Enterprise data platforms move to the center of AI infrastructure
Enterprise data platforms are integrated systems that store, organize, govern, and serve structured and unstructured business data so that analytics, automation, and AI applications can run reliably at scale. As generative AI moves from pilots to production, these platforms are turning into the core of AI infrastructure spending, sitting between cloud data warehouses and the AI models they feed. Boards increasingly see that model performance depends less on headline parameters and more on data quality, lineage, and accessibility across applications. This is shifting budgets toward tools that can prepare and deliver clean, well-governed data for AI agents and copilots. The result is an intense competitive race among vendors that were once seen as databases or analytics engines but are now repositioning as full-stack AI data platforms.
ClickHouse: High-speed analytics rides the AI agents wave
ClickHouse has emerged as one of the fastest-growing players in enterprise data platforms, crossing USD 250 million (approx. RM1,150 million) in annualised revenue run rate after tripling year-over-year. Its open-source, columnar database is designed for high-speed analytics on massive datasets, which makes it a natural fit for AI agents that need to query recent events, logs, and user interactions in real time. Co-founder and president Yury Izrailevsky expects revenue to reach the high nine digits by year end, and the company is openly preparing for an IPO by hiring former Snowflake investor relations leader Jimmy Sexton as CFO. ClickHouse’s strategy blends managed cloud services with targeted acquisitions, including Langfuse, a tool for tracking and evaluating AI agent performance. That positions ClickHouse not only as a database, but as part of the operational stack enterprises use to monitor and refine AI systems.
MongoDB: Document database turns AI demand into durable growth
MongoDB shows how a general-purpose document database can become a foundation for AI-era applications. In its first quarter of fiscal 2027, the company reported total revenue of USD 687.6 million (approx. RM3,165 million), up 25% year-over-year, driven mainly by subscription revenue of USD 666.1 million (approx. RM3,067 million). CJ Desai, President and CEO, said MongoDB’s teams are “capitaliz[ing] on strong end-market demand for the MongoDB platform across enterprise use cases and emerging AI opportunities.” The company also moved from a prior net loss to a GAAP net income of USD 4.4 million (approx. RM20 million) while generating USD 201.6 million (approx. RM928 million) in cash from operations, showing that AI-related workloads can bring both growth and improving profitability. With remaining performance obligations rising 88%, MongoDB’s Atlas cloud services are becoming a critical data layer for AI-enabled applications that mix transactional workloads with flexible schema and semi-structured content.
Databricks and Snowflake: Valuations signal a data-first AI era
Databricks’ latest funding round crystallizes how much value investors assign to enterprise AI data platforms. The company raised more than USD 7 billion (approx. RM32,200 million), including USD 5 billion (approx. RM23,000 million) in equity, at a USD 134 billion (approx. RM616 billion) valuation and reported a USD 5.4 billion (approx. RM24,800 million) annual revenue run rate with more than 65% year-over-year fourth-quarter growth. This puts Databricks in a direct contest with Snowflake, whose IPO at a USD 70 billion (approx. RM322 billion) valuation was built on its strength in structured data and cloud data warehouses. Databricks evolved from its Apache Spark roots into a “Data Lakehouse” that unifies structured and unstructured data, while Snowflake is expanding beyond its core analytics base toward AI use cases. Their competition centers on who can best prepare, govern, and operationalize data for analytics, automation, and agentic AI across finance, supply chain, HR, and customer operations.
The new AI bottleneck: data quality, governance, and access
Across ClickHouse, MongoDB, Databricks, and Snowflake, a common theme is emerging: enterprises are prioritizing data platforms as mission-critical AI infrastructure. Budgets that once targeted isolated machine learning projects or standalone cloud data warehouses are shifting toward platforms that can handle multi-modal data, enforce governance, and serve low-latency workloads to AI agents. Companies are discovering that the main bottleneck for AI adoption is not model access, but data readiness: cleaning historical records, unifying structured ERP data with documents and logs, and maintaining consistent security and compliance. This is driving rising data platform valuations and influencing long-term vendor lock-in decisions. The winners in this battleground will likely be those that offer flexible architectures, strong governance features, and deep integrations with AI tooling, allowing enterprises to turn their existing data into reliable fuel for copilots, automation workflows, and domain-specific models.
