Vector Databases
Qdrant vs Weaviate
Side by side
- Pricing model
- QdrantHybrid
- WeaviateHybrid
- Starting price
- QdrantFreemium
- WeaviateFreemium
- Free tier
- QdrantYes
- WeaviateYes
- Maturity
- QdrantEstablished
- WeaviateEstablished
- Availability
- QdrantGenerally available
- WeaviateGenerally available
- Last checked
- QdrantOct 16, 2026
- WeaviateOct 16, 2026
| Fact | Qdrant | Weaviate |
|---|---|---|
| Pricing model | Hybrid | Hybrid |
| Starting price | Freemium | Freemium |
| Free tier | Yes | Yes |
| Maturity | Established | Established |
| Availability | Generally available | Generally available |
| Last checked | Oct 16, 2026 | Oct 16, 2026 |
When each one fits
Choose Qdrant when…
Developers and startup founders who want an open-source vector database they can self-host for full data control and cost savings
Enterprise architects who need a compliant self-hosted vector store for sensitive data
Teams building complex RAG pipelines that need rich payload filtering and hybrid search
Look past Qdrant if…
Teams that want a zero-ops fully managed cloud vector database without any infrastructure concern
Non-technical users who need a no-code interface
Qdrant strengths
Open-source Rust engine
Self-hosted and cloud
Dense and sparse vectors
Rich payload filtering
Multi-vector per point
HNSW indexing
gRPC and REST API
Horizontal scaling
On-disk storage
Choose Weaviate when…
Developers building semantic search or RAG pipelines who want automatic vectorization without managing embedding models separately
Enterprise teams who need a compliant vector database with self-hosted or managed options
Startups prototyping AI features who want built-in integrations with OpenAI and Cohere
Look past Weaviate if…
Teams who already manage their own embedding pipeline and need only raw ANN search
Developers needing maximum raw query performance who prefer a leaner Rust-based engine
Weaviate strengths
Built-in vectorization modules
GraphQL and REST API
Multi-tenancy
Hybrid BM25 and vector search
Schema-based objects
Horizontal scaling
LangChain and LlamaIndex integrations
Self-hosted and managed cloud
Trade-offs
Known weaknesses from each tool’s record. Sourced ones link to where they came from.
Qdrant
Limited language support beyond Python and Rust
No source on file
Complex setup for non-Rust environments
No source on file
Performance degradation under high load without proper tuning
No source on file
Smaller community compared to more established vector search engines
No source on file
Weaviate
No native support for Windows operating systems or binaries.
Inability to reindex data smoothly, particularly for updating embeddings or inverted indexes.
No support for cross-tenant queries, requiring multiple separate queries to search across tenants.