Snowflake and Amazon Web Services have turned a decade-long partnership into a financial pact that reshapes the enterprise AI landscape. The snowflake aws deal, valued at US$6 billion over five years, signals a clear move from experimental AI projects toward production-grade, governed intelligence.
This agreement is not a routine renewal. It aligns the data platform giant with the world’s largest cloud provider at a moment when enterprises are desperate to deploy AI without compromising security or performance. Both companies have committed real capital and technical resources to make that happen.
Why did Snowflake raise its forecast?
The financial results behind the announcement explain the confidence. Snowflake reported first-quarter revenue of US$1.39 billion, well above the US$1.32 billion analysts expected. Product revenue reached US$1.33 billion, representing 33.9% year-over-year growth.
Based on that momentum, Snowflake raised its fiscal 2027 product revenue forecast to US$5.84 billion. The earlier estimate stood at US$5.66 billion. The company also projected second-quarter product revenue between US$1.415 billion and US$1.420 billion, again exceeding analyst estimates of US$1.37 billion.
CEO Sridhar Ramaswamy explained on the post-earnings call that the revised outlook reflected strength in the core data platform business alongside a measurable contribution from AI capabilities. The core data warehousing business remains the primary engine, but AI workloads are starting to drive incremental spending.
That said, the numbers reveal something deeper. Enterprises are not simply experimenting with AI. They are making procurement decisions that favor integrated platforms. Snowflake is capturing that demand by offering a complete environment where data management and AI processing coexist.
What does the $6 billion AWS deal include?
The scope of this agreement extends far beyond standard cloud commitments. At the infrastructure level, Snowflake will use AWS Graviton processors for general cloud workloads. These Amazon-designed chips offer improved price-performance for data-intensive tasks.
For AI-specific work, the deal includes GPU-accelerated Amazon EC2 instances. These instances handle model training and inference, which are the most compute-heavy stages of the AI lifecycle. By securing dedicated access to these resources, Snowflake ensures that its customers can scale AI workloads without competing for scarce GPU capacity on the open market.
Generative and agentic AI integrations
The snowflake aws deal also covers deeper product integrations for both generative AI and agentic AI. Generative AI refers to systems that create content, code, or designs. Agentic AI goes a step further and refers to systems that can autonomously perform tasks, make decisions, and interact with other systems based on goals.
For a Snowflake customer, agentic AI might mean an automated process that monitors data pipelines, detects anomalies in real time, and triggers corrective actions without human intervention. These integrations allow developers to build such capabilities using familiar SQL and Python tools within Snowflake, while AWS provides the underlying compute power.
Expanded AWS Marketplace activity
The agreement also expands activity through AWS Marketplace, where enterprise customers discover, buy, and deploy cloud software. Snowflake reported that its AWS Marketplace sales have surpassed US$7 billion over time. In calendar 2025 alone, sales exceeded US$2 billion, more than doubling year over year.
In addition, this channel is becoming a primary route for joint customers to procure Snowflake services. The simplified purchasing process reduces friction and accelerates time-to-value for organizations deploying AI workloads.
How does the deal help enterprises move AI to production?
One of the most persistent barriers to enterprise AI adoption is data gravity and governance. Moving large volumes of sensitive data to a separate AI platform introduces latency, cost, and compliance risk. The snowflake aws deal directly addresses this problem.
The architecture focuses on a simple principle: bring AI models to the governed data, not the other way around. Snowflake’s Cortex AI tools are central to this strategy. These tools support text-to-SQL, summarisation, sentiment analysis, and entity extraction directly within the Snowflake environment.
Consider a financial institution processing petabytes of transaction data. Without this integration, running a sentiment analysis on customer communications would require exporting data to a separate machine learning environment. That process introduces governance gaps and regulatory exposure. With Cortex AI running on AWS infrastructure, the institution can perform the same analysis in place. The data never leaves the governed Snowflake environment.
Snowpark and Cortex Code
Snowflake also offers tools such as Cortex Code and Snowpark, which support development, data processing, and machine learning use cases. These tools allow data engineers and data scientists to build pipelines and models using their preferred programming languages and frameworks, all while staying within Snowflake’s security boundary.
As a result, enterprises can move AI projects from proof-of-concept to production faster. The operational overhead of data movement diminishes, and the risk of data exposure drops significantly. This “data-in-place” processing model is a major selling point for regulated industries such as healthcare, finance, and government.
What new regions is Snowflake expanding into?
Global expansion is a central component of the broader strategy. Snowflake is expanding its AWS footprint across 10 new regions. The list includes Auckland, Cape Town, and Bangkok, along with the AWS European Sovereign Cloud.
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Data residency requirements are tightening worldwide. Countries are enacting laws that mandate citizen data be stored and processed within national borders. By deploying infrastructure in these specific locations, Snowflake enables local enterprises and public sector organizations to comply with sovereignty regulations.
The AWS European Sovereign Cloud warrants particular attention. This dedicated infrastructure region is designed for customers in the European Union who require the highest levels of data protection and operational autonomy. For Snowflake, being available on this sovereign cloud is critical for winning business from regulated European industries, including banking, healthcare, and government administration.
Moreover, launching in growth markets like Bangkok and Cape Town opens new revenue opportunities. These regions are experiencing rapid digital transformation, and enterprises there need cloud platforms that can deliver advanced analytics and AI capabilities without compromising local compliance.
What did analysts say about the agreement?
Industry analysts responded positively to the strategic implications of the deal. Gil Luria of D.A. Davidson said the snowflake aws deal aligns the company more closely with its largest partner. Alignment with AWS is important because AWS accounts for a substantial portion of Snowflake’s customer base.
Luria also emphasized that the agreement positions Snowflake for a larger role in customers’ transition to AI. This is not simply about selling more data warehouse capacity. It represents an expansion into the orchestration layer for enterprise AI workloads running on AWS.
The market reaction was swift and clear. Snowflake’s shares rose 36% in extended trading following the earnings report and deal announcement. Investors interpreted the deal as a derisking event for Snowflake’s AI strategy.
On the other hand, some observers noted that the agreement represents a multi-year infrastructure commitment that deepens Snowflake’s dependence on a single cloud provider. However, the preferential access to scarce GPU resources and the co-investment from AWS likely offset that concern for most customers and shareholders.
Frequently Asked Questions
How does the snowflake aws deal affect pricing for existing Snowflake customers on AWS?
The agreement is structured as an infrastructure commitment rather than a direct pricing change for individual customers. However, the scale of the deal means Snowflake secures better pricing on compute and GPU resources from AWS. Over time, these savings could help Snowflake maintain competitive pricing or invest more aggressively in product development, which indirectly benefits end users.
What specific AI workloads does Snowflake expect to enable with this AWS infrastructure?
Snowflake is targeting workloads that combine governed enterprise data with generative and agentic AI. Examples include real-time fraud detection using sentiment analysis, automated report generation using text-to-SQL, and intelligent data pipeline management using agentic AI systems. The AWS GPU instances will handle model training and inference, while Cortex AI tools provide the application-layer capabilities.
How does this deal impact Snowflake’s relationships with other cloud providers like Microsoft Azure and Google Cloud?
The US$6 billion commitment to AWS deepens Snowflake’s integration with that specific platform, but Snowflake continues to operate as a multi-cloud company. Customers on Azure and Google Cloud still have access to Snowflake’s core platform. However, the most advanced AI integrations and the best compute pricing are likely to appear first on AWS as a result of this agreement. Other cloud providers may need to offer similar commitments to maintain parity.
The snowflake aws deal represents a milestone for enterprise AI infrastructure. It moves beyond abstract strategy and commits substantial capital to solving the practical challenges of deploying AI at scale. By merging data governance, compute power, and global reach, this partnership offers a clear blueprint for organizations ready to build production AI workloads.
