Prediction: This Under-the-Radar Artificial Intelligence (AI) Stock Could Be a Multibagger by the End of 2027

DigitalOcean’s inference-services ARR surged 254% year over year in Q4 2025, while AI-customer ARR rose 150% to $120 million and net dollar retention improved to 101%. Management expects revenue growth to accelerate from 21% in 2026 to 30% in 2027, with revenue potentially reaching $1.42 billion next year and $1.5 billion in 2027. The article argues the stock’s 11x sales multiple could expand further if AI-driven growth continues.

Analysis

DOCN is benefiting from a second-order AI migration that hyperscalers are not optimized to capture: cost-sensitive inference and small-team model deployment. If AI spending continues shifting from training to inference, the economics favor a cheaper, simpler platform like DOCN because inference is far more volume-driven and less brand-sensitive than enterprise cloud procurement. That creates a niche where unit economics can expand faster than headline market-share suggests, especially as customers scale from experimentation into production.

The more important read-through is that DOCN’s growth is likely pulling demand away from lower-margin, GPU-constrained resale channels and from hyperscale instances that are overprovisioned for smaller workloads. That can pressure smaller cloud aggregators and software platforms that depend on AWS/GCP pass-through economics, while incrementally supporting NVDA demand because cheaper inference still consumes high-end GPUs. In other words, DOCN is not a direct threat to the hyperscalers’ core franchises, but it can siphon the long tail of AI workloads where pricing discipline matters most.

The market is probably underappreciating execution risk in the ramp, not the strategic thesis. The key vulnerability is that the current valuation already discounts several years of sustained inference adoption, so any slowdown in NDR, customer concentration, or GPU supply availability would compress multiple expansion quickly over the next 2-3 quarters. The bigger contrarian question is whether the recent stock move has already priced in the 2026-2027 growth inflection, leaving less upside unless the company proves durable operating leverage rather than just top-line acceleration.

If the growth story holds, the cleanest expression is to own DOCN into the next 2-4 earnings prints, but size it as a momentum-plus-fundamentals trade rather than a long-duration compounder at current levels. The risk/reward improves on any post-earnings pullback if management confirms inference bookings are translating into sticky multi-quarter usage, because that would extend the market’s visibility into 2027. Near term, the key catalyst is not revenue alone, but evidence that AI mix is improving gross margin and retention simultaneously.

Key Decisions for Investors

• Long DOCN into the next 1-2 earnings events; use a pullback of 8-12% as entry if management reiterates 2026 acceleration. Target a 20-30% upside over 3-6 months if inference ARR keeps compounding and NDR stays above 100%; cut if NDR falls back below 100% for two consecutive quarters.
• Pair trade: long DOCN / short a hyperscaler basket tilt via GOOGL-MSFT-AMZN equal weight for 3-6 months, expressing the view that incremental AI inference economics accrue more to cost-optimized platforms than to already-mature cloud franchises. Risk: this works only if AI demand broadens faster than cloud giants monetize it.
• Overweight NVDA on any DOCN-related AI demand confirmation, but prefer call spreads rather than outright stock for a 3-6 month horizon. DOCN’s growth implies sustained GPU utilization, yet valuation and positioning are already crowded, so upside is more convex than directional.
• Avoid chasing DOCN after large up-days; wait for post-earnings volatility. The stock likely needs proof of operating leverage before a premium re-rating is durable, so the best entry is on guidance beats with slower-than-expected multiple expansion.
• For more aggressive traders, consider DOCN Jan-2027 call spreads financed by short-dated puts only if liquidity allows. This captures the multiyear AI-inference thesis while limiting downside if the market decides the current rally is already fully priced.