# Vector database showroom. Part 3 Qdrant — The Hot Hatchback That Carries More Than It Looks

> Source: <https://dev.to/silver_dev/vector-database-showroom-part-3-qdrant-the-hot-hatchback-that-carries-more-than-it-looks-2ocm>
> Published: 2026-10-08 20:32:21+00:00

## 
  
  
  🏎️ Qdrant — The Hot Hatchback That Carries More Than It Looks

*Sharp acceleration, precise handling — and a trunk noticeably bigger than it looks from the outside.*

**Under the hood:** a Rust core shipped as a single binary or container (the Python client even has an embedded/local mode for experiments). Apache 2.0. The signature feature — **filterable HNSW**: the graph is built with payload indexes in mind, so filters participate in graph traversal instead of being glued on top.

### 
  
  
  Where it shines:

- Remember pgvector's three filter tools? Here filtering is part of the index itself: "similar, but only year=2024" is this car's home turf
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**Hybrid out of the box:** sparse vectors since v1.7, the Query API with prefetch and fusion (RRF/DBSF) since v1.10 — dense + sparse in a single request
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**Quantization** is our "lightweight alloys": int8 shrinks vectors 4×, binary 32×. Originals can live on disk while only compressed copies stay in RAM. Hundreds of millions of vectors on a single node is a real setup, not marketing
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**Multivectors (ColBERT-style):** * several vectors per point, late interaction — something very few databases in this showroom offer
Operations: Prometheus metrics, snapshots, API keys + JWT RBAC

### 
  
  
  Where it stalls:

- 
**Bring your own embeddings** — it's a database, not an embedder (contrast with last issue's scooter). Clients for Python/JS/Go/Rust, LangChain/LlamaIndex integrations included
- Distributed mode (sharding, replication, Raft) is real ops work — simpler than the freight train arriving later in this series, heavier than Postgres
- Backups are manual snapshots. No WAL, no point-in-time recovery like the one you're used to in Postgres
- Tuning is part of the contract: m, ef_construct, hnsw_ef, quantization with rescoring. Defaults are decent, but a hot hatch likes to be tuned

### 
  
  
  What breaks if you skip the manual:

- 
**The distance function (COSINE/DOT/EUCLID/MANHATTAN) is locked at collection creation.** Wrong pick = full reindex — there is no "alter on the fly"
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**Payload indexes:** forget to create them and filtered search degrades to scans. The right order: collection → payload indexes → bulk upload. Adding an index afterwards rebuilds the HNSW graph in the background to wire up the filterable edges
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**Benchmark trap #1:** while a segment stays below the optimizer's`indexing_threshold` , no HNSW index is built for it — search falls back to brute force, fast and with perfect recall. A naive benchmark on a small collection measures exact search, not HNSW. Honest numbers require production-like volume — or explicitly lowering`indexing_threshold`
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**Quantization without rescoring eats recall** — and binary quantization is model-sensitive: brilliant on some embeddings, poor on others. Ground truth for comparison: search with exact=true. Showroom thesis: vendor benchmarks are ads — verify on your own data
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**Named vectors:** if the collection was created with named vectors, queries must pass the vector name via the using parameter — otherwise a`400`

**Cost of ownership:** free under Apache 2.0; Qdrant Cloud (managed & hybrid) exists. RAM appetite is flexible — quantization + on-disk originals instead of "6 GB per million."

**Mechanics & parts:** Qdrant GmbH (Berlin) behind it; the docs are among the best in the industry. "Qdrant engineers" are rare on the market, but this is a database a backend developer can drive — not a twenty-pod zoo.

✅ **Take it if:** millions to hundreds of millions of vectors, filter-heavy RAG, self-hosting, and you want speed without building railroads

❌ **Pass if:** your logic lives in SQL and you need joins (the station wagon does those better), or you're counting billions with GPUs — the train arrives later in this series

**Test drive:**

A hatchback doesn't pretend to be a station wagon and doesn't build railroads — it just carries fast. And when your data outgrows the trunk, the API doesn't change: you move to a cluster via snapshots, and the application never notices.