Snowflake barrage adds more AI development, analysis tools
A streaming data service and tools that provide agents with contextual awareness highlight the latest from the vendor as it constructs a foundation for agentic enterprises.
Snowflake on Tuesday unveiled an avalanche of new features aimed at helping customers build AI tools that make employees better informed and more efficient.
Among others, they include a fully managed streaming data service in Snowflake CoCo (formerly Cortex Code), which is the vendor's coding agent for developing workflows and applications, and personalization capabilities in Snowflake CoWork (formerly Snowflake Intelligence), which is a personal agent that assists users as they analyze data and build data workflows.
In addition, Snowflake introduced new tools in Horizon Catalog, a data catalog that enables users to govern and discover data, aimed at securing and governing agents and standardizing the context agents call upon to carry out tasks.
The new features were revealed during Snowflake Summit, the vendor's user conference in San Francisco.
Michael Ni, an analyst at Constellation Research, noted that by unifying capabilities in CoCo, CoWork and Horizon Catalog, Snowflake is demonstrating its evolution toward becoming a platform for agentic AI. As a result, its additions are significant.
"Snowflake's release looks less like a product launch cycle and more like platform maturation," Ni said. "There are plenty of new features, but the real significance lies in Snowflake's … bigger strategic ambition as it shifts from being the Data Cloud, where the story was 'bring AI to your data', to the 'Agentic AI Platform' with the story of using trusted context to govern AI actions across the enterprise."
Sanjeev Mohan, founder and principal of analyst firm SanjMo, likewise noted that Snowflake's new features collectively comprise a significant update with a new feature called Cortex Training, which allows users to customize foundation models, showing the vendor's growth.
"It's significant due to the breadth," he said. "Many vendors ship features in one or two layers of the stack. Snowflake shipped simultaneously across infrastructure, metadata and semantics, security and AI surfaces for both developers and knowledge workers. The Cortex Training announcement … could be a net-new revenue category. Thus far, training or fine-tuning small models has not become mainstream."
Based in Bozeman, Mont., but with a campus in Menlo Park, Calif., Snowflake's data platform and AI development capabilities are designed to enable users to build AI and analytics tools on a trusted data foundation. Beyond introducing new features, Snowflake on May 27 expanded its partnership with AWS, signing a collaboration agreement to invest $6 billion in helping joint customers build and deploy AI.
Empowering enterprises with AI
Snowflake was slow to add AI development and management capabilities after OpenAI's November 2022 launch of ChatGPT sparked surging interest in AI development that continues to increase.
Rival Databricks and hyperscale cloud vendors AWS, Google Cloud and Microsoft all quickly added integrations with large language models such as ChatGPT -- some even developing their own -- and created development frameworks designed to simplify building AI tools.
It's significant due to the breadth. Many vendors ship features in one or two layers of the stack. Snowflake shipped simultaneously across infrastructure, metadata and semantics, security and AI surfaces for both developers and knowledge workers.
Sanjeev MohanFounder and principal, SanjMo
Following a CEO change in February 2024, Snowflake similarly embraced AI as a core part of its platform and continues to add AI capabilities that simplify using its tools as well as features that enable customers to create their own AI applications.
"Our whole mission is based on the premise that we are the platform that will help organizations make every team member be more productive … through the benefits of AI, and do so being able to sleep well at night because of security, compliance and governance," Christian Kleinerman, Snowflake's executive vice president of product, said during a virtual press conference on May 26.
Many of the new capabilities Snowflake revealed on Tuesday are tied to that aim.
Snowflake CoCo is the interface for developers to build the AI and analytics workflows that enable business users to be more productive. New CoCo features include Datastream to bring real-time Apache Kafka data into AI applications to keep them current and accurate, desktop and mobile versions that enable developers to work in preferred environments, Automations to autonomously execute recurring workflows, and prebuilt Skills that simplify engineering tasks.
Snowflake CoWork is the AI-powered interface that enables business users to be more productive. New CoWork capabilities include User Skills to personalize insights and actions based on an employee's role, Deep Research to enable in-depth analysis across both structured and unstructured data and Cortex Sense to join data with business definitions and operational knowledge to provide agents with better context.
Horizon Catalog is the hub that connects and governs an enterprise's Snowflake estate, enabling development and analysis. New Horizon Catalog tools include Horizon Context to provide context layer that ensures AI-driven outcomes are reliable, Semantic Studio and Semantic View Autopilot to build semantic views, Agent Identity to give each agent a verified identity before it can access data or take action, and adaptive compute to automatically optimize compute and software resources.
"Horizon Context along with Cortex Sense are probably the most valuable [new capabilities]," Mohan said. "AI agents are only as reliable as the definitions they reason from. … Snowflake's earlier Semantic Studio and Semantic View Autopilot along with the [Open Semantic Interchange] standard solved this at the platform level. Now they are taking it to a higher level -- context."
Finally, to improve access to the often distributed data that informs agents and other AI tools, Snowflake is improving the interoperability of its platform with capabilities such as support for Apache Iceberg v3, zero copy integrations with data sources including SAP and Salesforce, centralized governance across systems through Apache Polaris within Horizon Catalog, and Open Data Sharing to enable organizations to securely share data and AI assets with customers and partners.
While each of the individual features address customers' evolving needs, the most important additions are the tools that deliver trusted, relevant data to agents, according to Ni.
"The most valuable thing Snowflake announced wasn't another agent," he said. "It was the shared understanding that those agents operate from. Snowflake recognizes that when intelligence becomes cheap with the new LLMs, the hardest problem in enterprise AI is ensuring multiple humans, BI tools, applications, and agents operate from the same business truth."
Competitive standing
Although Snowflake was once slow to react to surging interest in AI development, the vendor is now one of many data management vendors in a race to provide the tools customers need to develop and manage agentic AI systems on a foundation of governed data, according to Ni.
Amid that race, however, Snowflake is carving out its own niche rather than directly competing with rival Databricks, he continued.
"While data and AI platform vendors like Databricks focus on helping developers build agents, Snowflake focuses on making agents simple to trust and scale," Ni said. "At the same time, the market is moving from agent creation to agent governance, and that's where Snowflake is making its biggest bet."
Mohan similarly noted that Snowflake is taking a different approach than Databricks, which has historically catered more to data scientists and engineers than Snowflake, which focuses on business users.
"Databricks' strengths are in the machine learning and data engineering workflow," he said. "Snowflake's counter-play is governance-first agent infrastructure. For teams building ML-heavy systems, Databricks is still stronger. For enterprises that need AI with audit trails and consistent business semantics, and want it to work for business users, Snowflake's Summit announcements make a strong case."
Regarding what more Snowflake can add to continue serving its customers as they attempt to modernize with agents, Mohan suggested that the vendor add tools that oversee how agents behave in production so that customers don't have to seek out such capabilities from competitors.
"Snowflake now has CoCo for developers and CoWork for knowledge workers, but … the hardest problems are operational, like monitoring, debugging, regression testing for agent behavior," he said. "Snowflake should build native tooling here before customers are forced to stitch together third-party solutions."
Ni, meanwhile, advised Snowflake to continue adding and refining features such as Horizon Context that help agents understand an enterprise's operations so they can perform as intended.
"Horizon Context is important to helping AI understand what the business means," he said. "The next frontier is helping AI understand how the business operates. Most enterprise decisions are not driven by data alone, but by the combination of business context, process state and operational constraints."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
