Snowflake Unveils New Developer Tools to Supercharge Enterprise-Grade Agentic AI Development
- 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 (NYSE:SNOW), the AI Data Cloud company, today 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.
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Developers can build securely and collaboratively with enhanced features like Workspaces, Git and VS Code integrations, increased support for open source tooling, and more
"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 today 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.
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. Today 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."
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.
Learn More:
- Dive deeper into the updates to Snowflake's developer environment supercharging the future of agentic AI development in this blog post.
- Learn how to get started with Snowflake's Intro to Data Engineering using Python in Snowflake and Data Engineering Pipelines with Snowpark Python Quickstarts.
- Check out all the innovations and announcements coming out of BUILD 2025 on Snowflake's Newsroom.
- Stay on top of the latest news and announcements from Snowflake on LinkedIn and X, and follow along at #SnowflakeBUILD.
1 MIT Technology Review Insights, Redefining Data Engineering in the Age of AI, October 2025. Based on a survey of 400 global senior data and technology executives.
2 "Apache Spark" is a registered trademark or trademark of the Apache Software Foundation in the United States and/or other countries.
3 Based on customer production use cases and proof-of-concept exercises comparing the speed and cost for Snowpark versus managed Spark services between November 2022 and May 2025. All findings summarize actual customer outcomes with real data and do not represent fabricated datasets used for benchmarks.
Forward Looking Statements
This press release contains express and implied forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements regarding (i) Snowflake's business strategy, plans, opportunities, or priorities (ii) the release, adoption, and use of Snowflake's new or enhanced products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, (iv) Snowflake's vision, strategy, and expected benefits relating to artificial intelligence and other emerging product areas, including the expected benefits and network effects of the AI Data Cloud, and (v) the integration, interoperability, and availability of Snowflake's products, services, and technology offerings with and on third-party platforms. Other than statements of historical fact, all statements contained in this press release are forward-looking statements. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading "Risk Factors" and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. Forward-looking statements speak only as of the date the statements are made and are based on information available to Snowflake at the time those statements are made and/or Snowflake management's good faith belief as of that time with respect to future events. Except as required by law, Snowflake undertakes no obligation, and does not intend, to update these forward-looking statements to reflect events that occur or circumstances that exist after the date on which they were made.
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About Snowflake
Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 12,000 customers around the globe, including hundreds of the world's largest companies, use Snowflake's AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone. Learn more at snowflake.com (NYSE:SNOW).
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Media Contacts:
Caroline McInerney
Product PR Specialist, Snowflake
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