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Qdrant vs Weaviate

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Qdrant
hybridFree (self-hosted, Apache 2.0)
Weaviate
hybridFree (self-hosted, open-source)
Hybrid Search
Metadata Filtering
Multi Tenancy
Managed Embeddings
Mcp Server
Serverless
Pricing
Free (self-hosted, Apache 2.0)Free Tier (1GB cluster)Standard Tier (usage-based)Premium Tier (minimum spend required)
Free (self-hosted, open-source)Free Hosted$45/mo Flex$280/mo Plus$400/mo Premium
Open Source
Self-Hosted
SDK Languages
pythonjavascripttypescriptrustgojavacsharp
pythonjavascripttypescriptgojava
Frameworks
langchainllamaindexvercel-aiopenai-agentshaystackdspy
langchainllamaindexvercel-aihaystackdspy
Compliance
soc2gdprhipaa
soc2gdprhipaa
Best For
High-performance vector search with rich payload filtering — Rust-written, predictable latency, and a clean self-hosted path
Hybrid RAG with built-in vectorizer modules, generative search, and strong multi-tenancy — runs anywhere from laptop to managed cloud
Limitations
No built-in embedding generation — bring your own embeddings; managed cloud is younger than Pinecone's; fewer turnkey RAG modules than Weaviate
Resource-hungry compared to Qdrant or Chroma; module configuration has a learning curve; some advanced replication features are cloud-only

Supported Not supported Unverified

Qdrant and Weaviate are the two most-considered open-source vector databases for production agent workloads. Both ship hybrid search, multi-tenancy, and MCP servers, but they prioritize different things: Qdrant favors raw performance and filter-driven querying, Weaviate favors module ecosystem and tenant lifecycle.

Where Qdrant wins

  • Payload-aware query planner. Qdrant indexes payload fields (keyword, integer, geo, datetime, and UUID) and uses filter selectivity to drive the plan. Weaviate filters work but lack the same planner depth.

  • Lighter footprint. Rust binary with low memory baseline. Weaviate is heavier per node.

  • Higher throughput per core at steady-state. Independent benchmarks consistently put Qdrant near the top on QPS-per-core for filtered vector queries.

Where Weaviate wins

  • Generative modules run RAG inside the database. generative-* retrieves + calls an LLM in one query. Qdrant retrieves only.

  • Tenant-per-collection scales to 100k+ tenants with offloading. First-class ACTIVE / INACTIVE / OFFLOADED tenant states. Qdrant uses shard keys, which are powerful but flatter.

  • Built-in vectorizer modules. OpenAI, Cohere, Voyage, Hugging Face, Ollama at ingest time. Qdrant deliberately stays out of embedding generation.

The agentic difference

For agents whose every query is heavily filtered — tenant, doc type, date — Qdrant's payload planner consistently delivers lower p99 latency. For agents with long-tail multi-tenant patterns or where you want generation inside the database, Weaviate's tenant model and generative modules pull ahead. Both ship first-party MCP servers and integrate cleanly with major agent frameworks; the differentiator is whether you want the database to do more (Weaviate) or stay narrowly fast (Qdrant).

When to pick which

  • Pick Qdrant when filter-heavy queries dominate, you want vector-first performance per dollar, or you prefer to own chunking and embedding outside the database.

  • Pick Weaviate when you need first-class multi-tenancy at scale, built-in vectorizer and generative modules, or retrieval + generation inside the database.

Last verified: 2026-06-10