Snowflake is putting $6 billion behind a bet that the next phase of enterprise artificial intelligence will be decided not by which company builds the flashiest model, but by which one can get trusted data into an AI system’s hands without breaking it. The AI Data Cloud company, Snowflake, has signed a multi-year strategic collaboration agreement with Amazon Web Services, committing to its largest infrastructure investment on AWS to date as the two companies work to help joint customers push agentic AI out of pilot programs and into everyday business operations.

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The commitment lands at a moment when enterprises across North America and the Gulf are asking a similar question: now that generative AI has proven it can write, summarize, and analyze, can it be trusted to actually do something inside a company's core systems. Snowflake and AWS are betting the answer depends less on model size and more on whether that AI can reach governed, trustworthy data without an organization losing control of it.

A Long Partnership Enters Its Agentic Phase

Snowflake was founded on AWS eleven years ago, and the relationship has grown into one of enterprise software's broadest and deepest collaborations. The majority of Snowflake's customers run on AWS today, and AWS has recognized Snowflake as a leading partner driving global customer adoption. The new agreement deepens that foundation through expanded product integrations across generative and agentic AI, a broader go-to-market push through AWS Marketplace, and joint investment in customer success programs, workload migrations, and industry-specific solutions built to move enterprises from AI experimentation to production-scale outcomes.

“AI has generated enormous excitement, but for enterprises, the real challenge and opportunity is turning intelligence into action,” said Sridhar Ramaswamy, CEO of Snowflake. “We are moving into the era of the agentic enterprise, where AI systems don’t just answer questions, but help organizations reason over trusted data, coordinate workflows, and drive real business outcomes. With AWS, we are making it easier for enterprises to bring AI directly to governed data, so they can move faster, operate with greater clarity, and create measurable impact at scale.”

Turning Intelligence Into Action

Matt Garman, CEO of AWS, framed the collaboration in similar terms, pointing to a market that has largely moved past proof-of-concept AI projects.

“Enterprises are rapidly moving from experimenting with AI to putting intelligent agents to work that drive real business outcomes,” said Matt Garman. “Snowflake has built on AWS since day one, and their deepened commitment to run on Graviton delivers the world-class performance, flexibility, and cost savings customers need to run data warehousing and AI workloads at scale.”

That reference to AWS Graviton processors points to a practical dimension of the deal that sits underneath the headline figure. Snowflake is leaning further into Graviton's price-performance advantages while also using high-performance, GPU-accelerated Amazon EC2 instances for AI model training and inference, giving customers a wider range of options as their workloads scale from analytics into full agentic systems.

Bringing Models to Where the Data Already Lives

The technical architecture behind the expanded collaboration addresses a problem that has quietly slowed enterprise AI adoption industrywide: AI is only as useful as the data behind it, and moving sensitive information between systems introduces both complexity and risk. Snowflake Cortex AI lets customers build and deploy AI applications for text-to-SQL, summarization, sentiment analysis, and entity extraction directly inside their Snowflake environment, allowing them to run AI on governed, trusted data without pushing it outside their secure perimeter. That design choice, bringing the model to the data rather than the other way around, is increasingly what separates enterprises that get AI into production from those still stuck running pilots.

A Familiar Pattern for the Gulf's Own AI Ambitions

The logic behind this expanded collaboration will sound familiar to enterprises across the GCC, where Snowflake has already applied the same governed-data approach regionally. Earlier this year, Snowflake brought general availability of its AI Data Cloud to the AWS Middle East, UAE Region, a move designed to give organizations lower latency, stronger performance, and an easier path to meeting local data residency requirements as the UAE works to translate its National AI Strategy 2031 into measurable enterprise adoption. The throughline connecting that regional rollout to this week's multi-year, multi-billion-dollar commitment is consistent: enterprises everywhere, whether in Seattle or Dubai, are less interested in AI as a standalone experiment and more focused on AI that operates safely, quickly, and directly on the data they already trust.

For North American enterprises watching their own AI budgets shift from testing to deployment, the scale of Snowflake's commitment offers a useful signal. Infrastructure spending at this level rarely happens on speculation. It tends to follow demand that is already showing up in customer pipelines, and both companies are pointing to exactly that kind of momentum as the basis for the deal.

The expanded collaboration also widens the commercial pathway between the two companies, with joint investments planned in customer success programs and workload migration support designed to help enterprises move faster once they decide agentic AI is ready for production. For an industry that has spent the better part of two years debating whether AI hype would translate into real operational value, Snowflake and AWS are using their checkbooks to answer that question themselves.

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