At its BUILD London event yesterday, Snowflake rolled out a new set of AI capabilities aimed at simplifying the development of agents and other advanced applications. The new capabilities include native integration of Snowflake Postgres, Semantic View Autopilot, and Cortex Code, among other tools.
These new capabilities come only a couple of days after Snowflake announced a $200 million, multi-year deal with OpenAI through co-innovation and joint go-to-market efforts. These two moves show Snowflake is bringing enterprise-ready AI directly to customer data, so organizations can build and run AI applications where their data already lives.
Snowflake claims that the new Postgres integration brings transactional application development much closer to Snowflake’s analytical core. What this means is that developers have to face fewer handoffs between systems. They also gain a more unified environment for building data-driven services. With applications sitting closer to the data they depend on, it simplifies architecture and shortens development cycles.
“As businesses move from AI experimentation to production, the real challenge is ensuring AI systems can consistently access data that is connected, governed, and discoverable across the enterprise,” said Christian Kleinerman, EVP of Product, Snowflake.
“That means eliminating data silos, fragile pipelines, and closed systems that slow down AI deployment and increase risk. By bringing unified operational and analytical data, as well as open interoperability together in one platform, we’re empowering customers to develop enterprise-ready AI systems that work with real business data, securely and at scale.”
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The Semantic View Autopilot tackles a different problem – alignment. As AI becomes more ingrained in workflows, there are competing definitions of key metrics. With an automatic semantic layer Snowflake is aiming to make AI systems operate on more consistent and governed business logic. This can help prevent confusion and makes it easier to trust outputs across teams.
With the Cortex Code, Snowflake introduces an additional dimension by providing an AI coding assistant that works with enterprise data context. Developers are often frustrated with generic code suggestions. Cortex Code aims to solve this. It first analyzes the structure and understandsmeaning of the organization’s data. Only then it moves to assist developers in moving faster while staying within the data governance boundaries.
If you look at the new AI capabilities, you can tell Snowflake is keen on investing in the connective tissue between raw data and production AI. It positions its platform as a place where data and intelligence come together to allow enterprise AI to be a natural extension of the existing workflows and not separate projects.
The $200M deal with OpenAI extends this same theme into the model layer. By integrating OpenAI models directly with enterprise data, Snowflake is trying to remove one of the biggest barriers to production AI – the distance between where models run and where data lives.
This is a significant move because enterprise AI is not just about model quality. It is also about access and consistency. The users also need to trust the data. With OpenAI models operating alongside Snowflake’s data services, users gain a more direct way to turn internal data into AI-driven insights across diverse use cases.
“By bringing OpenAI models to enterprise data, Snowflake enables organizations to build and deploy AI on top of their most valuable asset using the secure, governed platform they already trust,” said Sridhar Ramaswamy, CEO, Snowflake.
“Customers can now harness all their enterprise knowledge in Snowflake together with the world-class intelligence of OpenAI models, enabling them to build AI agents that are powerful, responsible, and trustworthy.”
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The OpenAI models were already available to Snowflake customers. However, they were only available through external integrations. Now with native access, those models run directly inside Snowflake’s AI environment. That removes much of the setup work that usually slows projects down. In addition, Snowflake is also supporting multiple model providers. This gives customers more choice.
Compared to some of its competitors, such as Databricks, Snowflake might have been late in making AI development its top priority. However, it has made some serious ground in the last 12 months. It spent that time evolving from a data warehouse platform into a full AI execution platform.
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Author: Ali Azhar
