Enterprise Data Platforms Become the New AI Front Line
Enterprise data platforms are the integrated systems that collect, store, govern, and analyze corporate data so that AI models, analytics tools, and operational applications can use it reliably at scale. As enterprise AI spending grows, competition is shifting away from headline-grabbing models and toward the platforms that make those models useful. Databricks and ClickHouse display how AI infrastructure investment is consolidating around a few strategic layers: unified data storage, real-time analytics, and agentic automation. Instead of treating data warehouses or lakehouses as back-office utilities, enterprises now view them as the core of AI strategy, where structured ERP data meets documents, logs, events, and application telemetry. This puts data platform competition on the same strategic level as model selection, with CIOs increasingly asking not only which model to adopt, but which data foundation will let those models operate across the whole business.
Databricks’ $134B Signal: Data Readiness as AI Infrastructure
Databricks’ recent funding round of more than USD 7 billion (approx. RM32.2 billion), including USD 5 billion (approx. RM23 billion) in equity financing at a USD 134 billion (approx. RM615.8 billion) valuation, is a clear vote of confidence in data platforms as core AI infrastructure. According to an Inc. profile, this came just months after a USD 1 billion (approx. RM4.6 billion) raise at a valuation above USD 100 billion (approx. RM459.9 billion). The company’s annual revenue run rate reached USD 5.4 billion (approx. RM24.8 billion), with fourth-quarter revenue up more than 65% year over year, an acceleration Theory Ventures’ Tomasz Tunguz called “exceptional” for a business at this scale. Databricks’ Data Lakehouse aims to unify structured and unstructured data, positioning it as the system where AI-ready data is prepared, governed, and fed into enterprise AI applications, agents, and automation workflows.
ClickHouse’s $250M ARR: Specialized Analytics for AI Workloads
While Databricks races ahead on valuation, ClickHouse is proving that focused analytics engines can win significant enterprise AI spending. The company’s serverless cloud offering has crossed over USD 250 million (approx. RM1.1 billion) in annual run-rate revenue and more than tripled year over year, while its customer base has grown to 4,000. Recent additions such as Capital One, Decagon, and Airwallex join existing users including Anthropic, Meta, Tesla, and Instacart, underlining how widely ClickHouse now sits in the enterprise stack. CEO Aaron Katz notes that “more than 1,000 new customers and a tripling of ARR within months of our Series D tell us this isn’t a cycle, it’s a structural shift in what data infrastructure has to do.” AI-era workloads like high-concurrency analytics, model telemetry, and observability fit ClickHouse’s strengths, turning it into a specialized pillar of enterprise data platforms.
AI Agents Move Inside the Data Platform
Both Databricks and ClickHouse are weaving AI agents directly into their platforms to differentiate in the crowded data platform competition. ClickHouse Agents, a fully managed agentic analytics service powered by Anthropic’s Claude, gives users a no-code way to define and operate agents grounded in ClickHouse data. Out of the box, these agents include a chat interface, sandboxed code interpreter, skills management, memory, and multi-agent workflows, and can connect to MCP-compatible systems and the AWS Agent Registry. Around this, ClickHouse is building AI observability and managed Postgres to unite transactional state with high-throughput analytics. Databricks, for its part, is positioning its Lakehouse as the place where AI agents can access governed, cross-domain data. The message is clear: AI agents are no longer external tools; they are becoming native capabilities of enterprise data platforms themselves.
From Models to Platforms: How Enterprise AI Spending Is Shifting
The rising valuations and revenue milestones of Databricks and ClickHouse show that enterprise AI spending is tilting from raw model access toward the data infrastructure that makes AI useful. While OpenAI, Anthropic, and Google dominate model conversations, Databricks, Snowflake, and ClickHouse compete for the layer where ERP transactions, customer events, documents, and observability data are combined into AI-ready context. For enterprises, this shifts the AI strategy question from “Which model?” to “Which platform will manage my data for AI agents, analytics, and automation?” Data architecture choices now determine how fast teams can deploy AI in finance, supply chain, HR, and customer operations. In this landscape, enterprise data platforms are no longer peripheral tools; they are becoming the central operating layer for AI-era business execution and the primary target of long-term AI infrastructure investment.
