Tech giants spend $9.25B on database deals as AI battle shifts to infrastructure

Major tech companies are racing to acquire database companies, with Snowflake’s $250 million purchase of Crunchy Data, Databricks’ $1 billion Neon acquisition, and Salesforce’s $8 billion Informatica deal all happening within weeks of each other. This shift signals that the AI infrastructure battle is moving from flashy large language models to the foundational database layer, where companies need AI-ready data served fast, resiliently, and at scale.

The big picture: The AI race has evolved beyond building sophisticated models to controlling the data infrastructure that powers intelligent applications, as companies realize that databases are now the front line of enterprise AI rather than just the back-end.

Why databases matter for AI: AI tools require continuous access to both structured and unstructured data at machine speed to produce human-like results.

• As one analyst noted, “AI is stupid without access to good data” — and not just data access, but data orchestration at machine speed.
• PostgreSQL, an open-source database system, has become popular for AI modernization because it’s reliable and trusted in enterprise settings, but it faces architectural limits with real-time AI workloads.
• Spencer Kimball, CEO of Cockroach Labs, explained that “AI agents, copilots and real-time pipelines generate nonstop reads and writes. They require globally consistent data in milliseconds and expect systems to absorb failure without flinching.”

Snowflake’s strategy: The company is repositioning itself as a “workflow-native” data platform connecting corporate data to AI-driven decisions.

• CEO Sridhar Ramaswamy emphasized this shift at the company’s 20,000-attendee summit, introducing tools like Openflow to unify fragmented data sources into real-time workflows.
• Snowflake Intelligence layers generative AI on enterprise data, letting non-technical employees query company information using natural language without SQL or engineering support.
• The company’s Cortex AI framework orchestrates data at scale and automates operations in live environments.

The enterprise reality: Most organizations remain stuck in AI proof-of-concept cycles due to infrastructure readiness gaps.

• Vivek Raghunathan, Snowflake’s senior VP of engineering, noted that “everyone’s experimenting. But very few are scaling responsibly.”
• According to Fivetran’s report on AI and data readiness, 42% of enterprises report that over half of their AI projects have been delayed, underperformed, or failed due to poor data readiness.
• Jeff Hollan, head of Cortex AI apps and agents, calls this “the next frontier,” noting that “the next generation of apps aren’t just data-hungry, but also reasoning-hungry.”

What they’re saying: Industry leaders emphasize that infrastructure readiness is critical for AI success.

• “Without clear vision and readiness at the infrastructure level, no amount of AI investment will deliver sustained value,” Raghunathan explained.
• Artin Avanes, head of core data platform at Snowflake, told the author: “While faster access to data is great, that’s not what you really need. You need systems that adapt to how decisions are made in real time.”

The strategic implications: These acquisitions represent land grabs in the AI foundation layer, as vendors no longer want to sit atop the data stack but own it entirely.