ClickHouse
ClickHouse helps enterprises query massive analytical datasets in real-time with sub-second latency.
ClickHouse is an open-source columnar database management system optimized for real-time online analytical processing (OLAP), enabling complex SQL queries at massive scale with sub-second latency. It achieves hundreds of millions of rows per second throughput—100x faster than competing systems—through its vectorized Merge Tree storage engine and native support for distributed, replicated architectures. The platform serves enterprises handling massive analytical workloads, from event ingestion to interactive dashboards, and is used by companies like Microsoft, Walmart, Tesla, and eBay to process billions of events per minute.
Problem solved
Data teams need to generate interactive analytical reports and real-time dashboards on massive datasets, but traditional OLAP databases sacrifice query speed for scale or require expensive hardware.
Target customer
Enterprise data teams and analytics engineers at high-scale companies processing millions of events per minute; particularly suited for companies with real-time analytics, event streaming, and observability use cases.
Founders
A
Alexey Milovidov
Co-Founder & CTO
Yandex engineer who conceived ClickHouse while building real-time web analytics systems; committed first lines of code in 2009.
A
Aaron Katz
Co-Founder & CEO
Former Chief Revenue Officer at Elastic, where he scaled the company from $15M to $500M+ in revenue; previously consulted at PeopleSoft and interned at Sun Microsystems.
Y
Yury Izrailevsky
Co-Founder & President
Joined after testing the open-source ClickHouse and being impressed by its speed and usability; brought operational expertise to commercialize the platform.
Funding history
Series A
$50M
September 2021
Led by Index Ventures
· Benchmark Capital
Series B
$250M
October 2021
Led by Coatue Management
· Altimeter Capital, Lightspeed, Redpoint Ventures
Series C
$350M
May 2025
Led by Khosla Ventures
· BOND, IVP, Battery Ventures, Bessemer Venture Partners
Series D
$400M
January 2026
Led by Dragoneer Investment Group
· Previous investors
Total raised:
$1.05B
Pricing
ClickHouse Cloud uses consumption-based pricing: compute billed at $0.22–$0.39 per unit-hour (in 8 GB RAM increments, per-minute billing), storage at $25.30 per TB-month, plus charges for data egress and ClickPipes. The open-source version is free under Apache 2.0 license.
Notable customers
Microsoft, Walmart, Cisco, Sony, Lyft, Uber, Comcast, eBay, Anthropic, Tesla, Mercado Libre, Meta, Memorial Sloan Kettering, Instacart, Property Finder
Integrations
AWS, Apache ecosystem, Salesforce, Google Workspace, Algolia, Segment
Tech stack
Zone.js (JavaScript frameworks)
Angular (JavaScript frameworks)
Highlight.js (JavaScript libraries)
Algolia (Search engines)
Module Federation
HTTP/3
Google Analytics (Analytics)
HSTS (Security)
Google Font API (Font scripts)
TypeScript (Programming languages)
Google Workspace (Email)
cdnjs (CDN)
Cloudflare (CDN)
Google Tag Manager (Tag managers)
Salesforce (CRM)
Amazon Web Services (PaaS)
OneTrust (Cookie compliance)
Segment (Customer data platform)
Website
Competitors
Apache Pinot
Focuses on ultra-low latency queries under 20ms with built-in upsert support; less suited for complex analytical joins.
StarRocks
Newer entrant with vectorized engine similar to ClickHouse; more recent adoption but less production battle-tested at scale.
Apache Druid
Offers serverless autoscaling and simplified operations; better for monitoring/observability use cases, lower throughput for complex analytics.
Amazon Redshift
Cloud-native data warehouse but slower query speeds and higher costs for real-time analytics workloads; better for data warehousing than OLAP.
Snowflake
General-purpose cloud data warehouse; significantly more expensive for interactive analytics and real-time query workloads than ClickHouse Cloud.
Why this matters: ClickHouse has achieved remarkable scale adoption (Microsoft, Walmart, Tesla, eBay) and valuation growth ($15B in Jan 2026, less than a year after $6.35B) by solving a critical problem: enterprises need real-time interactive analytics at billion-row scale, but existing solutions are 100x slower or 6x more expensive. The combination of open-source credibility, hyperscale production usage, and aggressive cloud pricing positions it as a category leader in analytical databases.
Best for: Enterprises needing real-time interactive analytics on massive event streams and operational metrics; teams building observability, analytics, and business intelligence platforms requiring sub-second query latency.
Use cases
Real-time Event Analytics
ClickHouse ingests billions of events per minute (as eBay does) and enables analysts to query event streams with sub-second latency, making it ideal for real-time dashboards, alerting systems, and operational monitoring. Companies can explore patterns in massive datasets interactively without pre-aggregation.
Interactive Business Intelligence & Reporting
Property Finder reduced website query time by 5x and cut operational costs by 50% by migrating analytics to ClickHouse. Teams can build interactive dashboards that respond instantly to filter changes and support high concurrency without expensive hardware.
Observability & Log Analysis
Engineering teams use ClickHouse to ingest and analyze logs, traces, and metrics at scale. Lyft processes tens of millions of rows and millions of read queries daily, enabling fast debugging and performance monitoring without separate log management infrastructure.
High-Concurrency Time Series Analytics
Companies building monitoring, APM, or analytics products embed ClickHouse to support hundreds of concurrent users querying massive time series datasets. The columnar storage and vectorized execution handle complex aggregations and joins faster than traditional databases.
Alternatives
Apache Pinot
Choose Pinot if you need ultra-low latency (sub-20ms) queries with built-in upsert support for streaming updates; ClickHouse excels at complex joins and aggregations.
Snowflake
Choose Snowflake for general-purpose data warehousing and easier setup; ClickHouse is significantly cheaper and faster for real-time interactive analytics.
Amazon Redshift
Choose Redshift if you need tight AWS ecosystem integration; ClickHouse offers faster query speeds and lower costs for OLAP and real-time analytics workloads.
StarRocks
Choose StarRocks for newer infrastructure with modern cloud-native defaults; ClickHouse has longer production history at hyperscale and broader ecosystem.
FAQ
What does ClickHouse do? +
ClickHouse is a columnar database optimized for online analytical processing (OLAP). It processes hundreds of millions of rows per second and enables analysts to run complex SQL queries interactively on massive datasets with sub-second latency, making it ideal for real-time analytics, observability, and business intelligence dashboards.
How much does ClickHouse cost? +
ClickHouse Cloud pricing is consumption-based: compute costs $0.22–$0.39 per unit-hour (billed per minute, in 8 GB increments), storage $25.30 per TB-month, plus egress charges. The open-source version is completely free under Apache 2.0 license.
What are the main alternatives to ClickHouse? +
Apache Pinot (for ultra-low latency queries), StarRocks (newer vectorized engine), Snowflake and Amazon Redshift (general-purpose cloud data warehouses), Apache Druid (for observability). ClickHouse differentiates through speed (100x faster than competitors at release), lower cost, and superior support for complex analytical joins.
Who uses ClickHouse? +
Enterprise data teams and analytics engineers at companies like Microsoft, Walmart, Tesla, eBay, Lyft, Meta, Uber, and Anthropic. It's particularly popular among companies processing massive event streams or building real-time analytics platforms, observability tools, and business intelligence systems.
How does ClickHouse compare to Snowflake? +
ClickHouse is 5–10x faster for interactive analytical queries and significantly cheaper (one customer paid 6x more on Snowflake's standard plan). Snowflake offers easier setup and broader data warehouse features; ClickHouse wins on real-time analytics performance and cost efficiency for event-driven workloads.
Is ClickHouse suitable for my use case? +
ClickHouse is ideal if you need sub-second interactive analytics on datasets with billions+ of rows, real-time event ingestion and querying, observability/monitoring platforms, or high-concurrency analytical workloads. It's less suited for transactional databases or slowly-changing dimensional data.
Tags
analytical database
OLAP
columnar storage
real-time analytics
observability
event streaming
data warehouse
high-performance SQL
open-source
infrastructure