Snowflake announced new product innovations that provide enterprises with easy-to-use tools, an interoperable environment, and trusted AI agents so they can move data and AI projects from idea to production faster.
These enhancements include the general availability of Cortex Code, a data-native coding agent built to automate and accelerate end-to-end enterprise development, providing users with an agent that deeply understands and operates within their enterprise data context. The general availability of Semantic View Autopilot, an AI-powered service that automates the creation and governance of semantic views, gives AI agents a shared understanding of business metrics to deliver consistent, trustworthy outcomes. With new enhancements to Snowflake Postgres (generally available soon), the world’s most popular database1, now runs natively in the AI Data Cloud, allowing enterprises to consolidate transactional, analytical, and AI use cases onto a single, secure platform.
Furthermore, Snowflake recently announced a new multi-year, $200 million partnership with OpenAI, which cements a joint commitment to co-innovation, joint go-to-market (GTM) strategies, and makes OpenAI models natively available to Snowflake’s 12,600 global customers within Snowflake Cortex AI.
“For AI to truly deliver value, it must move beyond experimentation and become engrained within the systems teams rely on every day,” said Christian Kleinerman, EVP of Product at Snowflake. “With our latest product advancements, we’re reimagining how teams build and operate by embedding AI directly into the development lifecycle, making data AI-ready by design and helping enterprises deliver real business impact with AI. This marks a fundamental shift in how organisations build with data and AI, enabling users to build solutions that are reliable, governed, and ready to run at enterprise scale.”
Snowflake and OpenAI forge $200 million partnership to bring enterprise-ready AI to the world’s most trusted data platform
Snowflake recently announced a new collaboration with OpenAI that enables global enterprises to unlock greater value from their proprietary data with AI.
This multi-year, $200 million partnership agreement cements Snowflake and OpenAI’s commitment to co-innovation and joint GTM strategies. Snowflake and OpenAI will work closely together to co-develop and deploy customised AI solutions for joint enterprise customers that deliver tangible return on investment.
The partnership also makes OpenAI models natively available to Snowflake’s 12,600 global customers within Snowflake Cortex AI. This empowers global organisations like Canva and Whoop to bring OpenAI models directly to their enterprise data for deep research and instant insights. OpenAI models like GPT-5.2 will also be accessible within Snowflake Intelligence, the trusted enterprise intelligence agent that empowers every employee to securely access, analyse, and act on all their organisation’s knowledge using natural language.
Snowflake unveils Cortex Code, an AI coding agent that understands your enterprise data context to help teams build faster
With the general availability of Cortex Code, users gain an agent that deeply understands and operates within their enterprise data context. It empowers everyone regardless of their technical-expertise, from data experts to domain experts, to build data pipelines, analytics, and AI apps faster.
As businesses race to deliver real impact with AI, teams across organisations face growing pressure to move faster without sacrificing trust, accuracy, or scale. To move data and AI initiatives forward faster and more reliably, teams require purpose-built tooling that understands their data environments, simplifies complex tasks, and enables sophisticated, trusted workflows through natural language.
Unlike generic coding assistants, Cortex Code understands users’ Snowflake data, compute, governance, and operational semantics. Cortex Code is customisable and interoperable, designed to work wherever users operate across Snowflake experiences and local developer environments. In addition, it fits naturally into existing workflows and supports the entire development lifecycle, from design and implementation to optimisation and operations, without compromising trust or security. Teams can use Cortex Code within the Snowflake platform through Cortex Code in Snowsight (generally available soon) or within their preferred terminal or code editor like VS Code or Cursor with Cortex Code CLI (now generally available).
To further reduce the friction that slows enterprise AI adoption and delivery, Snowflake has introduced new capabilities for vibe coding, advancing how users build, deploy, and manage AI-powered data workflows across the stack with a new integration with v0 by Vercel (generally available soon). This enables employees, from developers to analysts, to create rich, AI-powered apps with natural language that can be deployed securely inside of Snowflake through Snowpark Container Services.
Snowflake delivers Semantic View Autopilot to ensure that AI agents operate on the same trusted business definitions
Snowflake unveiled Semantic View Autopilot (now generally available), an AI-powered service that continuously learns from real user activity to ensure business logic remains accurate and up-to-date. As a result, enterprises can minimise AI hallucinations while cutting semantic model creation from days to minutes, accelerating time-to-market and delivering a decisive competitive advantage. These innovations expand upon Snowflake’s existing enterprise-grade foundations, ensuring that AI systems such as Snowflake Intelligence are trusted, governed, and ready to operate reliably at scale, all while working directly on organisations’ most valuable data.
Enterprises are deploying AI agents into environments where business metrics are manually defined and inconsistently governed, leaving AI systems without a shared understanding of business context. This fragmented and manual approach to building the semantic layer is a major bottleneck for AI adoption, producing unreliable outputs and making it harder for organisations to trust AI.
Semantic View Autopilot addresses this challenge by automatically building, optimising, and maintaining governed semantic views, eliminating the need for manual, error-prone semantic modeling. This builds on Snowflake’s commitment to initiatives like the Open Semantic Interchange (OSI), which standardises a shared semantic layer across ecosystem leaders. While OSI provides the connectivity to share business logic across the ecosystem, Semantic View Autopilot adds the intelligence to create and continuously maintain it, making it the connective layer for trustworthy, scalable AI across all data, wherever it lives.
Snowflake makes enterprise data AI-ready with Snowflake Postgres and advanced innovations for open data interoperability
Snowflake announced new enhancements to Snowflake Postgres, which now runs natively in the AI Data Cloud, allowing users to easily access, ingest, and migrate data from anywhere to build what they need on a single, secure platform.
Most organisations still keep their transactional and analytical databases siloed on separate systems, a legacy approach that forces teams to rely on complex pipelines to connect those systems. This fragmentation adds steep costs, slows development, introduces risk, and delays insights. Snowflake Postgres eliminates these pipelines by bringing transactional, analytical, and AI use cases together on a single, enterprise-ready platform. In turn, full compatibility with open-source Postgres allows companies to move their existing apps onto Snowflake, without code changes. Now with Snowflake Postgres, teams can power critical AI and apps, analyse business performance and trends using the most up-to-date data from their operations, and build AI-driven features like recommendations or forecasting — all from the same data, without having to move it between systems.
Discussion about this post