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ClickHouse is the open-source, column-oriented OLAP database used by Cloudflare, Uber, eBay and Microsoft to query billions of rows per second. Apache 2.0 licensed, with an optional fully-managed cloud.
ClickHouse is the open-source, column-oriented OLAP database that powers real-time analytics for Cloudflare, Uber, eBay, Microsoft, GitLab, Lyft and thousands of other companies. We rate it 92/100 — the fastest analytical database you can run yourself, and the obvious default for log analytics, observability and product-analytics workloads at any meaningful scale.
ClickHouse is a column-oriented OLAP DBMS that started inside Yandex in , originally built by Alexey Milovidov to power Yandex.Metrica's web-scale analytics. It was open-sourced under Apache 2.0 in , and the independent company ClickHouse Inc. spun out of Yandex in with a $50M Series A and a $250M Series B at a $2B valuation a month later, both led by Index Ventures and Coatue. The repo at github.com/ClickHouse/ClickHouse now sits at roughly 45,000+ stars with more than 1,500 contributors.
The pitch is simple: traditional OLTP databases like Postgres and MySQL store data row-by-row, which is fine for transactions but catastrophic for aggregations over billions of rows. ClickHouse stores data in compressed columns, vectorizes its query execution and parallelizes across cores and shards. The result is queries that are routinely 100×–1000× faster than equivalent Postgres or MySQL queries, with linear scaling on commodity hardware. Cloudflare, for example, uses a 36-node ClickHouse cluster to ingest non-aggregated request logs at over 6 million requests per second.
Sentiment in the data engineering community is overwhelmingly positive. The most upvoted Hacker News thread on ClickHouse calls it "unreasonably fast", and a long-running r/dataengineering discussion concludes that "once you've used ClickHouse for log analytics it's hard to go back to anything else." On G2, ClickHouse Cloud holds a strong 4.6/5 rating with reviewers consistently calling out query speed, low TCO and the team's responsiveness on GitHub issues. Cloudflare, Uber, GitLab, Instacart, Lyft, eBay and Contentsquare have all published engineering posts crediting ClickHouse with handling workloads that previously required Vertica, Snowflake or Druid.
The recurring complaints fall into three buckets. First, ClickHouse is analytics-only — it is not a replacement for OLTP and you should not point your transactional app at it. Second, the operational learning curve is real: ReplicatedMergeTree, sharding keys, partition design and merge tuning all matter, and the docs assume you understand the storage engine. Third, on the cloud side, several reviewers note that the Basic tier's idle scale-down can introduce cold-start latency on first query and that the consumption-based pricing is hard to forecast for spiky workloads.
The ClickHouse open-source server is free forever under Apache 2.0. ClickHouse Cloud, the fully-managed offering, uses consumption-based pricing across three service tiers. New accounts get $300 in credits and a 30-day trial.
| Plan | Compute | Storage | Best For |
|---|---|---|---|
| Open Source | Free (your hardware) | Free | Self-hosted on bare metal, k8s or VMs — Apache 2.0 |
| Cloud Basic | From $0.2181 / unit-hour | $25.30 / TB-month | Single replica dev/test, scale-to-zero |
| Cloud Scale | From $0.2986 / unit-hour | $25.30 / TB-month | Production HA workloads, 3+ replicas |
| Cloud Enterprise | From $0.3903 / unit-hour | $25.30 / TB-month | Single-tenant, custom cloud, SOC 2 / HIPAA / PCI |
Best for: Engineering teams running observability, log analytics, ad-tech, real-time product analytics, IoT or any time-series workload at billions-of-rows scale who need sub-second queries on commodity hardware. Especially strong as a Druid, Pinot, Vertica, BigQuery or Snowflake replacement for read-heavy analytical workloads.
Not ideal for: OLTP workloads (point lookups, single-row updates, multi-row transactions) — keep using Postgres or MySQL for that. Also overkill for sub-100GB analytical datasets, where DuckDB or even SQLite will be simpler.
Pros:
Cons:
The closest competitors are Apache Druid and Apache Pinot, both purpose-built for real-time analytics but with steeper operational complexity; Snowflake and Google BigQuery, which are easier to operate but multiples more expensive at high scale; DuckDB, an excellent embedded analytical engine for sub-100GB workloads; and StarRocks, a newer ClickHouse-style entrant. ClickHouse is the most mature and broadly deployed of the open-source column-store options.
Yes — for the workloads it was designed for, ClickHouse is the obvious choice. If you are doing log analytics, observability, ad-tech, product analytics, IoT or any other read-heavy aggregation over hundreds of millions of rows or more, ClickHouse will be faster, cheaper and more flexible than almost anything else in the market, especially against managed warehouses like Snowflake or BigQuery. Skip it only if you are in OLTP territory or your dataset is small enough that DuckDB or Postgres is enough. We rate it 92/100.
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