From Experiments to Agentic Enterprise AI Infrastructure
Enterprise AI infrastructure is the combined stack of cloud hardware, data platforms, and orchestration tools that lets companies run AI agents securely on production data at scale, turning isolated experiments into always-on business systems that can reason, decide, and act across workflows. After years of pilots and chatbots, large providers are now committing long-term capital to this layer. Snowflake has signed a multi-year strategic collaboration agreement with AWS and committed USD 6 billion (approx. RM27.6 billion) in Graviton compute and AI spend over five years to support enterprise agentic AI adoption. Oracle, meanwhile, has signed USD 67 billion (approx. RM308.2 billion) in AI infrastructure contracts in a single quarter, signaling that demand for cloud AI contracts is matching, and in some cases outpacing, the build-out of new capacity. The race is shifting from experimental models to dependable agentic deployment.
Snowflake and AWS: Bringing Agents to the Data Warehouse
Snowflake’s expanded AWS collaboration centers on a simple idea: bring AI to where governed data already lives. Through Snowflake Cortex AI, customers can run text-to-SQL, summarization, sentiment analysis, and entity extraction directly inside their data warehouse, cutting the risk of moving sensitive data into external AI tools. The USD 6 billion (approx. RM27.6 billion) commitment to AWS Graviton compute and GPU-accelerated EC2 instances is aimed at powering these enterprise AI workloads and agentic AI adoption over the next five years. According to Snowflake, AWS Marketplace sales have surpassed USD 7 billion (approx. RM32.2 billion) in lifetime volume with more than USD 2 billion (approx. RM9.2 billion) in 2025 alone, showing that customers prefer integrated procurement of data and AI. This tight AI data warehouse integration is emerging as a core competitive moat as enterprises move from copilots to autonomous agents.
Oracle’s USD 67 Billion Bet on Agentic AI Infrastructure
Oracle’s latest quarter shows how fast enterprise AI infrastructure demand is growing. The company reported USD 67 billion (approx. RM308.2 billion) in AI infrastructure contracts signed in Q4, supported by data center capacity that is being absorbed almost as soon as it comes online. Total revenue reached USD 19.2 billion (approx. RM88.3 billion) for the quarter, with cloud infrastructure revenue up 93% and remaining performance obligations at USD 638 billion (approx. RM2.93 trillion), up 363% year over year. Co-CEO Mike Sicilia said that “customers have moved past the experiment stage with AI” and are now ready for “enterprise-grade, complete agentic solutions.” Oracle reports delivering more than 1,000 AI agents across its application suites, alongside outcome-based pricing models and token bundles for advanced reasoning models, suggesting that agentic workloads are starting to reshape both infrastructure planning and software pricing.
Why Data Access, Not Models, Is Becoming the Moat
As cloud AI contracts grow, competitive advantage is shifting away from models and toward data access and AI data warehouse integration. Models can be swapped or fine-tuned, but governed, high-quality data pipelines are harder to copy. Snowflake’s expansion across new AWS regions is designed to keep AI close to operational data while meeting residency needs, so agentic systems can act on real transactions and events rather than static exports. Oracle’s 97.5% global GPU utilization shows that demand rises when AI is wired into core business data and processes, such as agentic coding tools that change how development teams work. In this environment, platforms that can connect AI agents directly to transactional, analytical, and event streams—while preserving governance—are best placed to turn agentic AI adoption into measurable revenue and efficiency gains, instead of isolated proof-of-concept wins.
Democratizing Agentic Analytics with Natural-Language-to-SQL
While hyperscalers focus on massive capital outlays, a new layer of tools is making enterprise AI infrastructure usable by non-specialists. Companies such as Mora convert plain English questions into verified SQL against existing warehouses, letting business teams ask, “What drove last quarter’s churn?” and get governed, query-backed answers without writing code. This reduces the skills barrier that has kept AI pilots confined to data science teams and speeds the move from copilots to agents that can generate, check, and execute analytics workflows end-to-end. Combined with platforms like Snowflake Cortex AI and Oracle’s agentic application suites, these natural-language interfaces help connect everyday decision-making to the same production data and cloud AI contracts that underpin large-scale agent workloads. The result is an AI stack where human operators and autonomous agents can share the same trusted data foundation, rather than operating in disconnected tools.
