Faiss
A library for efficient similarity search and clustering of dense vectors
- Also known as
- faiss
What is Faiss?
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM.
What Faiss does
The capabilities that matter for vector databases, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Deployment
- Embedded
- Serverless / consumption pricing
- ✗
- ANN index (HNSW / IVF / DiskANN / ScaNN / flat)
- -
- Distance metrics (cosine / dot / Euclidean)
- Euclidean, dot product, L1, Linf
- Metadata filtering
- -
- Hybrid search (dense + sparse fusion)
- -
- Built-in BM25 / lexical
- -
- Quantization
- -
- On-disk vs in-memory
- Disk-backed
- Multi-tenancy (namespaces / collections / tenants)
- -
- Horizontal scale
- Single-node
- Consistency
- -
- Licence class
- -
- Purpose-built vs feature-of-parent
- Extension
Platform & deployment
Independently observed- macOS
- Linux
- Windows
- Self-hosted
Faiss alternatives
Other vector databases we track, ranked by the same independent score.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Faiss, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Reliability | 50 | 10.00 | 500.0 | ✓ |
| Price Level | 0 | 6.00 | 0.0 | - |
| Capabilities | 0 | 17.00 | 0.0 | - |
| Integrations | 0 | 8.00 | 0.0 | - |
| Security Posture | 0 | 18.00 | 0.0 | - |
| Pricing Transparency | 0 | 8.00 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | {"deployment":"embedded","serverless":false,"storage_tier":"disk_backed","product_class":"extension","distance_metrics":"Euclidean, dot product, L1, Linf","horizontal_scale":"single_node"} | mediumsource · 2026-08-03 · 60% |
Pricing
| Attribute | Value | Evidence |
|---|---|---|
| Model | commercial | mediumsource · 2026-08-03 · 60% |
Reliability
| Attribute | Value | Evidence |
|---|---|---|
| Status page | Yes | mediumsource · 2026-08-03 · 60% |