Analyst(s): Futurum Research
Publication Date: June 1, 2026

Snowflake’s Q1 FY 2027 earnings centered on improving consumption trends and faster adoption of first-party AI products. The company also expanded its AWS relationship and raised full-year product revenue and profitability targets.

What is Covered in This Article:

• Snowflake’s Q1 FY 2027 financial results
• AI products driving consumption upside
• Agentic control plane product direction
• Partner momentum and enterprise expansion
• Guidance and Final Thoughts

The News: Snowflake (NYSE: SNOW) reported Q1 FY 2027 financial results. Revenue was $1.39 billion, up 33% year-on-year (YoY), versus the Wall Street consensus of $1.33 billion. Product revenue was $1.33 billion, up 34% YoY, and Professional services and other revenue grew by 25% YoY to $56.6 million. Non-GAAP operating income was $165.80 million (Q1 FY 2026: $91.7 million) with an 11.90% non-GAAP operating margin (Q1 FY 2026: 9%). Non-GAAP net profit was $148 million (Q1 FY 2026: $87.6 million), or a non-GAAP diluted earnings per share of $0.39 (Q1 FY 2026: $0.24).

“Snowflake delivered a milestone quarter, with product revenue of $1.33 billion, up 34% year-over-year, marking the strongest sequential dollar growth in our history,” said Sridhar Ramaswamy, CEO of Snowflake. “AI continues to be a powerful tailwind for Snowflake, and Q1 marks a clear inflection point in that journey.”

Snowflake Q1 FY 2027: AI Products Drive Faster Consumption Growth

Analyst Take: Snowflake’s Q1 FY 2027 results reinforce that AI usage is now contributing to both incremental revenue and higher core platform consumption. The quarter also showed improving customer expansion signals, including net retention moving higher and growth in large-spend cohorts. The company’s narrative is shifting from “AI features on a data platform” to a broader claim around being a governed control layer for agentic workflows. The most important question now is durability: whether AI-driven consumption stays incremental or becomes a budget substitution inside customer environments.

AI Products Move From Feature To Revenue Line

Cortex Code and Snowflake Intelligence became core products rather than add-ons, with Cortex Code in use across more than 7,100 accounts in Q1 FY 2027. Snowflake also cited that accounts using Snowflake Intelligence more than doubled quarter-over-quarter, pointing to faster uptake among business users. Internal execution signals matter here because the company tied AI tooling to productivity gains, including more than 25% faster case resolution times and a 25% increase in support throughput per engineer. The strategy includes cost controls for AI usage, with plans to manage token and account-level limits as deployments scale to large user bases. Gross margin pressure from AI products remains a watch item, but the company framed cost efficiency work and AWS economics as offsets. The near-term implication is that AI adoption can lift usage while also tightening operational efficiency, which supports both growth and margin targets.

Agentic Control Plane Strategy Becomes Product Architecture

Snowflake positioned Snowflake Intelligence as the business user surface and Cortex Code as the builder interface for a governed agentic layer. This framing ties directly to customer workflow expansion: questions become actions, and natural language creation becomes deployed pipelines, agents, and applications. The company emphasized using metadata and platform activity to improve AI context quality, which aligns with a moat argument that goes beyond model access. Cortex Code expanded to support other data platforms, including tools like dbt Cloud and Airflow, which suggests Snowflake wants to sit in the workflow even when data work spans multiple systems. The approach also suggests a path toward reusable “memory” and repeatable harnesses that improve outcomes over time as more users build inside the platform. The key risk is execution complexity, because control-plane claims require consistent governance, auditability, and reliability across many tools and user types. The strategic impact is that Snowflake is aiming to own the governed runtime for agentic work, not only the storage and compute layer.

Partnerships And Enterprise Expansion Support Scale

Snowflake expanded its collaboration with AWS through a new $6 billion multi-year agreement, including the use of Graviton processors and added go-to-market commitments. The company also cited surpassing $7 billion in lifetime AWS Marketplace sales, which supports the view that channel motion and consumption can compound. Beyond AWS, Snowflake referenced an expanded $200 million partnership with OpenAI and general availability for joint capabilities tied to SAP, keeping model and application ecosystem access central to the story. Customer metrics also signal breadth: 13,912 total customers, 813 Forbes Global 2000 customers, and 779 customers with trailing 12-month product revenue above $1 million. Large customer expansion continued, with 64 customers spending more than $10 million on a trailing 12-month basis. Remaining performance obligations grew 38% YoY to $9.21 billion, though the company noted bookings skew to the fourth quarter due to renewal timing. The operating implication is that partner routes and enterprise expansion can reinforce adoption while providing more consistent entry points for AI-driven workloads.

Guidance and Final Thoughts

For Q2 FY 2027, Snowflake guided product revenue of $1.415 billion to $1.420 billion, representing 30% YoY growth, with a 12.50% non-GAAP operating margin. For FY 2027, Snowflake guided product revenue of $5.84 billion, representing 31% YoY growth, and raised its non-GAAP operating margin guide to 13.50% from 12.50%. The company kept its non-GAAP product gross margin guide at 75% and reiterated a 23% non-GAAP adjusted free cash flow margin. The company also signed a definitive agreement to acquire Natoma, described as an enterprise Model Context Protocol platform intended to extend governed agent actions across everyday tools.

Snowflake’s quarter suggests the company is beginning to convert AI enthusiasm into measurable platform consumption rather than relying primarily on future positioning narratives. The broader push into agentic workflows and governed orchestration also indicates Snowflake wants to move higher in the enterprise AI stack, where workflow ownership and metadata context can become more strategically valuable than storage alone. At the same time, maintaining profitability while AI workloads scale will remain an important test, particularly if customers start scrutinizing whether incremental AI usage expands overall spending or simply reallocates existing cloud and analytics budgets.

See the full press release on Snowflake’s Q1 FY 2027 financial results on the company website.

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