How much does ClickHouse Cloud cost?

Pricing is based on usage with separate storage and compute costs. The Basic plan starts at $25.30 per 1TB of storage per month and $0.2181 per compute unit per hour. There's a free 30-day trial with $300 in credits.

Can I use ClickHouse for transactional (OLTP) workloads?

No. ClickHouse is optimized for analytical (OLAP) workloads. It doesn't support high-frequency point updates or complex transactions. For mixed workloads, consider using it alongside an OLTP database like PostgreSQL.

How does ClickHouse achieve such fast query performance?

Key techniques include vectorized query execution, columnar storage, data compression, sparse indexing, and efficient use of all available CPU cores. Queries are executed in a massively parallel way across data shards.

Is ClickHouse Cloud a managed version of the open-source database?

Yes. ClickHouse Cloud provides a fully managed, serverless version with features like automatic scaling, backups, and multi-cloud deployment (AWS, GCP, Azure). The core engine is the same open-source project.

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ClickHouse

ClickHouse is an open-source OLAP database management system designed for real-time analytical queries on large datasets. It provides column-oriented storage, vectorized query execution, and advanced compression to deliver millisecond query responses on billions of rows. Ideal for high-performance analytics, data warehousing, observability, and machine learning workloads. It offers both a self-managed open-source version and ClickHouse Cloud, a fully managed service on AWS, GCP, and Azure with features like automatic scaling, backups, and enterprise support. ClickHouse is trusted by companies like Spotify, Uber, and eBay for mission-critical, data-intensive applications.

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Sep 22, 2026

Column-oriented OLAPReal-time analyticsVectorized executionPetabyte scaleSQL interfaceOpen sourceCloud & self-hosted

Product information

Everything worth knowing about ClickHouse

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Real-time Analytics

Analyze billions of rows in real time with millisecond results for instant insights and dashboards.

Column-Oriented OLAP Database

Columnar storage architecture provides at least 100x faster query performance for analytical workloads compared to row-oriented databases.

Deploy Anywhere

Run on your machine, in ClickHouse Cloud (AWS, GCP, Azure), or via Bring Your Own Cloud deployment.

Observability Stack (ClickStack)

Store and query logs, metrics, and traces at scale using the open-source ClickStack stack powered by ClickHouse.

Agentic Data Stack for AI

Power AI applications with blazing-fast vector search, instant aggregations, and scalable training data.

Technical capabilities

Compute-Compute Separation

Isolate compute resources for different workloads, a feature available in Scale and Enterprise tiers.

Vectorized Query Execution

Maximizes CPU efficiency by processing data in batches, a core optimization for analytical speed.

Must watch videos

Building AI for Financial Analysis: How ClickHouse Powers Real-Time Intelligence

16:38

Tutorial16:38

Building AI for Financial Analysis: How ClickHouse Powers Real-Time Intelligence

Explore how StockGro, a leading social investing and financial education platform, leverages ClickHouse to power real-time analytics and AI-driven financial insights. In this talk, Shreyas Patil, Founding Engineer at StockGro, shares how ClickHouse’s speed and scalability enable smarter, data-backed decision-making at scale. Session recording from Open House roadshow Bangalore 2025. https://clickhouse.com/openhouse/bangalore

The ClickHouse Platform for AI: Build and run agents on your data

18:40

Tutorial18:40

The ClickHouse Platform for AI: Build and run agents on your data

The ClickHouse Platform for AI is an open, end-to-end stack that gives agents the real-time analytics they demand through MCP, the CLI, and ClickHouse Agents powered by Claude. See how to use and build agents on ClickHouse in minutes, on the data you already have. by: Pete Hampton

0:47

Tutorial0:47

AI is redrawing the database market

Scale versus speed. Volume versus cost. Simplicity versus flexibility. For years, those were the trade-offs you had to pick between, but in the AI era, they've all become table stakes. https://clickhouse.com/blog/ai-redrawing-database-market This video looks at why AI agents are changing the game for data systems and what a database needs to keep up. 🔸 How agent workloads differ from human ones 🔸 Why every row counts when AI is doing the querying 🔸 Real-time response as a hard requirement 🔸 Concurrency at the scale of thousands of parallel agents 🔸 Why ClickHouse was built for exactly this

Who uses it

Real use cases, no hype

Real-time Analytics

Power dashboards and interactive analysis on live, high-volume data streams with sub-second query latency.

Observability Stack

Centralize logs, metrics, and traces for efficient querying and long-term retention at a fraction of the cost.

Data Warehousing

Offload heavy analytical workloads from your transactional database for faster insights and reporting.

Machine Learning & GenAI

Accelerate feature engineering, vector search, and model training data preparation for AI applications.

Ad Tech & Marketing Analytics

Process billions of events for real-time bidding, attribution, and user behavior analysis.

IoT & Telemetry Analysis

Ingest and analyze massive volumes of time-series data from sensors and devices.

Security & Fraud Detection

Query security logs in real-time to identify threats, anomalies, and fraudulent patterns.

What's great

Blazing fast OLAP queries on petabyte-scale data.
Best-in-class data compression ratios reduce storage costs.
Developer-friendly SQL interface for complex analytics.
Proven at massive scale by major tech companies.
Strong open-source foundation with 50k+ GitHub stars.

Technical strengths

Vectorized query execution maximizes CPU efficiency.
Column-oriented storage engine optimized for OLAP workloads.
Native support for materialized views for incremental aggregation.
Support for Approximate Query Processing (AQP) functions.

Where it falls short

Not ideal for traditional OLTP workloads or frequent updates.
Self-hosted deployment requires significant operational expertise.
Cloud pricing can become complex with multiple billing dimensions.
Limited support for complex transactions and joins.

Technical limitations

Steeper learning curve for advanced features like projection tuning.
Eventual consistency model requires careful data modeling.
Schema evolution can be less flexible than row-based databases.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

ClickHouse is a column-oriented OLAP database designed for extreme query speed on large datasets. Unlike row-oriented databases, it's at least 100x faster for analytical queries by reading only relevant columns and using advanced compression.