Snowflake NYSE: SNOW reported accelerating second-quarter fiscal 2027 growth as demand for its core data platform and AI products increased, while the company also expanded its non-GAAP operating margin and raised its full-year product revenue outlook.
Product revenue totaled $1.49 billion, up 37% from a year earlier. Chief Executive Officer Sridhar Ramaswamy said the result marked Snowflake’s second consecutive quarter of record sequential dollar growth and represented a seven-percentage-point acceleration from the 30% year-over-year growth rate reported at the end of fiscal 2026.
“AI is compounding Snowflake’s advantage” by bringing new workloads to the platform, increasing adoption of first-party AI products and lifting broader platform consumption, Ramaswamy said. The company described its strategy as providing a governed data foundation, access to AI models, application workflows and an “agentic control plane” for enterprise customers.
Customer Growth and AI Product Adoption
Snowflake ended the quarter with 14,554 customers worldwide after adding 692 net new customers, a 32% year-over-year increase in net additions. The company added 14 Forbes Global 2000 customers during the period, bringing its total in that category to 829. Snowflake said its AI Data Cloud now supports more than 41% of the Forbes Global 2000.
The company’s largest customers continued to expand their use of the platform. Snowflake said 65 customers generated more than $10 million in trailing 12-month product revenue, while 828 customers spent more than $1 million over the same period, following 48 net new additions above that threshold in the quarter.
Net revenue retention was 126%, CFO Brian Robins said, supported by customer expansions involving migrations and AI-related use cases. Remaining performance obligations rose 30% year over year to $9 billion. About 54% of that amount is expected to be recognized as revenue over the next 12 months, representing approximately 42% growth compared with the company’s estimate in the year-earlier quarter.
Snowflake said 43% of customers now share data with at least one “Stable Edge,” reflecting the company’s data-sharing capabilities across organizations. Ramaswamy cited examples including a large Australian bank that migrated its financial-crime platform to Snowflake, processing 17 billion transactions and achieving 10 times faster query performance, according to the company.
Snowflake highlighted growing adoption of its CoWork and CoCo AI products. CoWork expanded to 5,800 accounts, up nearly 11% sequentially, while CoCo surpassed 9,100 accounts after adding more than 2,000 net new accounts during the quarter.
Ramaswamy said Snowflake’s AI portfolio accounted for approximately half of the company’s revenue-growth acceleration. Beyond CoCo and CoWork, he cited AI Functions, document processing, machine learning, notebooks, applications and faster migrations as contributors to growth.
AI Consumption and Model Strategy
Management said customers adopting AI products are also increasing consumption of Snowflake’s core platform. While the company did not disclose a specific consumption uplift for AI adopters relative to non-adopters, Ramaswamy said Snowflake measures customer cohorts and has observed a noticeable effect as adoption broadens within accounts.
He also said AI is reducing the time needed to bring new projects into production. Snowflake tracks the time it takes new customers to reach 80% of purchased consumption, and Ramaswamy said that metric has “very visibly improved” for newer customer cohorts.
The company is emphasizing model choice as enterprises seek flexibility among frontier, open-weight and Snowflake-developed models. Ramaswamy said customers are increasingly interested in switching between models and optimizing costs. Snowflake’s Cortex AI Gateway can route tasks to models based on customer-defined policies and performance data, according to the company.
Executive Vice President of Product Christian Kleinerman said customers are also showing early interest in post-training open models. Snowflake does not plan to train a frontier model, he said, but will continue developing Arctic-family models for narrower tasks where it can provide greater accuracy and efficiency.
Robins said the higher mix of fast-growing AI workloads affected gross-margin expectations because those workloads currently carry lower contribution margins. Still, he said the company remains focused on expanding overall operating margin over time.
Margins, Headcount and Outlook
Snowflake’s non-GAAP operating margin expanded by more than 400 basis points year over year to 15% in the second quarter. Robins attributed the improvement to stronger revenue growth and disciplined headcount management.
Year to date, Snowflake added 334 employees, including 173 employees from its Observe acquisition, compared with 935 employees added during the same period a year earlier. The company ended the quarter with $4.3 billion in cash equivalents and short- and long-term investments.
For fiscal 2027, Snowflake raised its product revenue forecast to $6.07 billion, representing 36% year-over-year growth. The outlook includes approximately one percentage point of growth from Observe, consistent with the company’s prior guidance.
- Third-quarter product revenue is expected to be between $1.588 billion and $1.593 billion, representing 37% to 38% year-over-year growth.
- Full-year non-GAAP product gross margin is expected to be 74%.
- Full-year non-GAAP operating margin guidance was raised to 14.5% from 13.5%.
- Third-quarter non-GAAP operating margin is projected at 15.5%.
- Snowflake reiterated its full-year non-GAAP adjusted free-cash-flow margin outlook of 23%.
Ramaswamy said the company remains on track to achieve GAAP profitability in the fourth quarter of fiscal 2028. He said Snowflake is entering the second half of fiscal 2027 with product momentum, improving sales productivity and continued operational discipline.
About Snowflake (NYSE:SNOW)
Snowflake Inc is a cloud-native data platform company that provides a suite of services for storing, processing and analyzing large volumes of data. Its core offering, often described as the Snowflake Data Cloud, combines data warehousing, data lake and data sharing capabilities in a single managed service delivered across major public cloud providers. The platform is designed to support analytics, data engineering, data science and application workloads with a focus on scalability, concurrency and simplified administration.
Key products and capabilities include a multi-cluster, shared-data architecture that separates compute from storage; continuous data ingestion and streaming; support for structured and semi-structured data formats; tools for data governance, security and compliance; and developer frameworks for building data applications.
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