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Databases · head to head

ClickHouse vs Zilliz

ClickHouse logo

ClickHouse

Databases

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

From
Free
Rated
-
Full reviewWebsite
Zilliz logo

Zilliz

Databases

Managed vector database and vector lakebase for AI applications

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; Zilliz pricing structure not publicly disclosed, requires sales contact
  • They diverge on capability: ClickHouse covers Column-oriented Storage, Zilliz covers Vector indexing.

Where they differ

Only the attributes on which ClickHouse and Zilliz actually diverge.

Attributes where ClickHouse and Zilliz differ
AttributeClickHouseZilliz
Pricing modelUnknowncontact-sales
PlatformsLinux, macOS, Windows (via Docker)Cloud, Self-hosted
Founded20212017

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 Zilliz

  • Vector indexing
  • Distributed architecture
  • SQL interface
  • Tensor support
  • Real-time search
  • Cloud-native
  • Open-source compatible

What people use each for

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

ClickHouse

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

Zilliz

  • Build retrieval-augmented generation (RAG) systemsnot ClickHouse
  • Implement semantic search over documentsnot ClickHouse
  • Create multimodal search with text and imagesnot ClickHouse
  • Power recommendation engines with vector similaritynot ClickHouse
  • Enable similarity search on user embeddingsnot 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

Zilliz

  • Pricing structure not publicly disclosed, requires sales contact
  • Operational complexity for self-hosted Milvus deployments
  • Learning curve for those unfamiliar with vector databases
  • Limited built-in analytics compared to some alternatives

Pricing, plan by plan

ClickHouse

Free

No published plan breakdown. See the ClickHouse review.

Zilliz

Free

No published plan breakdown. See the Zilliz review.

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 Zilliz if

  • You need vector indexing.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want distributed architecture.
Zilliz review

Questions people ask

Is ClickHouse or Zilliz better?
Neither clearly leads. ClickHouse starts at Free and Zilliz at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClickHouse or Zilliz?
ClickHouse starts at Free and Zilliz at Free.
Does ClickHouse or Zilliz run on more platforms?
ClickHouse runs on Linux, macOS, Windows (via Docker). Zilliz runs on Cloud, Self-hosted.
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 Zilliz is typically brought in for.
What can ClickHouse do that Zilliz cannot?
ClickHouse covers Column-oriented Storage, Real-time Analytics, SQL Support, Linear Scalability. Zilliz covers Vector indexing, Distributed architecture, SQL interface, Tensor support.

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
Zilliz: What is the difference between Milvus and Zilliz Cloud?

Milvus is the open-source vector database that you can self-host. Zilliz Cloud is the fully managed service built on Milvus that removes operational overhead and handles scaling automatically.

Source
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
Zilliz: How many vectors can Zilliz handle?

Milvus and Zilliz Cloud can store and search billions of vectors through their distributed architecture that separates storage and compute layers.

Source
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
Zilliz: Is Milvus open-source?

Yes, Milvus is open-source under the Apache License 2.0 and is part of the LF AI & Data Foundation.

Source
Zilliz: What pricing does Zilliz Cloud offer?

Zilliz Cloud pricing is not publicly listed and requires contacting their team to discuss your specific scale and use case requirements.

Source
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