fastembed
FastEmbed is a Python library for generating text, image, and multimodal embeddings using ONNX Runtime instead of PyTorch, making it lightweight and fast without requiring GPU or large dependencies. It supports multiple embedding models (dense, sparse, late-interaction, and rerankers) and integrates natively with Qdrant vector databases for RAG applications.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | qdrant/fastembed |
| Owner | qdrant |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 3.1k |
| Forks | 213 |
| Open issues | 137 |
| Latest release | v0.8.0 (2026-03-23) |
| Last updated | 2026-06-23 |
| Source | https://github.com/qdrant/fastembed |
What fastembed is
FastEmbed provides ONNX Runtime-based inference for embedding generation across modalities (text, image, multimodal via ColPali) with support for dense embeddings (Flag/BGE models), sparse embeddings (SPLADE++), late-interaction models (ColBERT), and cross-encoder rerankers. It includes custom model registration, data parallelism for batch processing, and optional GPU acceleration via CUDAExecutionProvider.
Get the fastembed source
Clone the repository and explore it locally.
git clone https://github.com/qdrant/fastembed.gitcd fastembed# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Model download and initialization occurs on first call; plan for lazy-load delays or pre-warm in initialization scripts.
- ONNX Runtime optimization trade-offs (e.g., quantization) may affect embedding quality; benchmark against baseline (Flag/BGE) models before production.
- Generator pattern for embeddings requires explicit list() conversion; ensure memory for large batches or use streaming consumption.
- Custom model registration via add_custom_model() is supported but requires manual ONNX file/metadata management.
- GPU support requires separate fastembed-gpu package and CUDA 12.x; CPU and GPU variants cannot coexist in same environment.
When to avoid it — and what to weigh
- Custom Fine-Tuned Models Requiring Training — FastEmbed is inference-only via ONNX. If you need to train or fine-tune embeddings on proprietary data, use PyTorch/Transformers directly.
- Requirement for Exotic Model Architectures — Support is limited to models convertible to ONNX format. Bleeding-edge research models not in the supported list require manual ONNX conversion (effort and risk unknown).
- Real-Time Latency <10ms on CPU — While faster than PyTorch, CPU inference still incurs latency. If sub-10ms embedding latency is required, GPU or specialized hardware is necessary.
- Strict Model Version Pinning — The library auto-downloads models from HuggingFace. If you require air-gapped or immutable model versioning, setup complexity increases.
License & commercial use
FastEmbed is licensed under Apache License 2.0 (Apache-2.0), an OSI-approved permissive license.
Apache-2.0 permits commercial use, modification, and distribution with minimal restrictions (attribution required, no liability/warranty). However, review depends on downstream model licenses (e.g., BAAI/bge-small-en-v1.5, sentence-transformers models); verify their terms for commercial embedding generation in closed-source or proprietary systems.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security vulnerabilities disclosed in provided data. Dependency on ONNX Runtime and PyTorch (transitive) should be scanned for CVEs. Model auto-download from HuggingFace requires network access; use private mirrors or cached models for air-gapped environments. No mention of input validation for embedding/reranking functions; sanitize user text if using in untrusted contexts. ONNX file integrity depends on HuggingFace CDN security.
Alternatives to consider
sentence-transformers (Hugging Face)
More mature, wider model selection, active research backing. Requires PyTorch, larger footprint; better for GPU-heavy or on-premises deployments.
OpenAI Embeddings API
Hosted, no infrastructure needed, strong accuracy (Ada-003). Higher latency, vendor lock-in, and per-token costs unsuitable for high-volume local/offline use.
Jina Embeddings v3
Comparable speed, multimodal support, modern architecture. Ecosystem less mature; Qdrant integration not as native.
Build on fastembed with DEV.co software developers
Evaluate FastEmbed for serverless pipelines, Qdrant integration, or batch embedding. Review model compatibility and downstream commercial license terms. Contact us to plan integration.
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fastembed FAQ
Do I need a GPU to use FastEmbed?
How does FastEmbed compare to OpenAI's Ada embeddings?
Can I add a model not in the supported list?
What is the typical latency for embedding a single document?
Software developers & web developers for hire
From first prototype to production, DEV.co delivers software development services around tools like fastembed. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across vector databases and beyond.
Ready to Build RAG with FastEmbed?
Evaluate FastEmbed for serverless pipelines, Qdrant integration, or batch embedding. Review model compatibility and downstream commercial license terms. Contact us to plan integration.