FastEmbed is a lightweight Python library from Qdrant for generating embeddings, available on GitHub with about 3,250 stars under the Apache-2.0 license and actively maintained in 2026. It is built around a simple observation: most embedding pipelines do not need a GPU or gigabytes of deep learning dependencies to be fast and accurate.
FastEmbed runs models on the ONNX Runtime instead of PyTorch, which makes it both lighter and faster for CPU inference, and it uses data parallelism to encode large datasets efficiently. That design makes it a natural fit for serverless environments such as AWS Lambda, containers with tight memory limits, and edge deployments. The library supports a growing catalog of popular text embedding models, including the BGE family from Flag Embedding that performs strongly on the MTEB leaderboard, plus multilingual models, sparse embedding models, and image embedding models. It understands query and passage prefixes for retrieval workloads, offers optional GPU support, and integrates smoothly with the Qdrant vector database, while remaining usable as a standalone embedding generator in any pipeline. Installation is a single pip command. The model catalog includes options for many languages and specialized domains, and models can be downloaded and cached locally, which keeps production deployments self-contained and reproducible. For teams already using Qdrant, generating vectors and storing them within the same ecosystem also removes a layer of glue code.
FastEmbed is free and open source under the Apache-2.0 license.
FastEmbed is for developers building search, recommendation, and RAG applications who want production-grade embeddings with minimal infrastructure, especially in Python and serverless stacks.