Qdrant
Best for
High-performance vector search with rich payload filtering — Rust-written, predictable latency, and a clean self-hosted path
Limitations
No built-in embedding generation — bring your own embeddings; managed cloud is younger than Pinecone's; fewer turnkey RAG modules than Weaviate
Features
Frameworks
SDK Languages
Compliance
Qdrant
Qdrant is an open-source vector database written in Rust, optimized for predictable latency and rich payload filtering. Its query planner uses payload indexes to short-circuit vector search whenever a metadata filter would dominate — which makes it especially strong for agent workloads where every query is scoped by tenant, user, or document type.
For agents, Qdrant's standout features are dense + sparse + late-interaction hybrid retrieval in a single API, tenant-aware payload filtering, and shard-key-based multi-tenancy that scales horizontally. The official Qdrant MCP server exposes search, upsert, and snapshot operations as agent tools.
Qdrant deliberately stays out of the embedding business — you bring your own embeddings (OpenAI, Cohere, local models). For teams that want the embedding pipeline owned outside the database (versioning, replays, A/B tests), this is a feature, not a missing one. For teams that want one-click ingest of raw text, Weaviate or Pinecone are a closer fit.
Agent-specific features:
- Hybrid search: dense + sparse + ColBERT-style late interaction in a single
query_pointscall - Payload-aware query planner: heavy filters short-circuit the vector search
- Shard-key multi-tenancy with per-tenant snapshots and deletion
- Official MCP server (
mcp-server-qdrant) for agent-driven retrieval - Hybrid Cloud and Private Cloud for fully managed deployments inside your VPC