Qdrant
Rust-based high-performance vector search engine with REST and gRPC interfaces, rich payload filtering, and quantization, available as self-hosted or managed Qdrant Cloud.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Rust implementation eliminates entire classes of memory safety vulnerabilities; self-hosted defaults to no TLS and no auth; read-only key separation limits blast radius of leaked credentials.
⚡ Reliability
Best When
Building agent memory or search backends where query-time payload filtering accuracy and raw search speed are the critical constraints.
Avoid When
You need a managed cloud service that includes built-in embedding models and want to avoid running a separate vectorization service.
Use Cases
- • Agent retrieval systems requiring sub-millisecond vector search with complex metadata pre-filters on payload fields
- • High-throughput embedding indexing pipelines where Rust performance and quantization reduce memory footprint by 4x
- • Multi-agent systems using named collections per agent with isolated vector spaces and independent HNSW indexes
- • Sparse+dense hybrid search combining BM25-style sparse vectors with dense embeddings in a single query
- • Recommendation systems within agents where payload filtering narrows the candidate set before ANN search
Not For
- • Teams wanting built-in embedding generation — Qdrant stores and searches vectors but requires an external embedding service
- • Workloads needing full-text search without vector components (use Elasticsearch instead)
- • Applications requiring multi-document ACID transactions across multiple collections
Interface
Authentication
API key passed as api-key header or query param; read-only vs full-access keys supported; self-hosted can run without auth (not recommended for production).
Pricing
Self-hosted via Docker or Kubernetes is completely free; cloud free tier is generous for prototyping; quantization (scalar, product, binary) significantly reduces memory costs.
Agent Metadata
Known Gotchas
- ⚠ Payload filtering uses a must/should/must_not structure — agents generating filter conditions dynamically must validate they produce valid MatchValue or Range types, as type mismatches silently return empty results
- ⚠ HNSW index is built asynchronously after bulk upload; querying immediately after upload may return incomplete results until indexing status shows green
- ⚠ Named vectors (multiple vector spaces per point) require specifying the vector name on every search request; omitting the name queries the default vector and silently ignores others
- ⚠ The scroll API (for iterating all points) does not guarantee order and may return duplicates near page boundaries if points are being inserted concurrently
- ⚠ Quantization (scalar/binary) reduces memory and speeds search but degrades recall — agents that calibrate recall thresholds on unquantized indexes may see unexpected misses after enabling quantization
Alternatives
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Scores are editorial opinions as of 2026-03-06.