FastEmbed
Python library for generating text and image embeddings with minimal dependencies
- Vendor
- Qdrant
- Also known as
- fastembed
What is FastEmbed?
FastEmbed is a Python library that generates text and image embeddings using ONNX Runtime, eliminating the need for large PyTorch dependencies. It supports various embedding models including sparse embeddings, late-interaction models, and cross-encoders, making it suitable for serverless and resource-constrained environments.
FastEmbed pricing
We don't have FastEmbed's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
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- Cloud / SaaS
- Self-hosted
Integrations (3)
Independently observed- Qdrant
- Langchain
- Llama Index
Security & compliance
Known vulnerabilities: 0 (0 in the last 12 months) sourcea count reflects scale & disclosure, not quality
FastEmbed FAQ
Common questions about FastEmbed, answered from independent, dated evidence.
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. It is indexed under Embeddings Apis.
Can FastEmbed be self-hosted?
Yes. FastEmbed can be deployed on-premise and self-hosted, so it does not have to run on the vendor's infrastructure.
Is FastEmbed open source?
Yes. FastEmbed is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.
FastEmbed alternatives
Other embeddings apis we track, ranked by the same independent score.
- Cohere EmbedNumerical representation models that convert text into embeddings for machine learning applicationsmedium · 60%
- Mixedbread (mxbai)API service for indexing and searching multimodal content with semantic capabilitiesmedium · 58%
- Amazon Bedrock Titan Embeddingslow · 35%
- sentence-transformersA Python module for working with embedding and reranking modelsmedium · 54%
- Jina EmbeddingsMultimodal vector embeddings for search, content retrieval, and AI agent developmentlow · 35%
- BAAI FlagEmbeddinglow · 45%
Compare FastEmbed
Side by side against other embeddings apis, attribute by attribute, with a source on every value.
The vioscaleAI 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 |
|---|---|---|---|---|
| Development activity | 59 | 0.14 | 7.9 | ✓ |
| Capabilities | 92 | 0.08 | 7.4 | ✓ |
| Release cadence | 82 | 0.08 | 6.6 | ✓ |
| Stars | 66 | 0.05 | 3.1 | ✓ |
| Integrations | 17 | 0.05 | 0.9 | ✓ |
| Dependent projects | 12 | 0.05 | 0.6 | ✓ |
| Security posture | 0 | 0.12 | 0.0 | - |
| Package downloads | 0 | 0.04 | 0.0 | - |
| Security score | 0 | 0.05 | 0.0 | - |
| Developer Q&A activity | 0 | 0.14 | 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 | 71 | mediumsource · 2026-10-15 · 65% |
Adoption
Content
| Attribute | Value | Evidence |
|---|---|---|
| Faq | 3 items | mediumsource · 2026-09-10 · 72% |
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 · 2026-10-15 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 3 | mediumsource · 2026-09-14 · 60% |
Language
| Attribute | Value | Evidence |
|---|---|---|
| Primary | Python | highsource · 2026-10-15 · 90% |
License
| Attribute | Value | Evidence |
|---|---|---|
| Spdx | Apache-2.0 | highsource · 2026-10-15 · 95% |
Pricing
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 · 2026-10-15 · 90% |
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