Skates: Yeah, totally.

Speaker #6: I see Spenser smiling. Is I know that's a function of time, obviously. And one customer didn't even know that you had acquired Statsig. So I guess where are you kind of in that journey?

Scott Berg: I see Spenser smiling. I know that's a function of time, obviously, and one customer didn't even know that you had acquired Statsig. I guess, where are you in that journey? When is the sales force properly ramped in that? The quarter sales results were good as is, obviously better awareness there can be even more helpful.

Speaker #6: Where do you when is the Salesforce properly ramped in that? I mean, the quarter's sales results were good as is, but obviously better awareness there can be even more helpful.

Speaker #3: Yeah, yeah. To your point, I think a lot of people still bucket us in the analytics company and it drives me absolutely crazy. Honestly, just sharing, hey, we have Statsig now and this is bleeding edge feature experimentation.

Spenser Skates: To your point, I think a lot of people still bucket us in the analytics company, it drives me absolutely crazy. Honestly, just sharing, "Hey, we have Statsig now, and this is bleeding edge feature experimentation, and you can use it too, and this is the same infrastructure OpenAI runs internally." Like, awesome. A lot of customers don't even know that. Same with Wave. I think they're just starting to understand Wave, same with our other products. I think if you remember from the prepared remarks, we do see ramping, we're moving customers from one to two to three to four to five to more products, it's much slower, that drives me crazy. I think there is no substitute for the work of like, hey, we built something amazing.

Speaker #3: And you can use it too. And this is the same infrastructure OpenAI runs internally, like awesome. A lot of customers don't even know that.

Speaker #3: And then, same with Wave. I think they're just starting to understand Wave, and then same with our other products. I think if you remember from the prepared remarks, we do see ramping.

Speaker #3: So we're moving customers from one to two to three to four to five to more products, but it's much slower and that drives me crazy.

Speaker #3: I think it is there is no substitute for the work of like, hey, we built something amazing. We have to educate the hundreds of people we have in our field.

Spenser Skates: We have to educate the hundreds of people we have in our field, they have to educate the thousands of customers in market. That's just work. That's just a whole thing. Something I'm spending a lot of time with Nate, our Chief Commercial Officer, as well as the rest of the executive team on in terms of how do we get that and do that more efficiently. We just had a kickoff a few weeks ago where we showed off a lot of what you saw today with Statsig and Wave and Custom Agents. That's not even to say all the other products we have, like Session Replay, Guides and Surveys, and AI Feedback that can displace point solutions. Anyway, that is I think last year we said it was the year of the platform.

Speaker #3: And then they have to educate the thousands of customers and market. That's just work and that's just a whole thing. Something I'm spending a lot of time with Nate, our chief commercial officer, as well as the rest of the executive team on in terms of how do we get that and do that more efficiently.

Speaker #3: We just had a kickoff a few weeks ago where we showed off a lot of what you saw today with Statsig and Wave and custom agents.

Speaker #3: But that's not even to say all the other products we have, like session replay and guides and surveys and AI feedback that can displace point solutions.

Speaker #3: So anyway, that is, I think last year we said the year was the year of the platform. I think we still have a ways to go on educating people on it.

Spenser Skates: I think we still have a ways to go on educating people on it. I will say that the good news on it is the main thing customers are looking for is, "Hey, prove to me you guys are at the bleeding edge of where this field is going." My view is that analytics and the whole behavioral data ecosystem is going to go through the same shift that coding has in the last two years. That is still going to happen.

Speaker #3: I will say that the good news on it is the main thing customers are looking for is a proof to me you guys are at the bleeding edge of where this field is going.

Speaker #3: And so, my view is that analytics and the whole behavioral data ecosystem are going to go through the same shift that coding has in the last two years.

Speaker #3: That is still going to happen. And so they want to we see it in a lot of the stuff we've been demoing and our customers see it too.

Spenser Skates: We see it in a lot of the stuff we've been demoing, and our customers see it too. They want to know, "Hey, am I working with the company that's bleeding edge on this?" Even if they're not necessarily ready to adopt a Wave or even a Statsig, I know that, okay, you at least help me take the first step to using some of the basics on these capabilities, and then I can add more, even if it's maybe too overwhelming for me right at the start or I'm not ready as a company. Anyway, that's all to say, we still have a bunch of work to do to make sure our field is equipped. There's definitely areas that do it extremely well, there's areas we need to do a better job on this. Appreciate you calling that out.

Speaker #3: And so they want to know, hey, am I working with a company that's bleeding edge on this? And so, even if I'm not necessarily ready to adopt a Wave or even a Statsig, I know that, okay, you at least help me take the first step to using some of the basics of these capabilities.

Speaker #3: And then I can add more even if it's maybe too overwhelming for me right at the start or I'm not ready as a company.

Speaker #3: So anyway, that's all to say we still have a bunch of work to do to make sure our field is equipped. There are definitely areas that do it extremely well, but then there are areas where we need to do a better job.

Speaker #3: So appreciate you calling that out.

Speaker #6: Thanks for that, Spenser. And then from my follow-up question is on the integration traction with Statsig. You all had a pretty aggressive goal obviously to move that asset into your organizations.

Scott Berg: Thanks for that, Spenser. My follow-up question is on the integration traction with Statsig. You all had a pretty aggressive goal, obviously, to move that asset into your organizations. Where are you with it? The other customers that we spoke with were super excited about that. A couple of them were already.

Speaker #6: Where are you with it? Because the other customers we spoke with were super excited about that—a couple of them were already Statsig customers, etc.

Spenser Skates: Yeah

Scott Berg: Statsig customers, et cetera. Just to understand, have you hit all your goals around that and are you at that point where now you can just deliver on product and sales versus just having to integrate the organization?

Speaker #6: So just kind of understand have you hit all your goals around that and are you kind of at that point where now you can just deliver on product and sales versus just having to integrate the organization?

Speaker #3: Yeah, yeah. So as you imagine, Statsig's been around for five years and there's a lot of work with getting it from a whole group of people who have never seen the code base or sold it or whatever else.

Spenser Skates: Yeah. As you imagine, Statsig's been around for 5 years, there's a lot of work with.

Scott Berg: Thank you

Spenser Skates: getting it from a whole group of people who have never seen the code base or sold it or whatever else. I think we've gotten through. There's always stuff, but we've gotten through all of the urgent fires in running and delivering Statsig, that's great. Customers are very excited about how it's landing. We want to make sure to give the fact that it's our main focus as opposed to at OpenAI, it was a little more of a side thing for them. It's all been received positively. That's good. Now we're starting to think about, okay, what's coming next for Statsig? If you look at statsig.com/updates, we're shipping stuff. We've been shipping stuff for the last few months. We're continuing to build the roadmap.

Speaker #3: I think we've kind of gotten through there's always stuff, but we've gotten through all of the urgent fires in running and delivering Statsig. So that's great.

Speaker #3: Customers are very excited about how it's landing. We want to make sure to give the fact that it's our main focus, as opposed to at OpenAI, where it was a little more of a side thing for them.

Speaker #3: It's all been received positively, so that's good. Now we're starting to think about, okay, what's coming next for Statsig? So if you look at statsig.com/updates, we're shipping stuff.

Speaker #3: We've been shipping stuff for the last few months. We're continuing to build in the roadmap. We're continuing to integrate it with Amplitude much more tightly so that if you're on both, which a lot of our customers are, you get the benefits of being able to use data from one and the other.

Spenser Skates: We're continuing to integrate it with Amplitude much more tightly so that if you're on both, which a lot of our customers are, you get the benefits of being able to use data from one and the other. I think a lot of the other thing we're seeing with Statsig is that there's a lot of demand from AI natives in particular. One of the reasons we're really excited to join forces with Statsig is that a lot of the way the future of product development is being run, people are choosing Statsig for that. It's engineering-first teams that tend to be much more technical. They're building out whole software development harnesses. They want to manage how stuff is deployed in that harness. Statsig is set up really well to scale. As I mentioned, OpenAI runs a version of that infrastructure internally for themselves.

Speaker #3: And I think a lot of the other thing we're seeing with Statsig is that there's a lot of demand from AI natives in particular.

Speaker #3: So one of the reasons we're really excited to join forces with Statsig is that they a lot of the way the future product development is being run.

Speaker #3: People are choosing Statsig for that. So it's engineering-first teams that tend to be much more technical. They're building out whole software development harnesses. They want to manage how stuff is deployed in that harness.

Speaker #3: And scale. As I mentioned, OpenAI runs a version of that infrastructure internally for themselves. And so they've tested that in tons of different ways over there.

Spenser Skates: They've tested that in tons of different ways over there. We're doing the same thing, except with everyone outside of OpenAI. There's a lot for us to do in terms of how do you set Statsig up to be a core part of the software development harness for all these bleeding edge AI customers. It's where everyone wants to go over time. That's what we're focused on.

Speaker #3: And we're doing the same thing, except with everyone outside of OpenAI. And so there's a lot for us to do in terms of: how do you set Statsig up to be a core part of the software development harness for all these bleeding-edge AI customers?

Speaker #3: And it's where kind of everyone wants to go over time. So that's what we're focused on.

Speaker #6: Awesome. Thanks for taking my questions.

Scott Berg: Awesome. Thanks for taking my questions.

Speaker #3: Of course, Scott.

Spenser Skates: Of course, Scott.

Speaker #1: Great. Thank you, Scott. Our next question will come from Billy Fitzsimmons from Piper Sandler. Followed by Clark Wright from DA Davidson. Go ahead, Billy.

John Streppa: Great. Thank you, Scott. Our next question will come from Billy Fitzsimmons from Piper Sandler, followed by Clark Wright from D.A. Davidson. Go ahead, Billy.

Speaker #7: Hey, guys. Good to see the results and guidance. I think one of the exciting things about Statsig is potentially the cross-sell opportunity. I know there's some things to do first, but last I looked or last I checked, I think there were 80 of the 400 Statsig customers were on Amplitude already.

Billy Fitzsimmons: Hey, guys. Good to see the results and guidance. I think one of the exciting things about Statsig is potentially the cross-sell opportunity. I know there's some things to do first. Last I looked, or last I checked, I think there were 80 of the 400 Statsig customers are on Amplitude already. There's a lot who aren't. Can you just help contextualize for us how we should think about the potential cross-sell opportunity of Amplitude into Statsig, or potentially vice versa, in how we should think about that flowing through the model long term?

Speaker #7: So there's a lot who aren't. Can you just help contextualize for us how we should think about the potential cross-sell opportunity of Amplitude into Statsig or potentially vice versa, and how we should think about that flowing through the model long term?

Speaker #3: I think probably the much bigger opportunity is to take Statsig to Amplitude customers. I think Statsig customers, as I mentioned earlier, tend to be much more bleeding edge from an AI innovation standpoint.

Spenser Skates: I think probably the much bigger opportunity is to take Statsig to Amplitude customers. I think Statsig customers, as I mentioned earlier, tend to be much more bleeding edge from an AI innovation standpoint. That's where everyone is trying to get their organizations to over the long term. Amplitude is historically focused on product management, and then Statsig is much more tailored towards engineers. It has tons of customization. It has all the statistical testing. Now, like I said, those two personas are merging, but it's early days on that. I think the opportunity is as more of our traditional Amplitude customers look and try to build like AI natives, introduce AI to their software development process, try to build out a harness, eventually try to get to self-improving products. All of those are opportunities for us to bring Statsig.

Speaker #3: And so that's where everyone is trying to get their organizations to over the long-term. It is a very it's like a more Amplitude is historically focused on product management and then Statsig is much more tailored towards engineers.

Speaker #3: It has tons of customization. The box—it has all the statistical testing. Now, like I said, those two personas are merging, but it's early days on that.

Speaker #3: So I think the opportunity is as more of a traditional Amplitude customers look and try to build AI natives, introduce AI to their software development process, try to build out a harness, eventually try to get to self-improving products, that all of those are opportunities for us to bring Statsig.

Speaker #3: Now, we definitely do see places where Statsig customers are also very interested in Amplitude. But there's a lot more both from a number and ARR basis that are Amplitude.

Spenser Skates: Now, we definitely do see places where Statsig customers are also very interested in Amplitude. There's a lot more, both from a number and ARR basis that are Amplitude.

Speaker #7: Perfect. And then if I can ask a second one, can you just contextualize maybe how either you're hiring needs have kind of changed year-to-date or where you're seeing the best ROI from AI-driven efficiencies internally within Amplitude?

Billy Fitzsimmons: Perfect. If I can ask a second one. Can you just contextualize maybe how either your hiring needs have changed year to date, or where you're seeing the best ROI from AI-driven efficiencies internally within Amplitude?

Speaker #3: Oh, there's a ton. On the hiring front, so a few different things. One, it's been I've been just very focused on transforming the entire workforce, getting leaders, getting engineers, getting people in other functions that are AI native, both by hiring that talent, acquiring it, hiring executives that have that background.

Spenser Skates: Oh, there's a ton. On the hiring front, a few different things. One, I've been just very focused on transforming the entire workforce, getting leaders, getting engineers, getting people in other functions that are AI native, both by Like hiring that talent, acquiring it, hiring executives that have that background. In addition to that, retraining and re-educating the workforce that we have here. The great part, everyone wants to learn. It's like, yeah, people see like, Hey, the more I can learn how to use AI, the more relevant my skills are going to be both at Amplitude and other places in the future. Everyone's embracing it, which is great. A few specific areas. That's an always ongoing thing. We were just adding Angela, which we announced today, in marketing.

Speaker #3: And then in addition to that, retraining the and re-educating the workforce that we have here the great part, like everyone wants to learn. It's like, yeah, people see like, hey, the more I can learn how to use AI, the more relevant my skills are going to be both at Amplitude and at other places in the future.

Speaker #3: So everyone's embracing it, which is great. The few specific areas, I think on so that's an always ongoing thing. We were just adding Angela, which we announced today.

Speaker #3: And marketing, we're always looking at companies and other places to pick up talent, new grads is another great source of very highly leveraged talent.

Spenser Skates: We're always looking at companies and other places to pick up talent. New grads is another great source of very highly leveraged talent. One of the funny things I'll tell you guys is, during downturns or whatever, a lot of companies pull back on university hiring, because it's the easiest thing to cut. If you have the confidence to evaluate who is great from that talent pool, you can get some exceptional folks right out of school, which is awesome. We've had that as a big focus here at Amplitude. That's the primary thing, and then the one specific area is Statsig. As you imagine, this is a huge, complex product and code base and architecture. We've taken our existing experimentation team, and they're now running Statsig, which is awesome, but they also need a lot more help.

Speaker #3: One of the funny things I'll tell you guys is during downturns or whatever, a lot of companies pull back on university hiring, but because it's the easiest thing to cut.

Speaker #3: But if you have the confidence to evaluate who is great from that talent pool, you can get some exceptional folks right out of school, which is awesome.

Speaker #3: So we've been had that as a big focus here at Amplitude. So that's the primary thing. And then the one specific area is Statsig.

Speaker #3: As you imagine, this is a huge complex product and code base and architecture. And so we've taken our existing experimentation team and they're now running Statsig, which is awesome, but they also need a lot more help.

Speaker #3: So we're adding lots of different roles and hiring on that. Data science leads, for deployed engineers, other engineers who are just familiar with that architecture—we've actually hired one person who used to work at Statsig, pre the OpenAI acquisition, and we're continuing to go more there.

Spenser Skates: We're adding lots of different roles and hiring on that, data science leads, four deployed engineers, other engineers who are just familiar with that architecture. We've actually hired one person who used to work at Statsig, pre the OpenAI acquisition, and we're continuing to go more there. There's a lot we need to do there. We've kind of caught the ball, which is good, but now we have to go maximize it.

Speaker #3: So there's a lot we need to do there. We've kind of caught the ball, which is good, but now we have to go maximize it.

Speaker #7: Great to see. Thanks, guys.

Billy Fitzsimmons: Great to see. Thanks, guys.

Speaker #1: All right. Thank you, Billy. Our next question will come from Clark Wright from DA Davidson, followed by Koji Akita from Bank of America. Clark, go ahead.

John Streppa: Great. Thank you, Billy. Our next question will come from Clark Wright from D.A. Davidson, followed by Koji Ikeda from Bank of America. Clark, go ahead.

Speaker #5: Thank you. It was great to see the 30% year-over-year increase in customers with over $100K in ARR. Would you like to see the highest since 2021?

Clark Wright: Thank you. It was great to see the 30% year-over-year increase in customers with over $100K in ARR, which looks to be the highest since 2021. Can you potentially break out the adds from Statsig, and what else is helping in terms of the new logo momentum that you are seeing today?

Speaker #5: Could you potentially break out the ads from Statsig? And what else is helping in terms of the new logo momentum that you're seeing today?

Speaker #1: Sure. So about 40 customers came from the Statsig business itself that we added. And so if you kind of do the quick math on that, you're still well in almost 23%, 24% growth in customers that are in that greater than 100K cohort.

Andrew Casey: Sure. About 40 customers came from the Statsig business itself that we added. If you do the quick math on that, you are still well in almost 23% to 24% growth in customers that are in that greater than $100K cohort. It is still growing quite nicely and contributing to ARR and to revenue growth. That was really good. As Spenser mentioned earlier, what we are seeing back when we are talking to customers, especially as we have gotten introduced to them for the first time, if they are brand-new customers to Amplitude that were formerly Statsig customers, is we are finding that they are, one, very appreciative of the fact that Amplitude is shepherding and taking forward the roadmap and showing confidence in our ability to actually give them a future where self-improving products is a reality.

Speaker #1: And so it's still growing quite nicely. And contributing to ARR and to revenue growth. So that was really good. And as Spencer mentioned earlier, what we're seeing back when we were talking with customers, especially as we've gotten introduced to them for the first time, if they're brand new customers to Amplitude, that were formerly Statsig customers, is we're finding that they're one, very appreciative of the fact that Amplitude is shepherding and taking forward the roadmap and showing confidence in our ability to actually give them a future where self-improving products is a reality.

Speaker #1: And they do that through adopting experimentation mindset and they're very confident then to move further with Amplitude in other areas. So that cross-sell expansion opportunity is real.

Andrew Casey: They do that through adopting an experimentation mindset, and they are very confident then to move further with Amplitude in other areas. That cross-sell expansion opportunity is real. I think we talked about it at the time, there was a multi-hundred million dollar opportunity for us just in the install base, so we are pretty excited about it.

Speaker #1: I think we talked about it at the time. There was a multi-hundred-million-dollar opportunity for us just in the install base, so we're pretty excited about it.

Speaker #5: Got it. And then last quarter, you called out event volume growth being 21% year-over-year. What is that now, as you kind of talk about the momentum that you're seeing in all-time highs? And how should we think about the ramp of that metric going forward, given agentic workflows and the amount of events that they can process?

Clark Wright: Got it. Last quarter, you called out event volume growth being 21% year-over-year. What is that now as you talk about the momentum that you are seeing in all-time highs, and how should we think about the ramp of that metric going forward given agentic workflows and the amount of events that they can process?

Speaker #1: Yeah, it's definitely growing faster than both ARR and revenue. And it's one of those areas that, for us, it feels like we've gone through many, many quarters of trying to bring it up and get the entitlements right-sized and everything else.

Andrew Casey: Yeah. It's definitely growing faster than both ARR and revenue. It's one of those areas that for us, it feels like we've gone through many quarters of trying to bring it up and get the entitlements right-sized and everything else. It's definitely a leading indicator for us that, one, we're not going to have the same types of churn issues like in the past. Two, sales has adopted that value-based orientation sale, where they're not trying to get everything up front, they're trying to get our customers to value quickly and show them the value of an expansion. Like I said, it's an indicator that we're going to see upsells have a larger meaningful contribution to growth.

Speaker #1: It's definitely a leading indicator for us that one, we're not going to have the same types of churn issues like in the past. Two, sales has adopted that value-based orientation sale where they're not trying to get everything up front.

Speaker #1: They're trying to get our customers to value quickly and show them the value of an expansion. And like I said, it's an indicator that we're going to see upsells have a larger, more meaningful contribution to growth.

Speaker #1: Whereas before, it was a tractor and the predominance of our gross with cross-sell. We're just not going to have those same instances if we've got customers who are bumping up against their entitlements.

Andrew Casey: Whereas before it was a detractor and the predominance of our growth with cross-sell, we're just not going to have those same instances if we've got customers who are bumping up against their entitlements and getting value from the investment they've made.

Speaker #1: And getting value from the investment they've made.

Speaker #5: Got it. Thank you.

Clark Wright: Got it. Thank you.

Speaker #1: Great. Thank you, Clark. Our next question will come from Koji Akita from Bank of America, followed by Nick Altman. Go ahead, Koji.

John Streppa: Great. Thank you, Clark. Our next question will come from Koji Ikeda from Bank of America, followed by Nicholas Altmann. Go ahead, Koji.

Speaker #4: Yep. Thank you. Thanks, guys. Thanks so much. I wanted to ask a question on Wave. Loved the demo. Long-term vision—it sounds like it's going to be awesome for finding problems and finding solutions, generating code, measuring outcomes.

Koji Ikeda: Yes. Thank you. Thanks, guys. Thanks so much. I wanted to ask a question on Wave. Love the demo, long-term vision. It sounds like it's going to be awesome for finding problems and finding solutions, generating code, measuring outcomes. It looks like the full deal here. The question really becomes, if Wave is successful in all the things I think it could be, then why would you need the other products from Amplitude like Statsig and Amplitude Analytics? Seems like you could do it all from Wave.

Speaker #4: I mean, it looks like the whole deal here. And so the question really becomes, if Wave is successful in all the things I think it could be, then why would you need the other products from Amplitude like Statsig and product analytics?

Speaker #4: Seems like you could do it all from Wave.

Speaker #3: Yeah, yeah, totally. Okay. So yeah, this is a brush over this architecturally. What Wave does is it takes data from lots of different data sources.

Spenser Skates: Yeah. Totally. Okay. I brushed over this architecturally. What Wave does is it takes data from lots of different data sources. It takes analytics data from Amplitude, experiment data from Statsig, where eventually we're planning to make it agnostic long term, so it can take data from any analytics thing. If you're using Google Analytics or Adobe or something else, it doesn't matter, and then translate that to insight. You still need a place to get that data. It's not like it can just look at a product and figure out what people are doing, it actually needs to have that data from some area. It's a nice build where it's like, hey, use Amplitude, use Statsig. The more data sources you put into this thing, the better the output that you see.

Speaker #3: So it takes analytics data from Amplitude, experiment data from Statsig. We're eventually planning to make it agnostic long term, so it can take data from any analytics platform.

Speaker #3: If you're using Google Analytics or Adobe or something else, doesn't matter. And then translate that insight. So you still need a place to get that data.

Speaker #3: It's not like it can just look at a product and figure out what people are doing in it. It actually needs to have that data from somewhere.

Speaker #3: And so it's a nice build where it's like, hey, use Amplitude, use Statsig. The more data sources you put into this thing, the better the output that you see.

Speaker #3: One of the big learnings from the AI boom is that the power of massive scale of data is just gets you better and more accurate and more insightful results like that is just a straight that you can the scaling laws look like you can grow that almost infinitely.

Spenser Skates: One of the big learnings from the AI boom is that the power of massive scale of data, it just gets you better and more accurate and more insightful results. The scaling laws look like you can grow that almost infinitely. Amplitude Analytics actually, as well as the experimentation and everything else we have, play a really important part in being the collection points for that data. Again, though, the goal is to be agnostic, so we can just plug into whatever system, your data warehouse, your own internal thing, other tools, third-party tools, and build it on top of that. I think another thing is that because we have that data, that gives us the ability to have much greater insight into the right things to build.

Speaker #3: So Amplitude Analytics actually as well as the experimentation and everything else we have play a really important part in being the collection points for that data.

Speaker #3: Again, though, the goal is to be agnostic so we can just plug into whatever system your data warehouse, your own internal thing, other tools, third-party tools, and kind of build it on top of that.

Speaker #3: I think another thing is that, because we have that data, it gives us the ability to have much greater insight into the right things to build.

Speaker #3: If you're a startup just starting out for the first time and you don't have the massive, multiple-petabyte data set that we have, it's like, okay, how do you even know if what you're recommending is best practice or what leads to something good?

Spenser Skates: If you're a startup starting out for the first time and you don't have the massive, multiple petabyte data set that we have, it's like, okay, how do you even know if what you're recommending is best practice or what leads to something good? There's a lot of feedback loops that we have. Because we have this data set, we know, okay, hey, here's what a great e-commerce app looks like. Here's what a great social media app looks like. Here's what a fintech app should look like. Here's the typical workflows for sign-up that work well. Here's what message customization should be. So, and so on. Because we're one of the few companies out there's no open-sourced equivalent data sets for it. Having that allows us to develop a much higher quality, better version of Wave than anyone else out there.

Speaker #3: And so, there are a lot of feedback loops that we have. Because we have this dataset, we know, okay, hey, here's what a great e-commerce app looks like.

Speaker #3: Here's what a great social media app looks like. Here's what a fintech app should look like. Here's the typical workflows for signup that work well.

Speaker #3: Here's what message customization should be, and so on. And so, because we're one of the few companies out there, there's no open-source equivalent datasets for it.

Speaker #3: And so having that allows us to develop a much higher-quality, better version of Wave than kind of anyone else out there. So the other good part is, it's not like it's an alpha.

Spenser Skates: The other good part is it's an alpha, so there are customers using it. It's not we're using it internally. There's a number of startups, there's a few enterprises that are using it. It's spitting out real things that frankly, you look at this and you're just like, "Holy shit, how did AI come up with this? This is crazy." I'm convinced that whoever wins this space, that's going to be a multi-billion dollar business, if not more. Our thing is like, let's run forward with that as fast as possible. I think we're well-positioned in the opportunity because we're the leader in analytics and a few other areas. And let's go build that business as quickly as we can.

Speaker #3: So there are customers using it. It's not that we're just using it internally. There are a number of startups, and a few enterprises, that are using it.

Speaker #3: And so it's spitting out real things that, frankly, you look at this and you're just like, holy shit, how did AI come up with this?

Speaker #3: This is crazy. I'm convinced that whoever wins this space, that's going to be a multi-billion dollar business, if not more. And so our thing is, let's run forward with that as fast as possible.

Speaker #3: I think we're well positioned in the opportunity because we're the leader in analytics. And a few other areas. Yeah. And let's go build that business as quickly as we can.

Speaker #5: Got it. Thanks, Spencer. All from me. Thank you so much.

Koji Ikeda: Got it. Thanks, Spenser. All from me. Thank you so much.

Speaker #3: Of course, Koji.

Spenser Skates: Of course, Koji.

Speaker #1: Thank you, Koji. Our next question comes from Nick Altman from BTIG, followed by YC Wong from Citi. Go ahead, Nick.

John Streppa: Thank you, Koji. Our next question comes from Nicholas Altmann from BTIG, followed by Y.C. Wong from Citi. Go ahead, Nick.

Speaker #6: Hey, awesome. Thanks, guys. Just to build off Koji's last question, I kind of wanted to ask the inverse on Wave, of it seems like there's more incentive to adopt the broader platform with Wave.

Nicholas Altmann: Hey, awesome. Thanks, guys. Just to build off Koji's last question, I wanted to ask the inverse on Wave of, like, it seems like there's more incentive to adopt the broader platform with Wave.

Spenser Skates: Yeah. Exactly.

Speaker #6: And I know it's still very early, but how are those kinds of conversations going with customers? Are you having more sort of multi-product or platform adoption customers as they kind of look at Wave and this vision of the self-improving product?

Nicholas Altmann: I know it's still very early, but how are those kind of conversations going with customers? Are you having more sort of multi-product or platform adoption customers as they look at Wave and this vision of the self-improving product? The follow-up there is just how should we think about Wave being monetized more so in the near term? Is it kind of indirectly in the sense of it gives customers more incentive to adopt the broader platform, and that's how you sort of plan to monetize it, or as kind of a standalone SKU?

Speaker #6: And then the follow-up there is just, how should we think about Wave being monetized more so in the near term? Is it kind of indirectly, in the sense of it gives customers more incentive to adopt the broader platform and that's how you sort of plan to monetize it, or is it kind of a standalone SKU?

Speaker #3: Yeah, so you're exactly right, which is that the more data sources you feed to this thing, the better. And so I've already seen multiple customers who have gotten on session replay, as well as one who signed up for AI feedback specifically because, hey, the stuff they send to Wave makes it a lot better.

Spenser Skates: Yeah. You're exactly right, which is the more data sources you feed to this thing, the better. I've already seen multiple customers who have gotten on Session Replay, as well as one that signed up for AI Feedback specifically because, hey, the stuff fed to Wave makes it a lot better. You're absolutely right, where it drives the whole platform play or it's like, okay, you have all these individual point things, and then they're more data sources. Session Replay, in particular, is very, very powerful. As you imagine, viewing the exact state of UI and where a user clicked has a lot of value for how it can be better. That's been awesome to see. Again, early, there's a handful of customers on it, but as we grow it out, I think that'll drive more adoption.

Speaker #3: And so you're absolutely right, where it drives the whole platform play, or it's like, okay, you have all these individual point things, and then they're just more data sources.

Speaker #3: Session replay in particular is very, very powerful. As you imagine, viewing the exact state of the UI and where a user clicked has a lot of value for how it can be better.

Speaker #3: So that's been awesome to see. And again, it's early—there's a handful of customers on it, but as we grow it out, I think that'll drive more adoption.

Speaker #3: And I also don't think, to my point earlier to Koji, it's like the goal is to be agnostic with it. We want to build the most bleeding-edge thing.

Spenser Skates: I also don't think, to my point earlier to Koji, it's the goal is to be agnostic with it. We want to build the most bleeding-edge thing. If we plug in the other sources too, all the better. On the monetization front, we'll charge for it. We absolutely will charge for it. I mean, you think about the value that this creates. Now you go from analytics or data tooling, where it's you have to manually go in, collect an event, or ask a particular question, get a result out, think about how to apply that to the business. Now you're having a whole flow that does it for you. Hey, I've already seen this user is having friction here. The docs example I made is, hey, we see most search queries are failing. Why is that?

Speaker #3: And so if we plug in the other sources too, all the better. On the monetization front, we'll charge for it. We absolutely will charge for it.

Speaker #3: I mean, you think about the value that this creates. Now, you go from analytics or data tooling where you have to manually go in, collect an event, or look at—ask a particular question, get a result out, think about how to apply that to business.

Speaker #3: And now you’re having a whole flow that does it for you. Hey, I’ve already seen this user is having friction here. The docs example I made is like, hey, we see most search queries are failing.

Speaker #3: Why is that? Well, they're single characters, and we're not waiting until someone types a complete word. So they get this error when they're in the middle of their typing, and it feels bad.

Spenser Skates: Well, they're single characters, and we're not waiting till someone types a complete word, so they get this error when they're in the middle of the typing and it feels bad, and it's, duh. Okay, yeah, you should resolve that and make that better. It's not just that, it's that times hundreds of things all across all surface areas of your product. One of the lessons is that behavioral data and product surface areas are so large, it is impossible for any team to stay on top of them. The fact that this thing is looking all the time for how it can be better, it's magical. It's crazy what it can do. I think whatever company goes to win that is going to be multiple billions in revenue, if not more, and we want to aggressively go after.

Speaker #3: And it's like, duh, okay, yeah, you should resolve that and make that better. And it's not just that—it's like that times hundreds of things all across all surface areas of your product.

Speaker #3: One of the lessons is that behavioral data and product surface areas are so large, it is impossible for any team to stay on top of them.

Speaker #3: And so the fact that this thing is looking all the time for how it can be better is just—it's magical. It's crazy what it can do.

Speaker #3: So I think whatever company goes to win that is going to be multiple billions in revenue, if not more. And we want to aggressively go after it.

Speaker #3: And yes, customers are willing to pay for that. Now, again, early days— we're in alpha— so we haven't figured out exactly how we're going to monetize it, but we absolutely will charge for that capability.

Spenser Skates: Yes, customers are willing to pay for that. Now, again, early days, we're in alpha so we haven't figured out exactly how we're going to monetize it, but we absolutely will charge for that capability. People are talking about, hey, there's all this money going into AI, where does it actually come out? This is one where you can draw the line really directly. It's, look, the customer experience is getting better. They're spending more, there's more revenue, there's less friction, there's less downtime. The whole thing's just better. Great use from an application standpoint.

Speaker #3: That's one of the great people are talking about, hey, there's all this money going to AI. Where is it actually come out? And this is one where you can draw the line really directly.

Speaker #3: It's like, look, the customer experience is getting better. They're spending more. There's more revenue. There's less friction. There's less downtime. The whole thing is just better.

Speaker #3: Great use from an application standpoint.

Speaker #6: Great. Thank you so much.

Nicholas Altmann: Great. Thank you so much.

Speaker #3: For sure.

Spenser Skates: For sure.

Speaker #1: Thank you, Nick. Our next question will come from YC Wong from Citi, followed by Arjun Bhatia from William Blair. Go ahead, YC, your line's open.

John Streppa: Thank you, Nick. Our next question will come from Y.C. Wong from Citi, followed by Arjun Bhatia from William Blair. Go ahead, Y.C., your line's open.

John Streppa: Thank you, Nick. Our next question will come from YC Wong from Citi, followed by Arjun Bhatia from William Blair. Go ahead, Y.C., your line's open.

Speaker #7: Hey, good evening. Thanks for taking a question here. Spenser and team, great to see the fast-expanding AI platform you have. Every quarter, I want to touch on agent analytics, which now seem to measure it all.

Y.C. Wong: Hey, good evening. Thanks for taking the question here. Spenser and team, great to see the fast-expanding AI platform here you have every quarter. I want to touch on Agent Analytics, which now seem to be measuring.

YC Wong: Hey, good evening. Thanks for taking the question here. Spenser and team, great to see the fast-expanding AI platform here you have every quarter. I want to touch on Agent Analytics, which now seem to be measuring.

Spenser Skates: Oh, I love it.

Speaker #3: I love it.

Y.C. Wong: -like agent themselves, right? I mean, where the market that we see is already multiple vendors out there trying to measure prompts, measure latency, hallucination too, all the stuff that you can see, but what is the customer problems that the Agent Analytics could solve that the current observability platform cannot? How do you view the market opportunity of that problem?

YC Wong: -like agent themselves, right? I mean, where the market that we see is already multiple vendors out there trying to measure prompts, measure latency, hallucination too, all the stuff that you can see, but what is the customer problems that the Agent Analytics could solve that the current observability platform cannot? How do you view the market opportunity of that problem?

Speaker #7: Agents themselves, right? I mean, the market that we see already has multiple vendors out there trying to measure prompts, measure latency, hallucination—all the stuff that you can see.

Speaker #7: But what are the customer problems that agent analytics could solve that the current observability platform cannot? And then, how do you view the market opportunity of that problem?

Speaker #3: Yeah, so I mean, I think first, to the extent this replaces most traditional interfaces, then the market opportunity is as large, if not larger.

Spenser Skates: Yeah. First, to the extent this replaces most traditional interfaces, then the market opportunity is as large, if not larger, than what's going on traditional user interfaces with Session Replay and analytics. In terms of our unique positioning, what we offer, which I shared a little bit in the customer story about The Economist, is that you can connect what's individually happening within a session to the long-term impact of your business. So you can say, okay, hey, you got a successful answer back from the bot. Did that lead to you spending more or signing up or keeping your subscription? Conversely, if you ran into a problem and you got frustrated, did that lead to some negative long-term outcome? That loop is really, really important. A lot of the engineering-specific observability products we see in this space are just standalone.

Speaker #3: Then what's going on in traditional user interfaces with session replay and analytics? In terms of our unique positioning, what we offer—which I shared a little bit in the customer story about The Economist—is that you can connect what's individually happening within a session to the long-term impact on your business.

Speaker #3: So you can say, okay, hey, you got a successful answer back from the bot. Did that lead to you spending more, or signing up, or keeping your subscription?

Speaker #3: Conversely, if you ran into a problem and you got frustrated, did that lead to some negative long-term outcome? And that loop is really, really important.

Speaker #3: Most. A lot of the engineering-specific observability products we've seen in this space are just kind of standalone. It's like, okay, they'll just sew the traces and that's kind of it.

Spenser Skates: It's like, okay, they'll just show the traces, and that's kind of it, and you have no idea if it's actually leading to different results down the line. That's why we see both traditional enterprises that are transforming their businesses, like The Economist, as well as a lot of AI natives. I mentioned one of the largest foundational model companies. They also are looking at, as you imagine, they'll have a lot of tooling there, but they want to know, okay, is this leading to someone to becoming a subscriber, to upselling, all of that sort of stuff long term. Being able to connect that journey end to end is what we uniquely offer.

Speaker #3: And you have no idea if it's actually leading to different results down the line. And so that's why we see both traditional enterprises that are transforming their businesses, like the economist, as well as a lot of AI natives.

Speaker #3: I mentioned one of the largest foundational model companies. They also are looking at, as you can imagine, they'll have a lot of tooling there, but they want to know, okay, is this leading to someone becoming a subscriber, to upselling, all of that sort of stuff long-term?

Speaker #3: And so, being able to connect that journey end-to-end is what we uniquely offer.

Speaker #7: That sounds like a more TAM expansion opportunity there.

Y.C. Wong: That sounds like a more TAM expansion opportunity there.

YC Wong: That sounds like a more TAM expansion opportunity there.

Speaker #3: Oh, absolutely. Absolutely. Yeah, I didn't cover as much today. We demoed it more on the Q1 earnings call, but yeah, it's actually one of the things my chief commercial officer and I are very excited about.

Spenser Skates: Oh, absolutely.

Y.C. Wong: Yeah.

YC Wong: Yeah.

Spenser Skates: I didn't cover it as much today. We demoed it more on the Q1 earnings call. Yeah, it's actually one of the things my Chief Commercial Officer and I are very excited about.

Speaker #7: Yeah, definitely look forward to hearing more, including Wave. I have a quick follow-up for Andrew as well on the guidance. Amplitude Grow has definitely been accelerating for the past year and more, right?

Y.C. Wong: Yeah, definitely look forward to hearing more, including Wave. I have a quick follow-up for Andrew as well on the guidance. Amplitude growth has definitely been accelerating for the past year and more, right? Even adjusting for the Statsig business this quarter, I think it's still accelerated. The implied guide that I'm looking for Q4 shows about 2 to 3-point decel. Could we have us double-click on the largest step down on the Q4 guide? Is it more just seasonality or incremental conservatism?

YC Wong: Yeah, definitely look forward to hearing more, including Wave. I have a quick follow-up for Andrew as well on the guidance. Amplitude growth has definitely been accelerating for the past year and more, right? Even adjusting for the Statsig business this quarter, I think it's still accelerated. The implied guide that I'm looking for Q4 shows about 2 to 3-point decel. Could we have us double-click on the largest step down on the Q4 guide? Is it more just seasonality or incremental conservatism?

Speaker #7: Even adjusting for the static business this quarter, I think it's still accelerated. But the implied guide that I'm looking at for Q4 shows about a two- to three-point decel.

Speaker #7: Could we kind of have us double-click on the largest step-down in the Q4 guide? Is it more just seasonality or incremental conservatism?

Speaker #2: What I would tell you is that we always take a look at what we're building our guidance on, what we believe are very strong currents to occur.

Andrew Casey: What I would tell you is that we always take a look at, when we're building our guidance, what we believe is very strong currents to occur. I mentioned some of the factors earlier about pipeline, how well that pipeline's developed, where we're seeing good demand from our customers. Usually, Q4 is our strongest quarter from an ARR perspective, and it's because that's the way we've built our comp plans. That's the way enterprise selling cycles run, typically in a calendar-based company. I would just tell you that our guidance is based upon what we know is out there as far as our pipelines, our RPO, and it's what we're comfortable with.

Speaker #2: And I mentioned some of the factors earlier about pipeline—how well that pipeline is developed, where we're seeing good demand from our customers. Usually, Q4 is our strongest quarter from a new ARR perspective, and it's because that's the way we've built our comp plans.

Speaker #2: That's the way enterprise selling cycles run, typically, in a calendar-based company. So, I would just tell you that our guidance is based upon what we know is out there as far as our pipelines and our RPO, and it's what we're comfortable with.

Speaker #7: Got it. Congrats, guys.

Y.C. Wong: Got it. Congrats, guys.

YC Wong: Got it. Congrats, guys.

Speaker #2: Thank you.

Andrew Casey: Thank you.

Spenser Skates: Great.

Speaker #1: Thank you, YC. And our last question will come from the line of Arjun Bhatia of William Blair, by Willow Miller. Willow, your line is open.

John Streppa: Thank you, Y.C. Our last question will come from the line of Arjun Bhatia of William Blair by Willow Miller. Willow, your line is open.

John Streppa: Thank you, YC Our last question will come from the line of Arjun Bhatia of William Blair by Willow Miller. Willow, your line is open.

Willow Miller: Hey, team. Thanks for taking our question. Can we hear your updated thoughts on the 20% plus revenue growth target, given the strong growth this quarter and the strong Q3 guide? I'm curious to hear how you're thinking about it now, considering Statsig and now Wave.

Speaker #8: Hey, team. Thanks for taking our question. Can we hear your updated thoughts on the 20%+ revenue growth target, given the strong growth this quarter and the strong third-quarter guide?

Speaker #8: I'm curious to hear how you're thinking about it now, considering Static and now Wave.

Speaker #3: Oh, yeah. I mean, I think Static is an accelerant to our long-term plans, which is part of why Jay and I agreed Amplitude would be the best home for Static long-term.

Spenser Skates: Oh, yeah. I think Statsig is an accelerant to our long-term plans, which is part of why we, Vijaye and I, agreed Amplitude would be the best home for Statsig long term. We put up $19 million in organic growth last quarter in Q2. We're just touching on that 20%. The annual number is $410. If you divide that out, it's like we're just shy of that 20% growth target when you annualize the quarterly numbers. To me, as I've always said, 20% is kind of bare minimum. We want to be making sure to continually hitting and exceeding that 20%. Long term, we're aiming a good deal higher. We want to get to 30% and beyond that as we continue to grow the business. Obviously, a lot of work between here and there. That's what we're very focused on doing.

Speaker #3: As I think, so we put up $19 million in organic growth last quarter in Q2. And so we're just touching on that 20%.

Speaker #3: It's like the annual numbers—4 out of 10. So if you divide that out, we're just shy of that 20% growth target when you annualize the quarterly numbers.

Speaker #3: To me, as I've always said, 20% is kind of the bare minimum. We want to make sure we're continually hitting and exceeding that 20%.

Speaker #3: Long-term, we're aiming a good deal higher. We want to get to 30 and then beyond that as we continue to grow the business. Obviously, a lot of work between here and there, but that's what we're very focused on doing.

Speaker #8: Great to hear. Thank you.

Willow Miller: Great to hear. Thank you.

John Streppa: Great. Thank you, Willow. That will conclude our Q2 earnings call. Thank you for your time and interest. We look forward to seeing you this quarter on the road as we attend conferences hosted by KeyBank, Citi, and Piper Sandler. Thank you.

Speaker #1: All right. Thank you, Willow. That will conclude our second quarter earnings call. Thank you for your time and interest. We look forward to seeing you this quarter on the road as we attend conferences hosted by KeyBanc, Citi, and Piper Sandler.

Speaker #1: Thank you.

Speaker #3: Thank you all.

Spenser Skates: Thank you all.

Speaker #2: Thank you.

Andrew Casey: Thank you.

John Streppa: Goodbye

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