Snowflake has signed a five-year, $6 billion agreement with Amazon Web Services, giving AWS another major win in the race to supply infrastructure for artificial intelligence workloads.
The agreement gives Snowflake access to AWS compute infrastructure, including Graviton processors and AI-related cloud capacity. Snowflake, best known for its cloud data platform, is increasing its dependence on high-performance cloud systems as more customers move data-heavy and AI-driven workloads onto its platform.
The deal is also important for Amazon. AWS is trying to prove that it can compete more aggressively in AI infrastructure at a time when Nvidia dominates the conversation around AI chips and Microsoft, Google, and Oracle are also fighting for large enterprise cloud contracts.
What Snowflake Is Buying From AWS
The deal centers on AWS infrastructure that Snowflake can use to power data processing, AI applications, and enterprise workloads. A major part of the agreement involves AWS Graviton processors, Amazon’s Arm-based central processing units built for cloud computing.
Graviton chips are not the same as Nvidia GPUs, which are widely used for training and running advanced AI models. CPUs still play a major role in the AI stack because many enterprise workloads require data movement, orchestration, queries, application logic, and agentic computing processes that do not always need GPU-heavy infrastructure.
For Snowflake, the agreement gives it long-term access to AWS capacity as demand grows for AI-powered data tools. The company has been expanding beyond traditional cloud data warehousing into machine learning, generative AI, and enterprise AI development.
Why This Deal Matters for Amazon
The agreement gives AWS a large customer commitment at a time when the cloud infrastructure market is being reshaped by AI demand. Amazon built AWS into the largest cloud computing business by serving storage, compute, database, and enterprise software workloads. The rise of AI has forced every major cloud provider to prove that it can supply the chips and infrastructure needed for modern AI systems.
Microsoft has benefited from its partnership with OpenAI. Google has pushed its own AI chips and Gemini models. Oracle has positioned itself as a major AI infrastructure provider through large cloud deals. Amazon, meanwhile, has been investing in its own chip strategy, including Graviton CPUs and Trainium AI accelerators.
Snowflake’s commitment helps AWS show that its chip and cloud infrastructure strategy is not limited to internal use. Large enterprise software companies are willing to commit billions of dollars to AWS systems for AI-era workloads.
Why Snowflake Needs More AI Infrastructure
Snowflake’s business depends on storing, processing, and analyzing massive amounts of enterprise data. As companies adopt AI tools, that data becomes even more important. AI agents, machine learning workflows, and generative AI applications all need structured access to business data.
Snowflake has been adding AI features through products such as Snowflake Cortex, which helps companies build AI applications on top of their own data. It has also expanded support for data engineering, model development, and application building through Snowpark and related tools.
That shift increases Snowflake’s need for reliable cloud compute capacity. AI features may help Snowflake sell more services, but they also require more infrastructure behind the scenes. A five-year agreement with AWS gives Snowflake a clearer foundation for scaling those services.
The CPU Angle Is Important
Much of the AI chip conversation has focused on Nvidia GPUs, especially because they are widely used for model training and high-performance inference. The Snowflake deal highlights a different part of the AI infrastructure market: CPUs.
AI systems do not run on GPUs alone. Enterprise AI platforms still need CPUs for data preparation, workflow coordination, application services, database queries, and many of the background tasks that make AI applications usable inside businesses.
Agentic AI could increase that demand. AI agents often perform many small, repeated tasks across software systems. These workloads may require large amounts of general-purpose compute in addition to specialized GPU power.
That is why AWS Graviton matters in this deal. Amazon is positioning its own chips as a cost-efficient option for cloud customers that need scalable compute, especially as AI workloads become more deeply integrated into enterprise platforms.
Snowflake’s Business Momentum
The AWS deal came as Snowflake reported stronger business momentum. The company raised its annual product revenue forecast as enterprise demand for AI and data services increased. Snowflake also reported quarterly revenue that beat analyst expectations.
Investors responded strongly to the news. Snowflake shares jumped after the company announced the AWS agreement and improved forecast, reflecting renewed confidence that AI-related demand could support growth across its platform.
The market reaction also showed how closely investors are watching cloud software companies for proof of real AI revenue. Snowflake is not only talking about AI as a future opportunity. It is signing infrastructure agreements and raising forecasts while customers increase spending on data and AI workloads.
What It Means for Cloud Competition
The deal strengthens the relationship between Snowflake and AWS, but it also reflects the broader competition among cloud providers. AI has made infrastructure more strategic than ever. Companies are no longer only choosing cloud vendors for storage or basic computing. They are choosing platforms that can support the next generation of AI applications.
For AWS, the agreement is another sign that its custom chip strategy is becoming more important. Amazon is not trying to replace Nvidia across all AI workloads. Instead, it is building a broader compute portfolio that includes CPUs, AI accelerators, and managed cloud services.
For Snowflake, the deal locks in access to a major cloud partner while it continues to move deeper into AI. The company needs infrastructure that can handle growing customer demand without slowing down product expansion.
Why This Is Bigger Than One Cloud Deal
Snowflake’s $6 billion AWS commitment shows how AI is changing the economics of cloud software. Data platforms are no longer just paying for storage and standard compute. They are making long-term infrastructure commitments so they can support AI tools, agentic workflows, and customer-facing automation at scale.
The deal also shows that the AI infrastructure market is expanding beyond Nvidia GPUs. GPUs remain critical, but CPUs, cloud networking, storage, databases, and managed services are all becoming part of the AI buildout.
Amazon benefits because AWS gets a major public commitment from one of the most important cloud data companies. Snowflake benefits because it secures infrastructure for a product roadmap increasingly built around AI. The wider market gets another signal that enterprise AI spending is moving from experimentation into large, long-term infrastructure contracts.
