Snowflake is committing $6 billion to AWS over multiple years as the cloud data company works to secure the compute capacity needed to support growing enterprise AI workloads.
The expanded agreement deepens Snowflake’s long-running relationship with AWS and comes as the company reports stronger revenue growth tied to AI adoption.
Revenue outlook improves as AI demand lifts sentiment
The stock jumped more than 30% after the announcement, helped by a stronger full-year outlook. For a company that had been swept up in the broader selloff around AI software, that was enough to change the mood fast.
The significant leap in Snowflake shares, which had done a 20% dip this year through last close, “tells you just how much [skepticism] had built up as data names were caught in the broader AI software sell-off,” Matt Britzman, senior equity analyst, Hargreaves Lansdown, told Reuters.
“But it also shows how quickly sentiment can turn when a company shows AI is already helping the top line, rather than simply decorating the slide deck.”
Snowflake reported quarterly revenue of $1.39 billion, up more than 33% year over year. The company also raised its product revenue guidance for the full fiscal year to $5.84 billion.
Snowflake’s AWS commitment strengthens AI infrastructure access
The AWS agreement is doing a wee bit more than expanding an existing relationship… it is a bet on access to infrastructure.
Under the deal, Snowflake will commit $6 billion to AWS over multiple years, securing capacity on services that include the company’s custom Graviton processors.
As we know, demand for compute tied to AI workloads has been outpacing supply, especially as enterprises move beyond testing and into ongoing usage.
Snowflake has long been tied to AWS, where the majority of its customers already live. The new agreement really seals that connection, specifically around AI workloads that rely on data, compute, and models all talking to each other.
The companies said the collaboration will include deeper integrations across generative and agentic AI, along with joint investments to help customers move faster from early experimentation to production.
What this means for the channel ecosystem around Snowflake
For channel partners, the deal points to rising customer demand for AI-ready data infrastructure that can move beyond pilots and into production.
MSPs, cloud consultants, and systems integrators working with AWS and Snowflake may see more opportunities around data modernization, AI workload migration, governance, and managed services as enterprises look to operationalize AI on existing cloud platforms.
Read more: We covered Snowflake’s 2025 updates to its reseller program, which introduced new investments in enablement and a renewed focus on customer outcomes.
Natoma acquisition extends Snowflake’s AI governance strategy
Alongside the AWS deal, Snowflake is pushing further into the management of AI systems. The company announced plans to acquire Natoma, a platform focused on the Model Context Protocol (MCP) for AI agents.
That move expands Snowflake’s focus beyond storing and processing data into governing how AI systems act on that data across an organization.
“AI continues to be a powerful tailwind for Snowflake, and Q1 marks a clear inflection point in that journey,” said Sridhar Ramaswamy, CEO, Snowflake Inc.
“With Cortex Code and Snowflake Intelligence, we are extending from the trusted foundation for enterprise data and context to become the control plane for the Agentic Enterprise. We are seeing strong momentum from both AI-driven acceleration of our core platform and growing adoption of our first-party AI products, positioning Snowflake to lead in this new era.”
Snowflake has been steadily rolling out tools like Cortex Code and Snowpark to help customers build and run AI applications directly on their data – this added layer is now making sure those systems behave themselves.
So basically, less time in test environments, more time seeing what happens when this stuff runs on real data, all day. At that point, it just… has to work.
Dell, NVIDIA, and Elastic have been working on the same problem from a different angle: helping companies connect AI models to the data they actually need. Recent updates to the Dell AI Data Platform focused on bringing together storage, retrieval, infrastructure, and inference in a way that’s ready for production workloads, not just demos.