Snowflake Summit 2025, held June 2–5 in San Francisco, drew more than 20,000 data and AI professionals, making it the largest event in the company’s history. Keynotes from Snowflake CEO Sridhar Ramaswamy and OpenAI CEO Sam Altman explored the evolving role of AI in business, highlighting its shift from experimental to operational. The summit included updates on new products and partnerships, reflecting Snowflake’s ongoing efforts to strengthen its data and AI platform. We view these enhancements as more incremental than groundbreaking. Key highlights included the introduction of Cortex AISQL, enabling users to analyze multimodal data using SQL, and the launch of Adaptive Compute, which promises enhanced performance and efficiency.
Here are some key takeaways from the summit:
Expanded AI Data Cloud with Crunchy Data acquisition. Snowflake announced the acquisition of Crunchy Data during its summit, adding PostgreSQL support to its AI Data Cloud. This strategic move expands Snowflake’s capabilities beyond analytics to include transactional and operational workloads. Developers can now build, deploy, and scale AI applications more efficiently within a unified platform. This acquisition positions Snowflake as a stronger contender in the data and AI space, enhancing its ability to compete with full-stack platform providers.
Partnering with OpenAI to advance enterprise-ready AI. At the summit, OpenAI CEO Sam Altman highlighted the rapid evolution of AI agents, from simple task assistants to advanced collaborators capable of advanced knowledge discovery. Snowflake announced an expanded partnership with OpenAI, enabling enterprises to build and deploy AI-powered applications directly within Snowflake’s secure and governed platform. By integrating OpenAI’s models, Snowflake will empower organizations to embed generative AI into their analytical workflows and decision-making processes, enhancing data-driven decision-making at scale.
Snowflake Intelligence laying the foundation of agentic AI. Snowflake launched a secure, integrated AI chatbot designed to enable users to interact with their data using natural language. This simplifies self-service data exploration and insight generation, eliminating the need for advanced query skills. Behind the scenes, Snowflake positions this as an agentic platform, capable of agent orchestration, integration with MCP, semantic model optimization, and document parsing. While the current capabilities appear relatively foundational, the platform lays the groundwork for more advanced, intelligent automation and AI-driven analytics in future releases.
Improved performance with Adaptive Compute and Gen2 Warehouse. Snowflake unveiled the new Adaptive Compute, a compute service that intelligently routes queries to the best-suited compute clusters, ensuring efficient and balanced resource utilization. It plans to evolve this service into a fully abstracted, intelligent, and adaptive compute and storage layer in the coming years. It also improved its warehouse data platform with Generation 2 Standard Warehouse, offering 2x performance improvements over the previous hardware. The announcement was well received, particularly for organizations that are beginning to run real-time, low-latency large data workloads on Snowflake.
Multimodal data taking the platform spotlight. While Snowflake has traditionally excelled in structured data, the introduction of Cortex AISQL expands its reach by enabling users to query and analyze diverse data types, including text, images, and audio, directly with SQL. This new capability seamlessly integrates genAI into SQL workflows, unlocking new use cases without requiring data movement. While other vendors have explored multimodal SQL, Snowflake’s approach stands out for its comprehensive SQL coverage and strong emphasis on delivering practical use.
Ramped-up migration tools. Snowflake announced SnowConvert AI, a free, AI-powered migration solution designed to help organizations seamlessly transition their entire data ecosystem to Snowflake, including data warehouses, ETL processes, and BI workloads. It automatically generates test cases using AI to validate the accuracy of converted code, ensuring consistency and reliability throughout the migration process. With AI-driven data validation and migration assistance features, SnowConvert AI will help reduce the manual effort typically required for such projects, saving time and money while minimizing risk.
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