What is FastEmbed?
A Python library that creates vector embeddings from text and images using efficient, quantized model weights optimized for speed and local inference. Supports multiple embedding architectures including multilingual models and outperforms OpenAI's Ada-002 in accuracy.
What FastEmbed does
The capabilities that matter for embeddings apis, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open weights local
- Matryoshka representation learning for dimension truncation
- -
- Multilingual support across 100 plus languages
- ✓
- Binary and scalar quantization for memory reduction
- ✓
- Cross encoder reranker API offered
- ✓
- Late interaction architecture colbert v2
- ✓
- Massive context window greater than 8k tokens
- ✓
- Multi modal image and text joint embeddings
- ✓
- Task specific prefixes query vs document instructions
- ✓
- Zero data retention training opt out enterprise
- -
- SOC2 type ii
- -
- Mit or apache permissive oss license
- ✓
- Pricing model
- Free open weights
Platform & deployment
Independently observed- On-premise
- Self-hosted
Integrations (1)
Independently observed- Qdrant
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
FastEmbed alternatives
Other embeddings apis we track, ranked by the same independent score.
- Mixedbread (mxbai)Strengthen AI agent performance through efficient multi-format data retrievallow · 35%
- Cohere EmbedTransform text and images into searchable vector representations for precise document matching and semantic discoverylow · 45%
- BAAI FlagEmbeddinglow · 27%
- Amazon Bedrock Titan Embeddingslow · 21%
- sentence-transformerslow · 32%
- Jina EmbeddingsEmbeddings API for multimodal, multilingual content that powers search, retrieval-augmented generation, and AI applicationslow · 36%
Compare FastEmbed
Side by side against other embeddings apis, attribute by attribute, with a source on every value.
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 FastEmbed, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Capabilities | 92 | 0.08 | 7.2 | ✓ |
| Release cadence | 82 | 0.05 | 4.2 | ✓ |
| Development activity | 36 | 0.09 | 3.4 | ✓ |
| Stars | 66 | 0.03 | 1.7 | ✓ |
| Dependent projects | 12 | 0.06 | 0.7 | ✓ |
| Integrations | 9 | 0.07 | 0.6 | ✓ |
| Security posture | 0 | 0.07 | 0.0 | - |
| Package downloads | 0 | 0.14 | 0.0 | - |
| Security score | 0 | 0.04 | 0.0 | - |
| Developer Q&A activity | 0 | 0.06 | 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.
Activity
| Attribute | Value | Evidence |
|---|---|---|
| Commits last 30d | 13 | mediumsource · 2026-08-26 · 65% |
Adoption
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Pricing model: free_open_weights · Architecture model: open_weights_local · Cross encoder reranker api offered: Yes · Mit or apache permissive oss license: Yes · Late interaction architecture colbert v2: Yes · Multi modal image and text joint embeddings: Yes | mediumsource · 2026-08-21 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 1 | mediumsource · 2026-08-21 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-08-26 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-08-26 · 95% |
Release
Security
| Attribute | Value | Evidence |
|---|---|---|
| Vulnerabilities | Count: 0 · Source: https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=fastembed&per_page=100 · Last 12m: 0 | highsource · 2026-08-26 · 90% |