ClickHouse
Open-source columnar OLAP database for millisecond analytics on petabyte-scale data.
If your analytical queries are crawling and your data is past a few hundred gigabytes, ClickHouse is the most direct open-source fix available. The catch is operational: someone on your team has to understand schemas, merges and cluster sizing, and major releases still ship backward-incompatible changes — 26.9 arrives on the heels of 26.8 LTS defaults shifts. Teams without that person should price ClickHouse Cloud (Basic from $53/mo, Scale from $437/mo, billed monthly on pay-as-you-use compute and storage) before committing to self-hosting. Compare DuckDB for sub-100GB work and Snowflake or BigQuery when you would rather trade control for less operational ownership.
Verified 6d ago · liveness 83/100 · cite: rightaichoice.com/tools/clickhouse
- Data engineers building real-time analytics dashboards on billions of rows
- DevOps and SRE teams running observability for logs, metrics and traces at scale
- ML engineers who need fast vector search and aggregation for AI applications
- Companies replacing or offloading expensive data warehouse workloads
- Transactional workloads — point lookups and updates belong in a row-store like Postgres
- Teams without SQL depth or anyone to own schema design and cluster tuning
- Analytics under roughly 100GB where DuckDB or Postgres gets you there faster
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip ClickHouse if your workload is transactional, your total data is under roughly 100GB, or nobody on the team wants to own schema design, merges and cluster sizing — DuckDB or Postgres will reach value faster at that scale.
ClickHouse Cloud bills compute separately from storage — $0.2181 per unit-hour on Basic, $0.2985 on Scale, $0.3903 on Enterprise — so wide scans and always-on clusters hit your invoice faster than the headline 'from
Self-managed open source costs $0 in license fees but you pay in compute, storage and engineering time. ClickHouse Cloud Basic from $53/month (metered monthly) suits prototypes; Scale from $437/month is the realistic production floor; Enterprise from $571/month buys SAML SSO, private regions and CMEK. Against Snowflake and BigQuery the pitch is price-performance and control; against DuckDB and Postgres it is scale, at the cost of simplicity. Bring Your Own Cloud is priced via sales discussion.
In short
ClickHouse — Open-source columnar OLAP database for millisecond analytics on petabyte-scale data. Best for Data engineers building real-time analytics dashboards on billions of rows, DevOps and SRE teams running observability for logs, metrics and traces at scale, ML engineers who need fast vector search and aggregation for AI applications. Free to start; paid plans from $53/mo.
What's new in ClickHouse
Checked 6 days agoAcross the latest 9 updates: 2 feature updates, 1 launch, 1 changelog entry, 1 community discussion and 4 news mentions.
ClickHouse release 26.9
ClickHouse 26.9 adds conditional LIMIT boundaries, incremental refreshes for append-only materialized views, disk spilling for DISTINCT, time-limited tokens, and faster min/max/count queries.
How Fountain rebuilt its data plane on ClickHouse Cloud to power Cue
Fountain rebuilt its data plane on ClickHouse Cloud to power Cue, its frontline superintelligence product.
ClickHouse appoints Mike Scarpelli, former Snowflake and ServiceNow CFO, to Board of Directors
ClickHouse adds former Snowflake and ServiceNow CFO Mike Scarpelli to its board of directors.
Postgres on NVMe: performance and the convergence of transactions and analytics
Engineering post examines Postgres performance on NVMe and the convergence of transactional and analytical workloads.
Postgres week in the Netherlands: PGDay Lowlands & Percona Live 2026
ClickHouse community recap of Postgres week in the Netherlands covering PGDay Lowlands and Percona Live 2026.
September 2026 newsletter
ClickHouse September 2026 newsletter roundup of community and product updates.
The official ClickHouse provider for Apache Airflow is now available
Official ClickHouse provider for Apache Airflow now available, enabling orchestrated ClickHouse workflows.
What's new in ClickStack - Aug '26
ClickStack August 2026 release notes covering new observability capabilities.
ClickHouse is now available on the dbt platform
ClickHouse is now available on the dbt platform, adding managed dbt workflows for ClickHouse users.
What people actually say about ClickHouse — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
47 mentions across 4 sources (Hacker News, App Store, GitHub, Lemmy) · researched Jul 3, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Blazing fast query performance even on petabyte-scale datasets.
- +Columnar storage with high compression ratios saves on storage costs.
- +Open-source (Apache 2.0) with active community and frequent releases.
- +Excellent for real-time dashboards and observability backends.
- +Distributed architecture supports horizontal scaling easily.
- −Steep learning curve for teams new to columnar databases.
- −SQL dialect differs from standard SQL, causing migration pains.
- −Self-managed deployment requires significant operational expertise.
- −Not suitable for transactional (OLTP) workloads.
- −High number of open issues (6,165) raises reliability concerns.
- • Self-managed version requires engineering time for setup and tuning
- • Cloud costs can add up for high query volumes due to compute charges
Viability Score
How well maintained and how widely used is ClickHouse? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Columnar storage with advanced compression
- Vectorized query execution for millisecond analytics
- Distributed query processing across clusters
- Standard SQL with array, nested and time-series extensions
- Built-in vector search for ML and GenAI workloads
- ClickPipes for managed data ingestion from external sources
- ClickStack open-source observability for logs, metrics, traces and session replays
- Managed ClickStack hosted observability with long-term retention
- ClickHouse Managed Postgres (public beta) for transactions plus analytics
- Langfuse LLM observability, evaluation and prompt management
- ClickHouse Local for querying CSV, TSV and Parquet without a server
- chDB in-process SQL engine with a Pandas-compatible API
- Terraform provider support including ClickStack
- Role-based access control and data masking
- Refreshable materialized views with incremental refreshes for append-only sources
About ClickHouse
ClickHouse is an open-source, column-oriented OLAP database built for real-time analytics over very large datasets. Columnar storage plus vectorized query execution return millisecond results across billions of rows, and aggressive compression keeps storage bills down. It fits data engineers, DevOps/SRE teams, and ML engineers who already work in SQL and need analytical queries to stay fast as data grows. You can run it three ways: self-managed open source, ClickHouse Cloud on AWS, GCP or Azure with compute and storage metered separately and scaled to zero when idle, or ClickHouse Local for querying CSV, TSV and Parquet files without a server. Querying stays standard SQL with extensions for arrays, nested data, and time-series functions. The product line has widened past the core database: ClickHouse Managed Postgres is in public beta for teams wanting transactions and analytics in one stack, ClickStack is an open-source observability stack for logs, metrics, traces and session replays, Managed ClickStack is its hosted counterpart, and Langfuse — now part of ClickHouse — covers LLM observability, evaluation and prompt management. Release 26.9 added conditional LIMIT boundaries, incremental refreshes for append-only materialized views, disk spilling for DISTINCT, time-limited tokens and faster min/max/count queries.
Behind the Verdict
ClickHouse's whole edge is architectural: store data by column, compress it hard, execute queries vectorized, and you get order-of-magnitude wins on the aggregation-heavy scans that dashboards, observability and feature pipelines live on. Sony, Lyft, Cisco, GitLab and Tesla are named as production users, and Anthropic says ClickHouse 'played an instrumental role in helping us develop and ship Claude 4' — signal that the engine holds up under real, continuous load rather than only in benchmarks. Strengths: real-time performance at billions of rows; genuinely simple SQL surface for the common cases; compression that lowers storage spend; and an unusually broad deployment story — self-managed OSS, ClickHouse Cloud on AWS/GCP/Azure with compute that scales to zero, Bring Your Own Cloud inside your own VPC, and ClickHouse Local for serverless queries over CSV, TSV and Parquet. The ecosystem around it has thickened: ClickPipes for managed ingestion, a Terraform provider that now covers ClickStack, an official Apache Airflow provider, and availability on the dbt platform. The expanded line — ClickHouse Managed Postgres in public beta, ClickStack, Managed ClickStack, and Langfuse for LLM observability and eval — means a single vendor can now cover transactions, analytics and AI telemetry, though each of those pieces is younger than the core engine. Weaknesses are honest and specific. This is an analytics engine, not an OLTP store; point lookups and updates belong in a row-store. Schema design, tuning and cluster operations still assume engineering depth, so teams without SQL or DBA capacity will feel it. Backward-incompatible changes land on major releases — 26.8 LTS altered JSON number parsing and max_insert_threads defaults — so upgrades need real testing. And cloud cost is usage-shaped: compute bills by the unit-hour ($0.2181 on Basic, $0.2985 on Scale, $0.3903 on Enterprise) plus storage at $25.30 per TB/mo, with public-internet egress from $0.1152/GB, so high-volume query patterns need autoscaling limits set deliberately. If your dataset is under roughly 100GB or you want point-and-click querying, DuckDB, Postgres or a managed warehouse will get you to value with less friction.
Researching ClickHouse? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas ClickHouse actually fits — and what changes day-one when you adopt it.
Point ClickPipes at the Kafka topic carrying product events, land raw rows in a MergeTree table, and build refreshable materialized views for hourly and daily rollups feeding a Grafana dashboard.
Outcome: Dashboards that scanned billions of rows in minutes now return in milliseconds without changing the team's SQL habits.
Deploy ClickStack on ClickHouse to store logs, metrics, traces and session replays in one engine, replacing a separate log store plus metrics backend.
Outcome: One query layer across telemetry types, with compression keeping retention costs far below the previous stack.
Use ClickHouse's built-in vector search and chDB's Pandas-compatible API to build embeddings, run low-latency similarity queries for an agent, and route LLM traces into Langfuse.
Outcome: Retrieval and evaluation share one database instead of a separate vector store plus a separate observability tool.
Use Cases
- Build real-time dashboards querying billions of rows in milliseconds.
- Ingest logs, metrics and traces for observability at scale, as Shopify does.
- Power recommendation engines and AI agents with low-latency vector search.
- Replace or offload expensive cloud warehouse workloads while keeping query speed.
- Stream and transform high-velocity event data with refreshable materialized views.
- Run ML feature pipelines and hyperparameter tuning on historical data.
- Monitor and evaluate LLM applications with Langfuse.
- Enable billion-row live streaming analytics, as Sony LIV does with ClickHouse Cloud.
Models Under the Hood
as of 2026-09-23
Limitations
- ClickHouse is an analytics engine, not a general-purpose transactional database, so point lookups and updates belong elsewhere.
- Schema design, tuning and cluster operations assume engineering depth and someone who owns them.
- Major releases carry backward-incompatible changes — 26.8 LTS changed JSON number parsing and max_insert_threads defaults — so test upgrades before rolling them out.
- On ClickHouse Cloud, compute is metered by the unit-hour and storage per TB, with egress billed separately, so high-volume query patterns need autoscaling limits set deliberately or bills drift.
- Several product lines beyond the core engine — Managed Postgres, Managed ClickStack — are still public beta.
- Some advanced features remain experimental.
as of 2026-10-02
Verification history
We have re-verified ClickHouse 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published ClickHouse tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0
Ideal for
Engineers who want the full database with no license fee and are willing to own servers, storage and upgrades themselves.
What this tier adds
Starting tier — $0 license, self-managed; you pay only your own compute and storage.
ClickHouse Cloud Basic
From $53/mo (metered monthly)
ClickHouse Cloud Scale
From $437/mo (metered monthly)
ClickHouse Cloud Enterprise
From $571/mo (metered monthly)
Bring Your Own Cloud
Custom
Ideal for
Regulated or data-residency-bound organizations that need ClickHouse Cloud's managed operations inside their own AWS, GCP or Azure VPC.
What this tier adds
Runs the managed service in your own cloud account and bills through your provider; priced via sales discussion.
Where the pricing makes sense
The company stage and team size where ClickHouse's pricing actually pencils out — and where peers do it cheaper.
Self-managed open source costs $0 in license fees but you pay in compute, storage and engineering time. ClickHouse Cloud Basic from $53/month (metered monthly) suits prototypes; Scale from $437/month is the realistic production floor; Enterprise from $571/month buys SAML SSO, private regions and CMEK. Against Snowflake and BigQuery the pitch is price-performance and control; against DuckDB and Postgres it is scale, at the cost of simplicity. Bring Your Own Cloud is priced via sales discussion.
Setup time & first value
How long it actually takes to get something useful out of ClickHouse — broken out by persona, not the marketing-page minute.
Self-managed: an afternoon to a first query if you already know Linux and SQL, days to weeks before the schema and cluster are tuned for production. ClickHouse Cloud: minutes to a running service via the free trial, though realistic performance tuning still takes days. ClickHouse Local: under five minutes for a local Parquet or CSV file. ClickStack and Managed Postgres take longer and depend on
Switching to or from ClickHouse
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From PostgreSQL: use the native Postgres integration and ClickPipes to replicate tables, then rewrite aggregate queries as materialized views for analytical scans.
- →From Snowflake or BigQuery: offload the heavy reporting and dashboard queries to ClickHouse first, keeping the warehouse for the rest.
- →From Apache Druid or Elasticsearch: move event and log pipelines to ClickHouse tables and point Grafana or Metabase at the same dashboards.
- →From CSV, TSV or Parquet files: load directly with ClickHouse Local before standing up a server.
- →From a self-managed ClickHouse cluster: migrate to ClickHouse Cloud or Bring Your Own Cloud for managed operations without rewriting SQL.
- ↗To DuckDB: for sub-100GB analytical work, export to Parquet and query in-process with no server to run.
- ↗To PostgreSQL: for transactional workloads, move point lookups and updates to a row-store and keep ClickHouse for analytics.
- ↗To Snowflake or BigQuery: if you would rather trade control and price-performance for a fully managed warehouse with a point-and-click interface.
Integrations
Resources & Guides
- Documentationclickhouse.com
Docs · ClickHouse
Full product docs from clickhouse.com
- Documentationclickhouse.com
Llms · ClickHouse
Full product docs from clickhouse.com
- Learnclickhouse.com
Learn · ClickHouse
Educational content from clickhouse.com
- Resourceclickhouse.com
Videos · ClickHouse
Helpful link from clickhouse.com
- Resourceclickhouse.com
Demos · ClickHouse
Helpful link from clickhouse.com
- Resourceclickhouse.com
Benchmark · ClickHouse
Helpful link from clickhouse.com
Tutorials & Learning
YouTube returned 6 videos for “ClickHouse”, and we withheld 6: 6 could not be judged, because “ClickHouse” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about ClickHouse.
Official links
Tools that pair well with ClickHouse
Common stack mates teams adopt alongside ClickHouse, with the specific reason each pairing earns its keep.
Doris
Apache Doris: an open-source real-time SQL database that unifies OLAP analytics, full-text search, and vector search in one engine.
Tidb
Open-source distributed SQL database unifying transactions, HTAP analytics, and native vector search for AI agents.
Chat2DB
Chat2DB is an open-source AI SQL client that turns plain English into queries across 40+ database engines, with query execution kept on your machine.
Featured Head-to-Head Comparisons
Clickhouse vs Spider Cloud
Choose ClickHouse if you need a blazing-fast, petabyte-scale analytics database for real-time dashboards or observability; choose Spider Cloud if you need to reliably scrape the web and feed structured data into AI agents or RAG pipelines. They solve completely different problems—one is a database, the other a data ingestion tool—so pick based on your data architecture need.
Clickhouse vs Temporal Ai
Think of ClickHouse as a turbocharged analytics engine for petabyte-scale log and event data — it excels at fast SQL queries. Temporal AI is your safety net for long-running, failure-prone workflows: it guarantees that your AI agents or microservices pick up exactly where they left off. Pick ClickHouse if you need to query massive datasets in real time; choose Temporal if you need reliability and state management across distributed steps.
Clickhouse vs Screenplayiq
ClickHouse and ScreenplayIQ serve completely different domains. Choose ClickHouse if you need a high-performance analytics database for real-time OLAP on massive datasets. Choose ScreenplayIQ if you are a film professional seeking AI-driven script feedback and box office predictions. No overlap in use cases.
Alternatives to ClickHouse
View all
Frequently Asked Questions
Best-of guides
Topics
Used ClickHouse? Help shape our editorial sentiment research.