• Leading organizations like EVgo and STARS are leveraging Snowflake’s unified, intelligent development environment to reduce operational overhead, increase interoperability, accelerate developer efficiency, and lower total cost of ownership

• Enhanced AI-native and collaboration tools accelerate the development of sophisticated, enterprise-grade agentic AI apps by allowing developers to write code faster and more reliably

• Developers can build securely and collaboratively with enhanced features like Workspaces, Git and VS Code integrations, increased support for open source tooling, and more

Snowflake , the AI Data Cloud company, announced a suite of new developer tools designed to help organizations rapidly build, test, and deploy cutting-edge, enterprise-ready AI apps faster and more securely. New enhancements to Snowflake’s developer collaboration environment, seamless open source integrations, and new data quality capabilities further accelerate productivity and reduce overhead, helping teams drive measurable business value at scale — all within a single, governed platform.

“The success of enterprise AI hinges on having the most trustworthy data, and the most productive developers,” said Christian Kleinerman, EVP of Product, Snowflake. “By delivering a single, intelligent, and governed environment, we’re not just accelerating code development and execution – we’re giving every developer a shorter, simpler path to build enterprise-ready AI apps that actually drive value. This is the new blueprint for enterprise innovation and a demonstration of how Snowflake is delivering on its promise of limitless interoperability.”

“Snowflake’s new developer capabilities have been transformative, empowering us to build data pipelines with the flexibility and interoperability we need, all while using the tools that best fit our workflow,” said Andre Byfield, Principal Data Architect, Enlyte. “dbt Projects on Snowflake allowed us to deploy and orchestrate our dbt pipelines directly on the Snowflake platform rather than having to build out that cloud infrastructure ourselves. This represented real cost and time savings for our lean data engineering team and delivered real-world value to our stakeholders.”

Build with AI for Accelerated Agentic AI Development

The era of agentic AI is already underway, with 20% of organizations actively deploying agents and another 54% planning to deploy within the next 12 months¹. However, this exponential growth has only intensified the pressure on data engineering teams to rapidly manage the vast volumes and types of data that power AI.

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Snowflake is addressing these pain points head-on by providing AI-native developer tools designed to help teams move into production faster, and with more confidence:

• Build AI Apps More Efficiently: Developers can now streamline their data workflows with Cortex Code (in private preview), a refreshed AI assistant within the Snowflake UI that lets users interact with their entire Snowflake environment using natural language. Cortex Code helps users easily understand their Snowflake usage, optimize complex queries, and fine-tune their results to maximize cost savings.
• Accelerate Secure Development at Scale: With enhancements to Snowflake Cortex AISQL (now generally available), developers can build scalable AI pipelines within Snowflake Dynamic Tables (now generally available) to create AI-inference pipelines through a simple declarative SQL query. Leveraging AI Redact (in public preview soon) within Cortex AISQL, users can scale more confidently with the ability to detect and redact sensitive data from unstructured data, allowing them to ready their multimodal dataset for AI while maintaining security and privacy.

Snowflake Provides the Mission-Critical, Open Foundation for App Development

Snowflake empowers developers with world-class tooling, coupled with interoperability across a wide-range of third-party products, so they can build the way they want, with their preferred solution. This choice and flexibility is critical to developer productivity, allowing them to collaborate across AI app development and supercharge velocity.

With Snowflake’s latest innovations, developers can build using the tools they already know and love, without leaving the secure and governed Snowflake platform:

• Accelerate Development with Seamless Collaboration: Snowflake’s centralized development environment Workspaces (now generally available) eliminates siloed data work and boosts collaboration, providing a unified editor for creating, organizing, and managing code across multiple file types. Workspaces is enhanced with direct Git Integration (now generally available), providing developers with a seamless way to review version control, and VS Code Integration (now generally available), allowing users to work from their preferred Integrated Development Environment (IDE) and share code with the rest of their team.
• Reduce Overhead with Support for Existing Tools: With dbt Projects on Snowflake (now generally available), enterprises such as Enlyte, InterWorks, NTT DOCOMO, and STARS can build, test, deploy, and monitor their dbt projects directly within their Snowflake environment — empowering engineers to focus on delivering insights rather than maintaining various tools and infrastructure.
• Increase Productivity with Fewer Code Changes: Snowflake is helping organizations like VideoAmp further accelerate developer productivity by running existing Apache Spark™² code on Snowflake’s secure engine with Snowpark Connect for Apache Spark (now generally available). Snowflake partners including BlueCloud, Infosys, Kipi.ai, a WNS Company, and Tredence are further supporting enterprises with their use of Spark on Snowflake’s platform through Snowpark Connect for Apache Spark, recognizing it as the leading platform for developer use cases. With the Snowpark execution engine, teams have achieved 5.6x faster performance and 41% cost savings over managed Spark environments³.

“As a non-profit that delivers life-saving care every day, every dollar counts. When we rebuilt our data and analytics platform, we needed right-size tooling that balances capability with simplicity and cost,” said Chris Androsoff, Director of Data, STARS. “The moment dbt became part of the Snowflake ecosystem, the path was clear. we experiment, codify, test, deploy, schedule, and monitor our entire dbt workflow natively inside Snowflake. Consolidating on one platform has created helpful simplicity, improved cost transparency, and freed our engineers to focus on delivering value faster.”

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Snowflake Promotes Data Quality and Code Security to Build with Confidence

In order to deploy agentic AI apps at scale, data teams need to ensure that both the quality and security of the data feeding their initiatives is best-in-class. To simplify the complex task of monitoring and reporting on data reliability, Snowflake has enhanced its Data Quality User Experience (UI) (in public preview), allowing developers to assess how accurate and trustworthy the data is, and automatically generating a summary for increased insights. With upgrades to Code Security (now generally available), teams also benefit from new security constructs that remove the risk of unsecured access to developer code to eliminate data poisoning or block unauthorized model tampering.