ANNex vs Qdrant

Qdrant is a production vector database server with a managed cloud offering, mature HTTP API, and strong operational tooling. ANNex is a Rust-native embedded library for applications that need to own the retrieval pipeline directly. The primary split is deployment model: server versus library.

Feature comparison

Feature ANNex Qdrant
Dense vector search ✓ ✓
Sparse / BM25 ✓ ✓
Hybrid RRF fusion ✓ ✓
Multivector / ColBERT ✓ ✓
Payload filters ✓ ✓
Embedded (no server) ✓ ✗
HTTP server ✓ ✓
Rust-native library ✓ ✗
Python bindings ✓ ✓
Snapshots + WAL ✓ ✓
Managed cloud ✗ ✓
Distributed / sharded ✗ ✓

Retrieval quality (BEIR nDCG@10)

5 corpora · pinned MiniLM vectors · identical BM25 weights · 2026-09-29 · Apple M2. Every per-corpus 95% bootstrap interval between hybrid systems includes zero — this is parity, not a proven advantage.

System NFCorpus SciFact FiQA Macro
ANNex hybrid 0.3574 0.7264 0.3798 0.4401
Qdrant hybrid 0.3580 0.7257 0.3798 0.4396

Full data: benchmarks →

When ANNex makes sense

  • → You want an embeddable library — no server process, no network hop, direct integration into your binary.
  • → You need custom pipeline access: HNSW internals, snapshot control, WAL tuning.
  • → You are building in Rust and want native types and zero-copy search.

When Qdrant makes sense

  • → You need a managed cloud service with SLAs and support.
  • → You need distributed / sharded search across multiple nodes.
  • → You want a battle-tested system with a large user community.