Snowflake commits $6 billion to AWS

The multi-year Snowflake AWS collaboration covers product integrations, workload migrations, customer support and go-to-market activity through AWS Marketplace

Snowflake has committed $6 billion to AWS over five years as part of a wider agreement focused on enterprise AI workloads.

The multi-year Snowflake AWS collaboration covers product integrations, workload migrations, customer support and go-to-market activity through AWS Marketplace.

The agreement is aimed at helping customers build AI applications on governed enterprise data, reducing the need to move sensitive information between systems.

Sridhar Ramaswamy, CEO of Snowflake, said: We are moving into the era of the agentic enterprise, where AI systems don’t just answer questions, but help organisations 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, he said.

Snowflake will expand its use of AWS infrastructure, including Graviton compute and AI services, as demand grows for data and AI workloads.

Snowflake’s Cortex AI allows customers to build AI applications within its environment, including tools for text-to-SQL, summarisation, sentiment analysis and entity extraction.

Snowflake also uses AWS Graviton processors and GPU-accelerated Amazon EC2 instances for AI model training and inference.

The company has surpassed $7 billion in lifetime AWS Marketplace sales. It recorded more than $2 billion in calendar year sales in 2025, more than doubling transaction growth year-on-year.

Snowflake has also expanded its AWS footprint into regions including New Zealand, South Africa, Thailand and the AWS European Sovereign Cloud to support data residency requirements.

Matt Garman, CEO of AWS, said: 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.