HomeSnowflake Signs $6B AWS Deal for AI Chips
Snowflake, a leading cloud data platform, has entered into a $6 billion, five-year partnership with Amazon Web Services, confirmed on Wednesday.
While Snowflake has historically operated on AWS, it now supports Microsoft Azure and Google Cloud as well. To contextualize the scale of this agreement, Snowflake has generated $7 billion in total revenue through the AWS Marketplace since its 2012 founding, meaning this new contract nearly equals its entire historical earnings from that channel.
Snowflake attributes this surge to accelerating customer spending on AWS, which doubled in 2025 to reach $2 billion for the year alone.
AI adoption is the primary driver of this expansion. Snowflake has offered its Cortex AI development tools for several years, leveraging the fact that enterprises store vast amounts of data within Snowflake. These tools enable natural language database queries, automated reporting, and other capabilities.
Significantly, this agreement secures Snowflake greater access to AWS’s proprietary ARM-based Graviton processors.
As AI transitions from model training to operational automation and agent-based workflows, CPU demand increases sharply. While GPUs manage training and complex reasoning, CPUs handle the majority of other AI-related tasks, especially those involving agents.
Last month, AWS CEO Andy Jassy highlighted that Amazon’s custom AI chips deliver superior price-performance compared to Nvidia’s products, despite AWS continuing to utilize Nvidia hardware. High demand for AI processing has led cloud providers like AWS to deploy chips rapidly. Additionally, most major AI model developers have optimized their applications specifically for Nvidia’s architecture.
Nevertheless, Amazon’s custom chips offer a cost-effective alternative for deployment. As a price-focused organization, Amazon passes these savings on to its clients.
These cost advantages are attracting multi-billion-dollar contracts. For example, AWS recently agreed to supply millions of Graviton chips to Meta to support its expanding AI infrastructure, a significant victory after Meta had previously committed $10 billion to Google Cloud.
Furthermore, these agreements signal to Nvidia that cloud providers are developing competitive CPUs to capture market share. Google has produced custom AI chips for years, and Microsoft introduced its Maia AI processor in January.
Nvidia CEO Jensen Huang recently stated he is prepared to defend and expand his market position. The company’s new AI-specific CPU, Vera, represents a “brand new” $200 billion market opportunity, following another record-breaking quarter. Huang noted that $20 billion in sales has already been secured.
Although Nvidia may not easily cede market share to cloud providers, AWS’s substantial contracts demonstrate how AI is boosting the cloud sector. Regardless of which companies ultimately benefit most from AI integration in daily life and work, cloud providers are capturing significant value.
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Snowflake, a leading cloud data platform, has entered into a $6 billion, five-year partnership with Amazon Web Services, confirmed on Wednesday.
While Snowflake has historically operated on AWS, it now supports Microsoft Azure and Google Cloud as well. To contextualize the scale of this agreement, Snowflake has generated $7 billion in total revenue through the AWS Marketplace since its 2012 founding, meaning this new contract nearly equals its entire historical earnings from that channel.
Snowflake attributes this surge to accelerating customer spending on AWS, which doubled in 2025 to reach $2 billion for the year alone.
AI adoption is the primary driver of this expansion. Snowflake has offered its Cortex AI development tools for several years, leveraging the fact that enterprises store vast amounts of data within Snowflake. These tools enable natural language database queries, automated reporting, and other capabilities.
Significantly, this agreement secures Snowflake greater access to AWS’s proprietary ARM-based Graviton processors.
As AI transitions from model training to operational automation and agent-based workflows, CPU demand increases sharply. While GPUs manage training and complex reasoning, CPUs handle the majority of other AI-related tasks, especially those involving agents.
Last month, AWS CEO Andy Jassy highlighted that Amazon’s custom AI chips deliver superior price-performance compared to Nvidia’s products, despite AWS continuing to utilize Nvidia hardware. High demand for AI processing has led cloud providers like AWS to deploy chips rapidly. Additionally, most major AI model developers have optimized their applications specifically for Nvidia’s architecture.
Nevertheless, Amazon’s custom chips offer a cost-effective alternative for deployment. As a price-focused organization, Amazon passes these savings on to its clients.
These cost advantages are attracting multi-billion-dollar contracts. For example, AWS recently agreed to supply millions of Graviton chips to Meta to support its expanding AI infrastructure, a significant victory after Meta had previously committed $10 billion to Google Cloud.
Furthermore, these agreements signal to Nvidia that cloud providers are developing competitive CPUs to capture market share. Google has produced custom AI chips for years, and Microsoft introduced its Maia AI processor in January.
Nvidia CEO Jensen Huang recently stated he is prepared to defend and expand his market position. The company’s new AI-specific CPU, Vera, represents a “brand new” $200 billion market opportunity, following another record-breaking quarter. Huang noted that $20 billion in sales has already been secured.
Although Nvidia may not easily cede market share to cloud providers, AWS’s substantial contracts demonstrate how AI is boosting the cloud sector. Regardless of which companies ultimately benefit most from AI integration in daily life and work, cloud providers are capturing significant value.
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