Snowflake Commits $6 Billion to AWS, Shares Jump 35% on Q1 Earnings Beat
Updated
Updated · CNBC · May 27
Snowflake Commits $6 Billion to AWS, Shares Jump 35% on Q1 Earnings Beat
6 articles · Updated · CNBC · May 27
Snowflake shares surged as much as 35% after hours after the data cloud company posted fiscal first-quarter adjusted EPS of 39 cents on $1.39 billion in revenue, topping analyst estimates of 32 cents and $1.32 billion.
The results landed alongside a new five-year commitment to spend $6 billion on Amazon Web Services, expanding Snowflake's use of AWS Graviton processors and cloud GPUs for AI workloads.
Snowflake also forecast a 12.5% adjusted operating margin and $1.415 billion to $1.420 billion in second-quarter product revenue, ahead of Wall Street expectations, and said it would acquire AI startup Natoma.
The AWS pact sharply expands Snowflake's prior cloud commitments—from $1.2 billion disclosed at its IPO to $2.5 billion in 2023—and implies about $1.2 billion in annual spending.
For Amazon, the deal adds to a broader AI push as customers increasingly adopt custom Arm-based chips for agentic AI, following major AWS commitments from Anthropic and growing Graviton demand from Meta.
Can Snowflake’s $6B bet on AWS infrastructure save it from the looming 'SaaSpocalypse' threat posed by AI agents?
With Arm now a direct rival, can AWS Graviton maintain its price-performance edge in the AI data center wars?
Snowflake and AWS Ink $6 Billion AI Cloud Pact: Strategic Impacts, Customer Economics, and the Future of Enterprise Data
Overview
On May 27, 2026, Snowflake and Amazon Web Services (AWS) announced a landmark five-year, $6 billion strategic collaboration. This deal marks Snowflake’s largest infrastructure investment and is designed to accelerate enterprise adoption of agentic and generative AI. The partnership aims to solve core challenges businesses face when integrating advanced AI by directly connecting foundation models with governed enterprise data. By bringing AI models to where the data resides, rather than moving sensitive information between systems, the collaboration reduces complexity and risk, making AI more secure, powerful, and easier for enterprises to adopt.