ClickHouse, Inc., a pacesetter in real-time analytics, knowledge warehousing, observability, and AI/ML, at this time introduced it has raised $350 million in Collection C financing. The spherical was led by Khosla Ventures, with participation from new buyers BOND, IVP, Battery Ventures, and Bessemer Enterprise Companions, in addition to present buyers together with Index Ventures, Lightspeed, GIC, Benchmark, Coatue, FirstMark, and Nebius. At present’s spherical follows earlier investments of over $300 million, bringing complete funding to over $650 million.
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This mixed funding shall be used to scale product improvement, assist world growth, and deepen partnerships with clients and know-how suppliers constructing the subsequent wave of AI-native functions. Along with this financing, ClickHouse has secured a $100 million credit score facility led by Stifel and Goldman Sachs.
The momentum behind ClickHouse is accelerating: the corporate grew over 300% through the previous yr and now serves over 2,000 clients throughout a spread of industries from fintech and transportation to shopper and healthcare. New clients embrace Anthropic, Tesla, and Argentina’s Mercado Libre, amongst others. They be a part of firms corresponding to Sony, Meta, Memorial Sloan Kettering, Lyft, and Instacart, in addition to AI innovators Sierra, Poolside, Weights & Bases, Langchain, and extra.
At present’s announcement was made at ClickHouse’s inaugural consumer convention, showcasing how enterprises are constructing knowledge merchandise to satisfy the calls for of the agentic period.
“As AI brokers proliferate throughout data-driven functions, observability, knowledge infrastructure, and past, the demand for agent-facing databases like ClickHouse has reached an inflection level. The way forward for analytics isn’t simply dashboards. It’s clever brokers that interpret knowledge, set off workflows, and energy real-time selections,” stated Aaron Katz, CEO of ClickHouse. “However AI is only one driver. We designed and constructed ClickHouse from day one to assist a broad spectrum of real-time knowledge functions throughout industries, and our momentum displays that enterprises are hungry for a platform that may sustain with their scaling ambitions.”
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“We invested in ClickHouse as a result of they’re fixing one of the vital necessary infrastructure challenges of this period of AI and brokers: enabling real-time knowledge platforms that may assist each conventional analytics and the rising calls for of AI-native workloads,” stated Ethan Choi, Accomplice at Khosla Ventures. “As AI reshapes each trade, the flexibility to ship quick, scalable, and cost-efficient analytics is changing into foundational, ClickHouse is poised to turn into the default engine for next-generation clever knowledge merchandise.”
The Subsequent Frontier: Actual-Time Analytics for Agentic Workloads
The market traction round ClickHouse is rooted in a basic shift: enterprises are not simply constructing dashboards or batch experiences—they’re constructing real-time, clever knowledge platforms that should serve each human and AI brokers. Since AI brokers can generate queries a lot sooner and at the next fee than human analysts, agent-facing databases should assist low-latency, interactive analytical queries at an more and more excessive throughput.
ClickHouse was designed from the bottom as much as meet this demand. Its high-performance, columnar storage engine allows interactive, analytical queries throughout huge datasets with minimal latency—good for powering AI and ML functions, real-time analytics, cloud knowledge warehousing, and observability workloads.
Constructed for Velocity at Scale: Excessive-Efficiency Analytics Throughout Industries
Conventional databases and warehouses are struggling to maintain up with this demand. Transactional databases don’t scale for analytical workloads. In the meantime, conventional knowledge warehouses are optimized for inside, batch-heavy use circumstances with restricted concurrency and sluggish efficiency. Lastly, search-oriented applied sciences turn into prohibitively costly for structured analytics—utilizing 10x extra in storage and compute and limiting the sensible vary of functions attributable to value.
In contrast, ClickHouse gives a purpose-built answer that bridges the hole—combining high-performance analytics with the scalability and concurrency that at this time’s clever, data-driven functions require.
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