Snowflake’s purchase of services and technology from Amazon Web Services will occur over five years, according to a press release about the agreement. Snowflake intends to expand its use of Amazon’s Graviton general-purpose chips, as well as cloud-based graphics processing units for AI.<\/p>\n
It’s the latest sign that AWS is gaining momentum in AI as clients turn to the market-leading cloud for more advanced technologies. In April, Claude creator Anthropic said it aims to spend over $100 billion<\/a> on AWS over a decade. Amazon also has a deal with OpenAI<\/a>.<\/p>\n Both of its agreements with the AI model companies include an equity investment, while the Snowflake deal does not. Snowflake, which went public<\/a> in 2020, has a market cap of just over $60 billion, and has long relied on AWS. <\/p>\n Snowflake shares jumped as much as 36% in extended trading after it announced strong results<\/a> for its fiscal first quarter, which ended on April 30. The company reported 39 cents in adjusted earnings per share on $1.39 billion in revenue, up 33% year over year. Analysts polled by LSEG had expected 32 cents per share and $1.32 billion in revenue. <\/p>\n Guidance was also strong, as Snowflake called for a 12.5% fiscal second-quarter adjusted operating margin on $1.415 billion to $1.420 billion in product revenue. Analysts surveyed by StreetAccount had been looking for a 11.9% margin, with $1.37 billion in product revenue.<\/p>\n Snowflake also said it was acquiring AI startup Natoma<\/a> for an undisclosed sum.<\/p>\n At the time of Snowflake’s IPO, it disclosed an amended deal with an unnamed cloud provider for $1.2 billion in spending over five years, with $350 million coming in the final year. The provider was Amazon, a Snowflake spokesperson told CNBC. In 2023, the agreement climbed to $2.5 billion.<\/p>\n The new $6 billion arrangement implies an average annual spend of $1.2 billion.<\/p>\n AWS announced its first Arm-based Graviton<\/a> chip in 2018, and it remains the company’s most successful custom chip to date. Snowflake first talked about adopting Graviton in 2022. <\/p>\n Snowflake also has close ties to Nvidia<\/a>, following a partnership announced<\/a> in 2023. In November, Snowflake touted updates<\/a> to simplify the process of running AI workloads on Nvidia GPUs.<\/p>\n The deal marks yet another large tech company choosing custom Arm<\/a>-based processors over those based on traditional x86 architecture.<\/p>\n For decades, server chips have been built on the x86 instruction sets pioneered by Intel<\/a> in the 1970s and Advanced Micro Devices<\/a> a couple of decades later. Arm’s alternative power-efficient architecture went mainstream when Apple<\/a> adopted it for the first iPhone in 2007. But it was Amazon that brought Arm chips into data centers with Graviton. Cloud rivals Google<\/a> and Microsoft<\/a> followed Amazon in bringing out custom Arm chips.<\/p>\n Fast-forward to 2026, and central processing units like Graviton are seeing a renewed flurry of demand as mass AI adoption shifts from call-and-answer chatbots to task-oriented agentic apps.<\/p>\n While GPUs like Nvidia<\/a>‘s excel at training AI models because they have thousands of tiny cores narrowly focused on performing many operations simultaneously, CPUs have a smaller number of powerful cores running sequential general-purpose tasks. Agentic AI requires a lot of general compute power to move large amounts of data around for AI workflows, orchestrating across multiple agents.<\/p>\n In April, Meta<\/a> said it would draw on hundreds of thousands<\/a> of Graviton chips. <\/p>\n “Graviton is our industry-leading CPU chip, which allows Meta to run the CPU-intensive workloads behind Agentic AI with the performance and efficiency they need at their scale,” Amazon CEO Andy Jassy said on the company’s April earnings call<\/a>. <\/p>\n WATCH: How Amazon chips are powering Anthropic without Nvidia<\/a><\/p>\n
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