Meet the AI Software Stock That’s Crushing Palantir (Hint: It’s Significantly Cheaper)

Snowflake delivered fiscal Q2 2027 revenue growth of 35% year over year to $1.55 billion and EPS growth of 77% to $0.62, beating the $0.45 consensus estimate. The company raised full-year product-revenue guidance to $6.07 billion from $5.84 billion, implying 36% growth and exceeding the $5.86 billion consensus, while shares rose more than 16% after results. AI-product adoption supported 692 net new customers, 126% net revenue retention, and a 30% increase in remaining performance obligations to $9 billion, underpinning continued growth expectations.

Analysis

SNOW’s upside now hinges less on new-logo growth than on whether AI workloads translate into durable consumption revenue rather than low-cost feature adoption. The key operating read-through is expansion within the installed base: if AI agents increase query, storage, and governance usage, Snowflake’s usage-based model can produce positive operating leverage and support further estimates revisions over the next 1-3 quarters. The risk is that AI features are bundled to defend the data platform, raising compute costs faster than monetization and limiting incremental gross-margin conversion.

The relevant competitive set is broader than PLTR. Databricks, hyperscalers (MSFT/Azure, AMZN/AWS, GOOGL/GCP), and open-source data stacks can subsidize AI/data workloads to protect cloud consumption, making Snowflake’s premium valuation contingent on maintaining workload portability and multi-cloud differentiation. PLTR’s much higher sales multiple does not make SNOW intrinsically cheap; it only makes SNOW cheaper relative to the most aggressive AI-software benchmark. A de-rating in AI application software would likely affect both, though SNOW has greater sensitivity to enterprise data-spending budgets and cloud optimization.

Near term, the post-results move likely leaves little tolerance for a deceleration in consumption, net revenue retention, or backlog conversion at the next earnings print. Over 6-18 months, the structural bull case requires AI-driven workloads to expand the addressable spend per customer faster than hyperscaler-native alternatives capture it. Contrarian view: enterprise buyers may consolidate AI tooling around their cloud provider, making broad agent usage an adoption metric rather than evidence of incremental software monetization.

Key Decisions for Investors

• Maintain a tactical long SNOW only on pullbacks or after confirmation of sustained consumption acceleration; target a 3-6 month hold, with upside driven by another revenue/operating-margin guide raise. Falsify if net revenue retention falls below 120% or management attributes growth primarily to contract timing rather than workload consumption.
• Use a relative-value pair: long SNOW / short PLTR over 3-6 months, sized beta-neutral. SNOW offers less valuation duration and more visible data-platform monetization, while PLTR is more exposed to multiple compression if commercial growth or deal conversion moderates; exit if PLTR’s commercial growth materially reaccelerates while SNOW’s consumption trends soften.
• Do not chase outright SNOW after a large gap without checking implied volatility and forward revenue estimates. If options pricing implies a repeat earnings-scale move, prefer defined-risk call spreads rather than naked calls into the next report; the missing diligence item is current implied volatility versus SNOW’s historical post-earnings move.
• Monitor MSFT, AMZN, and GOOGL cloud commentary for AI workload migration and pricing. Evidence that enterprises are standardizing AI/data workflows on a single hyperscaler is a 6-18 month negative for SNOW’s independent-platform multiple, even if near-term reported growth remains strong.