Key Takeaways
- Snowflake forecast FY product revenue of ~$4.4B (vs. Bloomberg consensus ~$4.34B), helping soothe investor concerns about software demand.
- Product revenue for the quarter rose ~32% to $1.09B (ended July 31); remaining performance obligation (RPO) was $6.9B (vs. $6.78B est.).
- New customers grew ~21%; Snowflake added ~500 employees and is investing in products that make generative-AI workflows easier on customer data.
- Shares jumped ~13% in after-hours trading on the outlook; the firm remains a beneficiary of cloud/AI tailwinds but faces competition from Databricks and other data‑platform vendors.
- Key risks: macro weakness that crimps enterprise spend, competitive pricing/feature pressure, and the need to convert AI interest into durable, higher‑margin product revenue.
What Happened?
Snowflake beat near‑term expectations and issued a stronger‑than‑feared outlook: management now sees FY product revenue around $4.4B. Quarterly product sales climbed about 32% to $1.09B and RPO beat estimates at $6.9B. The company highlighted that newer products—designed to simplify generative‑AI on customer data—contributed meaningfully. Snowflake also accelerated hiring (adding ~500 staff) to scale sales and go‑to‑market.
Why It Matters
Snowflake is positioned as a neutral data layer for AI, which makes it a gatekeeper for many enterprises trying to deploy generative models on proprietary data. The upbeat guidance reduces fears that incumbents will be displaced quickly by niche AI vendors and supports the narrative that large cloud/data incumbents can monetize the AI transition. For investors, this means Snowflake could sustain premium growth multiple if it (a) grows ARR/RPO predictably, (b) converts AI product interest into higher‑value, repeatable bookings, and (c) manages investor concerns over cost‑to‑serve as AI workloads become more compute‑intensive.
What’s Next?
Watch subsequent quarters for: (1) cadence of product‑revenue growth and margin impact from AI‑related workloads; (2) RPO-to-revenue conversion and churn trends; (3) net-new customer quality and expansion within large accounts; (4) competitive moves from Databricks, Microsoft, and specialist AI vendors on pricing/packaging; and (5) how the company manages hiring versus sales productivity. Earnings commentary on product adoption for generative‑AI use cases will be especially market‑sensitive.
