ClickHouse is making a decisive move to cement its role as foundational infrastructure for the AI era. The real-time analytics and data warehousing company has closed a $400 million Series D round, one of the largest recent financings in modern data infrastructure, signaling strong investor conviction that performance-driven data platforms will sit at the center of production AI systems.

The round was led by Dragoneer Investment Group, with participation from a heavyweight roster that includes Bessemer Venture Partners, GIC, Index Ventures, Khosla Ventures, Lightspeed Venture Partners, T. Rowe Price–advised accounts, and WCM Investment Management. The scale and composition of the investor group underscore a broader belief: as AI moves from experimentation into production, data infrastructure—not models—becomes the bottleneck.

Growth fueled by production-grade workloads

ClickHouse’s funding arrives on the back of rapid, sustained growth. The company now serves more than 3,000 customers on ClickHouse Cloud, its fully managed service, with annual recurring revenue growing more than 250% year over year. Over the past quarter alone, organizations such as Capital One, Polymarket, Airwallex, Lovable, and Decagon have either adopted ClickHouse or expanded existing deployments.

These customers join a base that already includes data- and AI-intensive brands like Meta, Tesla, Sony, and Cursor. Notably, ClickHouse isn’t just replacing legacy analytics systems—it’s enabling new, real-time use cases that were previously impractical due to cost or latency constraints.

Unlike many data platforms that primarily serve internal BI teams, ClickHouse is frequently embedded directly into customer-facing products. That distinction matters. In always-on systems—fraud detection, real-time personalization, observability, AI-driven decisioning—performance and reliability are not nice-to-haves; they define the end-user experience.

“ClickHouse was built to deliver exceptional performance and cost efficiency for the most demanding data workloads, and this momentum validates that strategy,” said CEO Aaron Katz. “As we look toward the future, we’re expanding into unified transactional and analytical workloads and adding LLM observability, so developers can build and run AI applications on the best possible technical foundation.”

Why Dragoneer is leaning in

Dragoneer is known for its selective, research-heavy approach and long-term partnerships with category-defining companies. Founded by Marc Stad in 2012, the firm has backed several of the most influential data and infrastructure platforms of the past decade, as well as foundational AI companies.

For Dragoneer, ClickHouse stood out because it sits closest to production—a critical advantage as AI systems scale. AI-driven applications generate far higher query volumes, demand tighter latency, and require continuous evaluation of outputs. As models become more capable, the performance burden shifts decisively to the data layer.

“Major platform shifts ultimately reward the infrastructure companies that sit closest to production,” said Christian Jensen, Partner at Dragoneer. “As models improve, data infrastructure becomes the bottleneck. ClickHouse delivers the performance, efficiency, and reliability required for AI systems operating at scale.”

That assessment reflects a broader industry trend. As enterprises move beyond pilots and proofs of concept, they are prioritizing platforms that can support always-on, data-intensive workloads without spiraling costs.

Enter LLM observability, via Langfuse

One of the most consequential moves tied to the funding is ClickHouse’s acquisition of Langfuse, an open-source LLM observability platform. While traditional observability focuses on system health and performance metrics, LLM observability tackles a newer, more complex problem: evaluating the quality, safety, and behavior of non-deterministic AI outputs in production.

As generative AI becomes embedded in workflows—from customer support to financial analysis—the ability to understand why a model produced a given output is rapidly becoming table stakes. Langfuse has emerged as a leading project in this space, ending 2025 with more than 20,000 GitHub stars and over 26 million SDK installs per month.

“We built Langfuse on ClickHouse because LLM observability is fundamentally a data problem,” said Marc Klingen, CEO of Langfuse. “Together, we can deliver faster ingestion, deeper evaluation, and a much shorter path from a production issue to a measurable improvement.”

The acquisition positions ClickHouse to offer a differentiated observability stack—one that spans traditional analytics, system observability, and now AI behavior monitoring. For teams deploying LLMs in production, that convergence could significantly reduce operational risk.

A unified data stack: Postgres meets ClickHouse

ClickHouse is also pushing into another strategic frontier: unifying transactional and analytical workloads. The company announced a native, enterprise-grade Postgres service deeply integrated with ClickHouse, aimed squarely at modern AI applications that need both real-time transactions and high-speed analytics.

The service includes scalable Postgres backed by NVMe storage, native change data capture (CDC), and tight synchronization with ClickHouse—enabling up to 100x faster analytics on transactional data. A unified query layer, powered by a native Postgres extension, allows developers to build applications that span transactions and analytics without managing separate systems.

The Postgres service is built in partnership with Ubicloud, an open-source cloud company led by veterans from Citus Data, Heroku, and Microsoft.

“Postgres and ClickHouse naturally complement each other for AI applications,” said Umur Cubukcu, Co-CEO and Co-Founder of Ubicloud. “Together, we’re removing complexity and delivering a production-grade stack where transactions and analytics work as one.”

This move reflects a growing industry push toward simplification. As AI applications proliferate, teams are increasingly wary of stitching together fragmented data stacks that add latency, cost, and operational risk.

Global expansion and ecosystem momentum

Alongside product expansion and acquisitions, ClickHouse continues to grow its global footprint. Over the past year, the company entered the Japanese market through a partnership with Japan Cloud and deepened its relationship with Microsoft Azure, including work around OneLake.

ClickHouse has also invested heavily in community and ecosystem development, hosting user events across San Francisco, New York, Amsterdam, Sydney, and Bangalore. These events have attracted more than 1,000 attendees and featured speakers from OpenAI, Tesla, Capital One, Ramp, and Canva—signaling broad adoption across industries.

On the product side, ClickHouse has expanded support for modern data lake formats, including Apache Iceberg and Delta Lake, and strengthened compatibility with widely used data catalogs. Full-text search capabilities have been enhanced to support observability and AI monitoring use cases, while lightweight updates have been introduced to meet the demands of AI-driven applications.

According to recent benchmarks, ClickHouse continues to outperform leading cloud data warehouses on price-performance—a critical differentiator as AI workloads drive data volumes sharply higher.

The bigger picture

ClickHouse’s $400 million raise is about more than scale—it’s about positioning. As AI applications become mainstream, the winners won’t just be model providers. They’ll be the infrastructure platforms that quietly, reliably power production systems at scale.

By combining real-time analytics, a unified transactional-analytical stack, and LLM observability under one roof, ClickHouse is betting that the future of AI runs on data platforms built for speed, efficiency, and continuous evaluation. For enterprises moving AI from prototype to production, that bet may prove well-timed.

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