ClickHouse
FreemiumFastest open-source real-time OLAP database for petabyte-scale analytics and AI.
By Tanmay Verma, Founder · Last verified 05 Jul 2026
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Added 5d ago
77/100Safe BetIn short
ClickHouse — Fastest open-source real-time OLAP database for petabyte-scale analytics and AI. Best for Data engineers building real-time analytics dashboards, DevOps teams implementing observability backends (logs, metrics, traces), Machine learning engineers requiring fast vector search and feature stores. Free to start; paid plans from $50/mo.
Is ClickHouse actually worth it?
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Editorial Verdict
Best for
Data engineers building real-time analytics dashboardsDevOps teams implementing observability backends (logs, metrics, traces)Machine learning engineers requiring fast vector search and feature storesEnterprises needing a cost-efficient data warehouse at petabyte scaleSaaS companies handling high-ingestion event streams
Not ideal for
Transactional workloads (OLTP) — not a replacement for PostgreSQL or MySQLTeams without SQL proficiency or database administration skillsSmall-scale analytics on less than 100 GB of data (overkill for many use cases)Users needing a simple no-code query interface — administration requires CLI or client drivers
ClickHouse remains the fastest open-source OLAP database for real-time analytics at scale. Its cloud offering simplifies operations, but the learning curve and operational complexity mean it's overkill for small datasets. Best for teams with data engineering expertise who need sub-second queries on petabyte-scale data. For smaller workloads, consider PostgreSQL or DuckDB.
Skip ClickHouse if Skip ClickHouse if you need row-level transactions or ACID compliance, or if your analytical workload fits comfortably on a single server with under 100 GB of data — simpler tools like PostgreSQL or DuckDB will serve you better.
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Last verified: July 2026
What's new in ClickHouse
Checked 3 days agoAcross the latest 4 updates: 1 feature update, 1 launch, 1 changelog entry and 1 news mention.
FeatureBlog·6 days agoNewest
ClickHouse Release 26.6 adds hypothetical skip indexes, cascading refreshable materialized views, and experimental continuous queries.
New features in 26.6: hypothetical skip indexes for query planning, cascading materialized views, and experimental continuous queries for streaming data.
LaunchBlog·7 days ago
ClickHouse Agents is now available for Managed Postgres, enabling agentic workflows.
ClickHouse Agents launched for Managed Postgres, allowing agentic data operations directly from the database.
NewsBlog·9 days ago
Clever ingests 200x more logs at the same cost with ClickHouse Cloud.
Clever increased log ingestion 200x without cost increase using ClickHouse Cloud, demonstrating efficiency.
ChangelogBlog·10 days ago
Notion Custom Agents can now natively connect to ClickHouse.
ClickHouse becomes a native connection in Notion Custom Agents, enabling SQL queries from Notion.
What independent users actually report about ClickHouse
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).
75% positive25% critical
Recurring strengths
- +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.
Recurring frustrations
- −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.
Patterns worth knowing
Exceptional performance for real-time analytics on large datasets
Seen on Hacker News, Lemmy
Steep learning curve and operational complexity
Seen on Hacker News, Lemmy
Active ecosystem with frequent enhancements and acquisitions
Seen on Lemmy
Learning curve
advancedProductive in ~Days of setup
Hidden costs people mention
- • 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
77/100
Safe BetHow likely is ClickHouse to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Real-time analytical queries on petabytes of data
- Column-oriented storage with high compression ratios
- Distributed query execution across clusters
- Cascading refreshable materialized views (26.6)
- Hypothetical skip indexes for query optimization (26.6)
- Experimental continuous queries for streaming (26.6)
- Built-in vector search for AI/ML workloads
- Separate compute and storage scaling
- Auto-scaling to zero on idle (Cloud)
- Over 100 native integrations via ClickPipes
- Open-source (Apache 2.0) with managed cloud option
- Time-series functions and window functions
- Support for semi-structured data (JSON, arrays)
- Role-based access control and data masking
- LLM observability via Langfuse integration
About ClickHouse
FreemiumAdvancedAPI availableWeb · CLI · API · Desktop
ClickHouse is an open-source, column-oriented OLAP database designed for real-time analytics on massive datasets. Originally developed by Yandex, it now powers data-intensive applications at companies like Uber, Lyft, and Cisco. Its core differentiator is sub-second query latency on petabyte-scale data, achieved via vectorized execution, aggressive compression, and distributed architecture that separates compute from storage. ClickHouse supports standard SQL with extensions for arrays, nested data, and time-series functions. Deployment options include self-managed open-source or ClickHouse Cloud (AWS, GCP, Azure), plus Bring Your Own Cloud (BYOC) for full control. The recent 26.6 release adds hypothetical skip indexes, cascading refreshable materialized views, and experimental continuous queries. Langfuse, acquired by ClickHouse, offers LLM observability for AI applications. The ecosystem includes over 100 native integrations via ClickPipes for ingestion from Kafka, S3, MongoDB, and visualization tools like Grafana and Tableau. ClickHouse is open-source (Apache 2.0) with a cloud pricing model based on separate compute and storage usage, starting at $50/month. While powerful, its columnar design and distributed setup require data engineering expertise, making it ideal for data-savvy teams handling large-scale analytical workloads.
Behind the Verdict
ClickHouse's performance advantage is real: column-oriented storage, vectorized execution, and aggressive compression deliver sub-second queries on billions of rows. The 26.6 release (July 2026) introduces hypothetical skip indexes and cascading materialized views, further optimizing query planning and data freshness. The Langfuse acquisition adds dedicated LLM observability (evaluations, prompt management) for AI applications. On the downside, ClickHouse is not an OLTP database — you cannot do row-level updates efficiently. Its SQL dialect, while standard, has quirks around JOINs and lack of full ACID across shards. The cloud pricing is usage-based, which can surprise users with high query volumes if autoscaling limits aren't set. Self-managed deployment requires significant operational expertise. The recent Notion Custom Agents integration (June 2026) expands its reach into AI workflows, but the core audience remains data engineers and teams handling petabyte-scale workloads, not casual analysts.
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Real-world workflow fit
Concrete scenarios for the personas ClickHouse actually fits — and what changes day-one when you adopt it.
Data engineer at a SaaS company
Ingest 10 TB/day of event data from Kafka via ClickPipes, create cascading materialized views to aggregate user sessions hourly, and query in under 100ms for dashboards.
Outcome: Real-time insights with <100ms latency, 6x compression reducing storage costs, and autoscaling compute that drops to zero overnight.
DevOps engineer at a cybersecurity firm
Ship 100 TB of logs and traces daily from S3 to ClickHouse Cloud, use experimental continuous queries for streaming threat detection, and alert on anomalies in seconds.
Outcome: 69x faster detection queries compared to previous stack, with 200x more logs ingested at the same cost (as achieved by Clever).
ML engineer at a tech company
Store 50 billion embeddings in ClickHouse's built-in vector search, build a recommender system that queries nearest neighbors in under 10ms, and combine with time-series filters for real-time personalization.
Outcome: Sub-10ms vector search with SQL-joined metadata, eliminating the need for a separate vector database.
Use Cases
- Build real-time dashboards that query billions of rows in under a second
- Ingest and analyze logs, metrics, and traces for observability at scale
- Power recommendation engines and agentic AI with low-latency vector search
- Replace expensive cloud warehouses (e.g., BigQuery) to cut costs while maintaining speed
- Stream and transform high-velocity event data from Kafka with materialized views
- Run machine learning feature pipelines and hyperparameter tuning on historical data
Models Under the Hood
ClickHouse Vector Search (proprietary)Langfuse (LLM observability)
as of 2026-07-05
Limitations
- ClickHouse is an OLAP database, not designed for point-updates or transactional workloads (no full ACID across rows).
- While SQL-standard, many JOIN operations are optimized for specific patterns and can be slower than row-oriented databases.
- The cloud's pay-as-you-go model may surprise users with high-volume queries if compute scaling limits aren't set.
- Also, some advanced features (e.g., continuous queries) are experimental and not production-ready.
as of 2026-07-05
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
—
—
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 (self-managed)
Free
Ideal for
Teams with data engineering expertise who want full control over infrastructure and no per-unit costs, at any data scale.
What this tier adds
Free, self-managed deployment — no usage limits, community support only.
ClickHouse Cloud (Serverless)
Pay-as-you-go (starts at $50/month)
Ideal for
Teams wanting a managed service with autoscaling, no upfront commitment, and flexible compute/storage separation, starting at $50/month.
What this tier adds
Pay-as-you-go, autoscaling to zero, managed by ClickHouse — includes free trial credit.
Bring Your Own Cloud (BYOC)
Contact sales
Ideal for
Enterprises requiring data residency, compliance, or full control in their own AWS/GCP account, with dedicated support and custom pricing.
What this tier adds
Managed in your own cloud account — custom pricing, enterprise SLA, compliance controls.
Integrations
Where the pricing makes sense
The company stage and team size where ClickHouse's pricing actually pencils out — and where peers do it cheaper.
ClickHouse Cloud's pay-as-you-go model (starting at $50/mo) is cost-effective for teams with variable workloads, especially compared to BigQuery ($5/TB scanned) or Snowflake (compute-heavy pricing). However, for predictable, high-volume workloads, reserved compute plans or self-managed open-source may be cheaper.
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.
Data engineers can get ClickHouse Cloud running in minutes — spin up a service, configure ClickPipes for Kafka/S3 ingestion, and start querying. Self-managed OSS requires more effort: cluster deployment, configuration optimization, and index tuning can take days to weeks for production readiness. The learning curve for advanced features (materialized views, partition pruning) adds time.
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.
Migrating in
- →From BigQuery: export data to Parquet on S3, then use INSERT INTO SELECT or the bq-to-clickhouse migration tool; most SQL is compatible with minor dialect adjustments.
- →From Redshift: use the UNLOAD command to S3, then import with ClickHouse's S3 table function; optimize column order and compression for ClickHouse's columnar format.
- →From PostgreSQL: use the ClickHouse PostgreSQL integration to replicate or bulk-import; note that OLTP model doesn't directly translate — restructure for analytical queries.
Migrating out
- ↗To Snowflake: export ClickHouse query results to Parquet on S3, then load into Snowflake using COPY INTO; adjust SQL for Snowflake's dialect (e.g., window functions).
- ↗To PostgreSQL: export aggregated datasets to CSV/Parquet; ClickHouse's wide-column design doesn't directly map to normalized rows, so expect schema redesign.
- ↗To BigQuery: use BigQuery's batch load from GCS (export ClickHouse data to Parquet); note that BigQuery queries scanned data, so costs may change.
Resources & Guides
Frequently Asked Questions
Tools that pair well with ClickHouse
Common stack mates teams adopt alongside ClickHouse, with the specific reason each pairing earns its keep.
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