Software · head to head
Qdrant vs ClickHouse
Qdrant
Software
High-performance vector database for similarity search and embedding-based retrieval
- From
- Free
- Rated
- -
ClickHouse
Software
Fast open-source column-oriented database for real-time analytics
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments; ClickHouse limited multi-row atomic transactions and expensive UPDATE/DELETE operations unsuitable for transactional systems
Where they differ
Only the attributes on which Qdrant and ClickHouse actually diverge.
| Attribute | Qdrant | ClickHouse |
|---|---|---|
| Pricing model | freemium | Unknown |
| Platforms | Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming) | Linux, macOS, Windows (via Docker) |
| Founded | Unknown | 2021 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 Qdrant
Nothing recorded that ClickHouse does not also cover.
Only in ClickHouse
- Column-oriented Storage
- Real-time Analytics
- SQL Support
- Linear Scalability
- Data Compression
- Vectorized Query Execution
- Approximate Calculations
- Kafka
What people use each for
The jobs each tool is most often brought in to do.
Qdrant
- Retrieval-augmented generation (RAG) backends for LLM applicationsnot ClickHouse
- Semantic search across large document corporanot ClickHouse
- Multimodal retrieval (text, images, video) for recommendation systemsnot ClickHouse
- Similarity-based product or content recommendationsnot ClickHouse
- Real-time vector indexing for streaming embedding datanot ClickHouse
ClickHouse
- Business intelligencenot Qdrant
- Data warehousingnot Qdrant
- Real-time analyticsnot Qdrant
- Reportingnot Qdrant
- Machine learningnot Qdrant
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Qdrant
- Free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
- Standard and Premium pricing usage-based; specific costs not published; requires calculator or quote
- Requires understanding of embeddings and vector search concepts; not suitable for SQL-only teams
- Early-stage serverless offering (coming soon) suggests maturity gaps in that deployment model
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
Pricing, plan by plan
Qdrant
Free- FreeFree
- Single-node cluster
- 0.5 vCPU
- 1GB RAM
- Standard$null/usage-based
- Dedicated resources
- Flexible scaling
- High availability
- Premium$null/minimum spend
- SSO and SAML
- Private VPC links
- 99.9% uptime SLA
ClickHouse
FreeNo published plan breakdown. See the ClickHouse review.
Which should you pick?
Choose Qdrant if
- You want to start without paying.
- You work on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
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.
Questions people ask
- Is Qdrant or ClickHouse better?
- Neither clearly leads. Qdrant starts at Free and ClickHouse at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Qdrant or ClickHouse?
- Qdrant starts at Free and ClickHouse at Free.
- Does Qdrant or ClickHouse run on more platforms?
- Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming). ClickHouse runs on Linux, macOS, Windows (via Docker).
- Can I use Qdrant for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Qdrant best used for?
- Qdrant is most often used for retrieval-augmented generation (rag) backends for llm applications, semantic search across large document corpora, multimodal retrieval (text, images, video) for recommendation systems, similarity-based product or content recommendations. Of those, retrieval-augmented generation (rag) backends for llm applications and semantic search across large document corpora are not what ClickHouse is typically brought in for.
- What can Qdrant do that ClickHouse cannot?
- ClickHouse covers Column-oriented Storage, Real-time Analytics, SQL Support, Linear Scalability.
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.
SourceClickHouse: 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.
SourceClickHouse: 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