Buying snapshot
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| Decision point | What ClickHouse states |
|---|---|
License | Apache-2.0. ClickHouse calls the self-managed open-source server "Always free". |
Deployment | ClickHouse Cloud on AWS, GCP and Azure; self-managed open source; or Bring Your Own Cloud, which runs ClickHouse Cloud in your own VPC (custom quote). |
Install |
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Cloud trial | $300 in credits for 30 days; the trial ends when credits run out or 30 days pass, whichever comes first, then moves to pay-as-you-go. A card added during the trial is charged only after credits are used up or expire. |
How to buy | Direct, or pay-as-you-go or committed contracts through the AWS, GCP and Azure marketplaces at the same pricing. Self-service customers pay monthly by credit card in USD. |
Spend controls | Maximum autoscaling limit on compute and a per-service usage breakdown. Trial and committed-spend customers get emails at 50%, 75% and 90% of credits. |
Our take
Choose ClickHouse if your main workload is analytics over large datasets, such as dashboards, logs, metrics and traces, and you want to pick between running the open-source engine yourself and paying for metered ClickHouse Cloud. If your core need is transaction processing, keep your OLTP database: ClickHouse presents itself as an OLAP system and says developers often run it on top of their warehouse or OLTP databases as a "speed layer".
Evaluate ClickHouse
Popular comparisons
See all 15 ClickHouse comparisons
Where it stands out
Built for analytical reads. ClickHouse stores data by column and uses compression and vectorized query execution to use CPU efficiently. That suits aggregations over large tables more than row-by-row work.
Queries without a server. ClickHouse Local runs SQL on local CSV, TSV, Parquet and other files, so analysts can try the engine before anyone provisions a cluster.
Idle compute stops billing. In ClickHouse Cloud, compute scales to zero after inactivity and wakes on the next query or insert. Storage keeps billing, so workloads with long idle periods benefit most.
Several services, one copy of data. With compute-compute separation (listed on Scale), several services share one copy of the data and storage is billed once.
Observability and AI telemetry. ClickStack is an open-source stack for logs, metrics and traces on ClickHouse, and Langfuse, an open-source platform for LLM observability, evaluations and prompt management, is now part of ClickHouse.
Product screenshot
Trade-offs to weigh
Basic is not a production tier. ClickHouse positions Basic for development, testing and small departmental workloads on a single replica. Plan production on Scale from the start, because multi-zone availability and private networking begin there.
Compliance decides your plan. ClickHouse says Enterprise adds the controls regulated teams need, including SAML SSO, customer-managed keys and HIPAA and PCI compliance. If you need any of these, every compute hour is billed at the Enterprise rate.
Free software is not free to run. ClickHouse says self-managed cost depends on your compute and storage resources plus the headcount needed to manage the deployment. Budget for operators, not only servers.
Managed Postgres is still beta. ClickHouse Managed Postgres is in Public Beta on all three Cloud plans, so treat it as an option to test rather than a settled replacement for an existing Postgres service.
Pricing and plan conditions
ClickHouse Cloud bills compute and storage as separate meters. One compute unit is 8 GiB of RAM and 2 vCPUs; compute is metered per minute and billed per compute-unit-hour at the rate for your plan, cloud provider and region. Storage is metered on the compressed size of your data in object storage plus backups, and storage rates are the same across plans, varying only by provider and region. ClickHouse says analytical data "typically" compresses around 10×, so 1 TB of raw data is "often" billed as roughly 100 GB; test your own data before relying on that ratio.
Three more items can appear on the bill: backups at the storage rate (every service includes one backup every 24 hours, kept for a day), ClickPipes ingestion priced by connector type, and public internet and cross-region egress. Ingress is free.
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| Plan | Published price | What it adds |
|---|---|---|
Basic | Storage $25.30 per 1TB / mo; compute $0.2181 per unit / hr | Up to 1 TB storage; 8-12 GiB total memory; backups every 24h, retained for 1 day; 1 availability zone; export backups to your own cloud account; expert support with 1 business day response time; SSO using Google or Microsoft social login; MFA |
Scale | Compute $0.2985 per compute-unit-hour; storage rates are the same across plans | Everything in Basic, plus: unlimited storage; configurable memory; compute-compute separation; configurable backups; 2+ availability zones; expert support with 1 hour response time 24x7 for Severity 1 issues; S3 role-based access; private networking; automatic vertical scaling; manual horizontal scaling |
Enterprise | Compute $0.3903 per compute-unit-hour; storage rates are the same across plans | Everything in Scale, plus: SAML SSO; private regions; manual vertical scaling for custom profiles; Enterprise support with 30 min response time for Severity 1 issues; Named Lead Support Engineer; transparent data encryption / CMEK; scheduled upgrades; consultative migration guides; HIPAA and PCI compliance |
Data transfer | Public internet egress starting at $0.1152 / GB; inter-region egress starting at $0.0312 / GB | Ingress is free |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
44.9k GitHub commits 90d50.8k GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 5.4/10
See all signals from 10 sources
npm · @clickhouse/client@1.25.0 · PyPI · clickhouse-connect@1.12.0
Alternatives to ClickHouse
The reviewed substitutes for ClickHouse among the OLAP databases, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Apache Pinot
- Choose this if you are building user-facing analytical applications that demand extreme concurrency and consistently low latencies.
- Apache Druid
- Choose this if you need real-time ingestion from streaming sources combined with sub-second interactive queries at high concurrency levels.
- StarRocks
- Choose this if you want ClickHouse-level performance with easier operability and native MySQL wire protocol support.Applies to: Choosing the analytical database that will serve fast queries over large event data.
- Imply Cloud
- ClickHouse and Imply Cloud both run real-time analytical queries over large event streams for dashboards and user-facing apps. Imply Cloud is a managed service built on Apache Druid, while ClickHouse is an open-source columnar database that is also sold as a serverless cloud service.Applies to: Choosing between two products of the same kind for one job.
- SingleStore
- ClickHouse and SingleStore both serve fast analytical queries over large datasets. SingleStore optimises joins and real-time ingestion, speaks the MySQL protocol and also handles transactions, while ClickHouse optimises raw columnar scan throughput, and a team picks one engine.Applies to: Choosing the analytical database that will serve fast queries, with or without heavy joins.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- DuckDB
- Choose this if you need fast local analytics on datasets that fit on a single machine, or you want to embed OLAP capabilities directly in Python, R, or Java applications without running a separate server.Applies to: Single-node or in-process analytical queries, where an embedded engine can replace a cluster. Not an alternative for multi-tenant serving.
- InfluxDB
- ClickHouse and InfluxDB both store and query large volumes of timestamped data such as metrics and events. InfluxDB is a purpose-built time series database for monitoring sensors and systems, available open source, in the cloud or as an enterprise product. ClickHouse is a general columnar SQL database, so it fits when time series are queried with full SQL next to other analytical data.Applies to: When timestamped metrics and events are the main data being stored and queried.
- Elasticsearch
- A columnar analytical database and a search engine both answer questions over large volumes, one by aggregation and one by search and relevance ranking. The overlap on log and event analytics is real, which is why they are compared, and the decision follows whether the workload is aggregation or retrieval.Applies to: Whether the workload is aggregation over events or search and relevance over documents.
- Dremio
- ClickHouse and Dremio both run fast SQL analytics for dashboards and ad-hoc questions. ClickHouse loads data into its own columnar storage and is built for very low latency at high concurrency. Dremio queries Iceberg and Parquet files where they sit on the data lake, so it avoids an ETL step into a separate database.Applies to: When dashboards need fast SQL and the data can either be loaded into a database or queried where it sits on the lake.
- TimescaleDB
- ClickHouse and TimescaleDB both run SQL analytics over time-series data such as metrics, events and IoT readings. TimescaleDB is a PostgreSQL extension, so it keeps Postgres tools and transactional features while adding time partitioning and compression. ClickHouse is a separate columnar OLAP database that fits when very large volumes need fast aggregation more than Postgres compatibility.Applies to: For teams that run SQL analytics over large volumes of time-series data.
- Databricks
- Databricks and ClickHouse can both serve analytical SQL on large datasets. Databricks is a lakehouse platform that also carries data engineering and machine learning; ClickHouse is an open-source columnar database built for very fast queries, which a team picks when query speed for dashboards and applications matters most.Applies to: Serving fast analytical queries, and whether the same platform must also carry ETL and ML.
- Amazon Redshift
- Amazon Redshift and ClickHouse each run analytical SQL on large columnar tables. Redshift is the AWS managed warehouse and the natural central store for an AWS-based company. ClickHouse is an open-source OLAP database tuned for real-time reports and high query concurrency, which matters when the warehouse is too slow for live dashboards.Applies to: Serving fast analytical queries, and whether the central warehouse can also carry them.
Head to head
See detailed alternatives analysis
ClickHouse is one of the fastest columnar OLAP databases available, processing petabyte-scale analytical queries in milliseconds using its column-oriented storage engine written in C++. With nearly 47,000 GitHub stars and over 100,000 developers using the platform, it has become a go-to choice for real-time analytics. However, ClickHouse's complexity in data manipulation, limited transaction support, and steep operational overhead for self-hosted deployments mean that several ClickHouse alternatives deserve serious consideration depending on your workload profile.
Top Alternatives Overview
DuckDB is an in-process SQL OLAP database that runs entirely embedded within your application, requiring zero infrastructure setup. It uses a columnar-vectorized query execution engine and supports Parquet, S3, and standard SQL natively. DuckDB earned a 9/10 user rating and excels at single-node analytical workloads where ClickHouse's distributed architecture adds unnecessary complexity. Choose this if you need fast local analytics on datasets that fit on a single machine, or you want to embed OLAP capabilities directly in Python, R, or Java applications without running a separate server.
Apache Druid is a distributed real-time analytics data store that combines concepts from data warehouses, time-series databases, and search systems. Druid ingests streaming data from Kafka and provides sub-second OLAP queries with automatic data summarization and indexing. It handles high-concurrency workloads efficiently through its segment-based architecture and pre-aggregation at ingestion time. Choose this if you need real-time ingestion from streaming sources combined with sub-second interactive queries at high concurrency levels.
Apache Pinot is a real-time distributed OLAP datastore rated 9/10 by users, designed specifically for low-latency user-facing analytics. It powers analytics at LinkedIn, Uber, and Stripe, delivering P90 query latencies in tens of milliseconds even on petabyte-scale datasets. Pinot supports hundreds of thousands of concurrent queries per second with pluggable indexing options including StarTree, Bloom filter, and geospatial indexes. Choose this if you are building user-facing analytical applications that demand extreme concurrency and consistently low latencies.
Trino (formerly PrestoSQL) is a distributed SQL query engine designed for federated analytics across multiple data sources. Unlike ClickHouse, which requires data ingestion, Trino queries data in place across Hadoop, S3, MySQL, Cassandra, and other systems within a single SQL query. It is free and open-source under Apache 2.0, and the project lists no paid plans. Choose this if you need to query data across multiple heterogeneous sources without copying or moving it into a centralized store.
StarRocks is a next-generation sub-second MPP OLAP database that won InfoWorld's 2023 BOSSIE Award, designed for multi-dimensional analytics, real-time analytics, and ad-hoc queries. It is free and open source, and managed offerings are sold separately by third parties. StarRocks provides MySQL protocol compatibility, making migration from existing MySQL-based tooling straightforward. Choose this if you want ClickHouse-level performance with easier operability and native MySQL wire protocol support.
PostgreSQL is the most mature open-source relational database with over 30 years of active development, offering JSONB support, full-text search, and extensive extensibility. While not a dedicated OLAP engine, PostgreSQL with extensions like Citus or TimescaleDB can handle moderate analytical workloads alongside transactional ones. Choose this if your analytical needs are secondary to transactional workloads and you want a single database that handles both OLTP and moderate OLAP queries.
Architecture and Approach Comparison
ClickHouse uses a shared-nothing distributed architecture with columnar storage, MergeTree table engines, and aggressive compression to achieve its query speed. It processes data using vectorized execution and SIMD instructions, reading only the columns needed for each query. This makes it exceptionally fast for aggregation-heavy workloads but limits its ability to handle frequent updates and deletes -- a well-known weakness users cite around data manipulation.
DuckDB takes the opposite architectural approach: it runs as an embedded, single-process engine with no network overhead. While ClickHouse requires a running server process and cluster management for distributed deployments, DuckDB embeds directly into your application process. This means DuckDB wins on simplicity and startup time but cannot scale horizontally across nodes.
Apache Druid and Apache Pinot both use segment-based architectures optimized for time-series and event data. Druid pre-aggregates data at ingestion time using rollup, reducing storage requirements and query latency at the cost of losing raw row-level detail. Pinot preserves raw data while using pluggable indexes (inverted, range, text, JSON, geospatial) to accelerate queries. Both handle streaming ingestion from Kafka natively, whereas ClickHouse's Kafka integration requires more configuration and tuning.
Trino is architecturally different from all others here because it is a query engine, not a storage engine. It pushes computation down to the underlying data sources and federates results. This makes it ideal for data lake query federation but means it depends on the performance characteristics of each connected source system.
StarRocks uses a hybrid architecture combining columnar storage with an MPP execution engine and a cost-based optimizer. It supports both real-time ingestion and batch loading, and its MySQL wire protocol compatibility means existing MySQL clients, BI tools, and JDBC/ODBC connectors work without modification.
Pricing Comparison
Most ClickHouse alternatives are open-source, but their managed and cloud offerings differ substantially in cost structure.
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| Tool | Self-Hosted Cost | Cloud/Managed Starting Price | Pricing Model |
|---|---|---|---|
| ClickHouse | Free (Apache 2.0) | Usage-based (ClickHouse Cloud) | Open Source + Cloud |
| DuckDB | Free (MIT) | N/A (embedded only) | Open Source |
| Apache Druid | Free (Apache 2.0) | Vendor-dependent (Imply) | Open Source |
| Apache Pinot | Free (Apache 2.0) | Vendor-dependent (StarTree) | Open Source |
| Trino | Free (Apache 2.0) | No paid plans from the project | Open Source |
| StarRocks | Free (Apache 2.0) | No published pricing; managed offerings sold by third parties | Open Source |
| PostgreSQL | Free | Vendor-dependent | Open Source |
| Elasticsearch | Free (basic) | From $99/month (Elastic Cloud Hosted Standard) | Freemium |
| Dremio | N/A | $0.20 per DCU (usage-based) | Usage-Based |
ClickHouse Cloud uses consumption-based pricing, which can be cost-effective for bursty workloads but unpredictable at scale. DuckDB stands out as truly free with no managed service to pay for. For teams wanting a fixed monthly price, Elastic Cloud Hosted starts as low as $99/month for the Standard tier, while StarRocks publishes no pricing and managed StarRocks is sold by third parties. Dremio Cloud charges $0.20 per DCU (Dremio Compute Unit), which suits infrequent analytical workloads but adds up under heavy usage.
When to Consider Switching
Your workload is single-node analytical processing. If your datasets fit on a single machine (up to hundreds of gigabytes), ClickHouse's distributed architecture adds unnecessary operational burden. DuckDB delivers comparable columnar query performance with zero infrastructure, running embedded in your application.
You need strong data manipulation and transactional guarantees. ClickHouse's append-optimized MergeTree engine makes updates and deletes expensive and eventually consistent. PostgreSQL or StarRocks provide proper UPDATE/DELETE support with transactional semantics that ClickHouse cannot match.
Your primary use case is federated querying across data sources. If your data lives across S3, MySQL, Cassandra, and Hadoop, Trino lets you query all of them with a single SQL statement without ingesting data into ClickHouse first. This eliminates ETL pipelines and data duplication.
You are building user-facing applications requiring extreme concurrency. Apache Pinot handles hundreds of thousands of concurrent queries per second with P90 latencies in tens of milliseconds, purpose-built for user-facing dashboards. ClickHouse can handle high throughput but is optimized for complex analytical queries rather than massive concurrent simple lookups.
Your team needs streaming-first real-time analytics. While ClickHouse supports Kafka ingestion, Apache Druid and Apache Pinot were designed from the ground up for streaming data, offering tighter Kafka, Pulsar, and Kinesis integration with less configuration overhead.
Migration Considerations
ClickHouse uses standard SQL with extensions, so most analytical queries translate directly to alternatives like Trino, StarRocks, and DuckDB with minimal rewriting. StarRocks is the easiest migration target because it supports MySQL wire protocol and offers similar columnar storage semantics, meaning existing BI tools and connectors work without changes.
Data format compatibility is generally strong across this ecosystem. ClickHouse can export to Parquet, which DuckDB, Trino, Dremio, and Apache Pinot all read natively. For large datasets, exporting ClickHouse tables to Parquet files on S3 and importing from there is the most practical migration path.
The learning curve varies significantly. DuckDB requires almost no operational learning since it runs embedded. PostgreSQL is the most widely known database, so most teams already have expertise. Trino and Dremio add federation complexity but use standard ANSI SQL. Apache Druid and Apache Pinot have their own ingestion specifications and segment management concepts that require dedicated learning time, typically two to four weeks for a team to become productive.
One critical consideration: ClickHouse's MergeTree family of table engines (ReplacingMergeTree, AggregatingMergeTree, etc.) encode data modeling decisions that do not have direct equivalents in most alternatives. Teams heavily relying on these engine-specific features will need to redesign their data models during migration, which can take weeks of planning and testing for production workloads.