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Databases · head to head

ClickHouse vs Dremio

ClickHouse logo

ClickHouse

Databases

Fast open-source column-oriented database for real-time analytics

From
Free
Rated
-
Full reviewWebsite
Dremio logo

Dremio

Databases

SQL query engine and lakehouse layer over Iceberg tables in object storage

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: ClickHouse limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems; Dremio reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.
  • They diverge on capability: ClickHouse covers Column-oriented Storage, Dremio covers Arrow-based execution.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which ClickHouse and Dremio actually diverge.

Attributes where ClickHouse and Dremio differ
AttributeClickHouseDremio
Pricing modelUnknownPer Dremio Compute Unit consumed
PlatformsLinux, macOS, Windows (via Docker)Linux, Kubernetes, Cloud, Docker
Founded2021Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in ClickHouse

  • Column-oriented Storage
  • Real-time Analytics
  • SQL Support
  • Linear Scalability
  • Data Compression
  • Vectorized Query Execution
  • Approximate Calculations
  • Kafka

Only in Dremio

  • Arrow-based execution
  • Reflections
  • Semantic layer
  • Iceberg catalogue
  • Federated queries
  • Autonomous management
  • Fine-grained access control
  • BI connectors

What people use each for

The jobs each tool is most often brought in to do.

ClickHouse

  • Business intelligencenot Dremio
  • Data warehousingnot Dremio
  • Real-time analyticsnot Dremio
  • Reportingnot Dremio
  • Machine learningnot Dremio

Dremio

  • A company with petabytes of Parquet in S3 that wants BI dashboards without duplicating it into a warehousenot ClickHouse
  • A data platform team standardising on Apache Iceberg and needing a SQL engine plus catalogue that does not lock the tables innot ClickHouse
  • An analytics group accelerating slow lake queries with Reflections instead of hand-built aggregate tablesnot ClickHouse
  • A regulated enterprise that must keep data on premises but wants a modern lakehouse SQL layernot ClickHouse

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

ClickHouse

  • Limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems
  • Requires upfront schema design discipline with MergeTree engine choices and sort/partition keys
  • Experimental vector search support, not production-ready for vector operations
  • Different query syntax from standard SQL requiring migration planning
  • Limited JOIN capabilities compared to traditional relational databases
  • Migration complexity with 2-4 weeks estimated for data type mapping and query translation

Dremio

  • Reflections consume compute and storage to build and refresh continuously, so a team that enables them widely discovers that background maintenance rather than user queries drives the DCU bill.
  • Self-managing Dremio on Kubernetes requires real platform engineering capacity for tuning executors, memory and coordinator sizing, and it is not comparable in effort to running a managed warehouse.
  • The Community Edition lacks the security and governance features most enterprises require, so the free tier is a trial path rather than a viable production option for regulated buyers.
  • Dremio Cloud is AWS-first, which leaves Azure and Google Cloud customers on the self-managed path with the operational burden that entails.
  • Query performance without Reflections on raw, poorly laid out files is often unremarkable, so the promise of querying the lake as is depends on file layout work you still have to do.

Pricing, plan by plan

ClickHouse

Free

No published plan breakdown. See the ClickHouse review.

Dremio

Free
  • Community EditionFree
    • Self-managed on your own hardware
    • SQL engine and semantic layer
    • No vendor support
  • Dremio Cloud$0.2/hour
    • Billed at $0.20 per Dremio Compute Unit
    • Includes query execution, Reflections and background processing
    • 400 dollar trial credit for 30 days
  • Enterprise$undefined/year
    • Self-managed on Kubernetes, on premises or any cloud
    • Enterprise security, SSO and governance
    • Vendor support with SLA

Which should you pick?

Choose ClickHouse if

  • You need column-oriented storage.
  • You want to start without paying.
  • You work on Linux, macOS, Windows (via Docker).
  • You also want real-time analytics.
ClickHouse review

Choose Dremio if

  • You need arrow-based execution.
  • You want to start without paying.
  • You work on Linux, Kubernetes, Cloud, Docker.
  • You also want reflections.
Dremio review

Questions people ask

Is ClickHouse or Dremio better?
Neither clearly leads. ClickHouse starts at Free and Dremio at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClickHouse or Dremio?
ClickHouse starts at Free and Dremio at Free.
Does ClickHouse or Dremio run on more platforms?
ClickHouse runs on Linux, macOS, Windows (via Docker). Dremio runs on Linux, Kubernetes, Cloud, Docker.
Can I use ClickHouse for free?
Both have a free tier, so you can try either at no cost before committing.
What is ClickHouse best used for?
ClickHouse is most often used for business intelligence, data warehousing, real-time analytics, reporting. Of those, business intelligence and data warehousing are not what Dremio is typically brought in for.
What can ClickHouse do that Dremio cannot?
ClickHouse covers Column-oriented Storage, Real-time Analytics, SQL Support, Linear Scalability. Dremio covers Arrow-based execution, Reflections, Semantic layer, Iceberg catalogue.

Answered from the vendors’ own pages

ClickHouse: What is ClickHouse best used for?

ClickHouse is optimized for analytical workloads on large datasets. It excels at fast aggregations and queries, being 10-100x faster than PostgreSQL on large aggregations.

Source
Dremio: How is Dremio Cloud billed?

At 0.20 US dollars per Dremio Compute Unit, which counts query execution, Reflection building and platform overhead, not just user queries.

ClickHouse: Does ClickHouse support transactions?

ClickHouse has limited transaction support and expensive UPDATE/DELETE operations. It is not suitable for transactional workloads requiring strict ACID guarantees.

Source
Dremio: Is there a free version?

Yes, a Community Edition you self-manage, but it omits the enterprise security and governance features and comes with no support.

ClickHouse: How does ClickHouse compare to PostgreSQL?

ClickHouse is 10-100x faster for analytics but PostgreSQL is better for transactional workloads. Many teams use both: PostgreSQL for writes via MaterializedPostgreSQL replication to ClickHouse for analytics.

Source
Dremio: Does it lock in my data?

No, tables stay in Apache Iceberg or Parquet in your own object storage and can be read by Spark, Trino or other engines.

Dremio: Do I still need a warehouse?

Often not for analytics, but Dremio is not a transactional store and high-concurrency operational serving is not its strength.

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