Defining the Agentic Enterprise and the New Snowflake AWS Pact
The agentic enterprise describes a model where AI systems are embedded into core business platforms so they can reason over governed data, coordinate workflows, and take autonomous actions that drive measurable business outcomes at scale. Snowflake’s new multi-year strategic collaboration agreement with AWS is framed squarely around this vision, with the company committing USD 6 billion (approx. RM27.6 billion) in Graviton compute and AI spend over five years to run its AI data cloud on AWS. This deepened Snowflake AWS partnership goes beyond traditional cloud hosting: it standardizes AWS as the default engine for Snowflake’s agentic AI infrastructure, from data warehousing to foundation model inference. For enterprises, the deal signals that AI agents will no longer sit on the edge of data systems. Instead, they will be designed into cloud data platforms from the start, with AWS positioned as the primary execution layer.
Bringing Enterprise AI Agents to Where Data Lives
Snowflake and AWS are aligning their architectures so enterprise AI agents operate directly where governed data resides. Rather than copying sensitive information into external AI stacks, Snowflake Cortex AI brings text-to-SQL, summarization, sentiment analysis, and entity extraction into the same Snowflake environment that already enforces data governance. The stack combines Snowflake’s AI data cloud with AWS Graviton processors and GPU-accelerated Amazon EC2 instances for model training and inference, giving enterprises a single environment for both analytics and autonomous agents. According to Snowflake’s CEO Sridhar Ramaswamy, the goal is to move from “answering questions” to coordinating workflows and actions. In practice, that means AI agents can read transactional data, trigger operational tasks, and feed back outcomes without leaving the platform. This integrated approach to agentic AI infrastructure reduces data movement risk while making it easier to operationalize intelligent agents in production.
From AI Experiments to Production: Why the $6B Bet Matters
The USD 6 billion (approx. RM27.6 billion) commitment is not just a bulk compute purchase; it is a signal that enterprise AI agents are moving from proofs of concept to core workloads. Snowflake has already surpassed USD 7 billion (approx. RM32.2 billion) in lifetime AWS Marketplace sales, including more than USD 2 billion (approx. RM9.2 billion) in calendar year 2025, showing that customers are buying AI-ready cloud data platforms at scale. The expanded agreement doubles down on that demand with shared investments in workload migrations, customer success programs, and sector solutions that help enterprises standardize agentic AI on AWS. By betting on AWS Graviton as its default compute layer, Snowflake is also aligning performance and cost economics with the needs of always-on AI agents. The message to enterprises is clear: to compete, AI must be embedded into the infrastructure where data and decisions already live.
Customer Use Cases: AI Agents on Governed Data
Real-world deployments show how agent-based AI is starting to reshape day-to-day work on cloud data platforms. Fetch, for example, uses Snowflake Cortex AI on AWS to power a semantic agent that lets sales teams query campaign data in natural language and receive instant insights. Instead of waiting for analysts, teams interact with an enterprise AI agent that speaks the language of the business while staying inside governed datasets. Analytics company Hex describes Snowflake on AWS as the “foundation” for customers using Hex to explore, analyze, and build with AI, stressing that security and governance are what make enterprise AI adoption credible. These use cases illustrate the broader shift from dashboards to agents: rather than passively viewing metrics, employees collaborate with AI agents that understand schemas, enforce governance, and trigger next best actions, all within a unified agentic AI infrastructure.
Competitive Positioning in Cloud Data and Agentic AI
By tying its largest infrastructure commitment to AWS, Snowflake is clarifying its competitive stance in the cloud data and AI platform race. The company is betting that enterprises will prefer agentic AI embedded in cloud data platforms they already trust, rather than separate AI layers layered on top of data warehouses. AWS gains a strategic partner that drives high-value AI workloads and reinforces Graviton and EC2 as the default fabric for enterprise AI agents. For rivals, the move raises the bar: competing platforms must match not only compute performance but also the depth of integration between data governance, AI agents, and marketplace distribution. As Snowflake expands its regional presence on AWS and scales joint go-to-market efforts, the Snowflake AWS partnership positions both companies as a default choice for organizations standardizing on agentic AI infrastructure in the cloud.
