Mixedbread
Embedding and reranking models with a hosted API
Mixedbread is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Mixedbread is an AI company that builds open-weight and hosted embedding and reranking models for search and retrieval. Its mxbai embedding models produce dense vectors that perform well on retrieval benchmarks at compact sizes, and its rerank models reorder candidate results by deep semantic relevance. Developers can run the open weights locally or call Mixedbread's API for embeddings, reranking, and managed vector stores, which covers both prototyping and production retrieval from one provider. The embedding models also handle retrieval across many languages, and the company contributes its models and research openly to the community.
Key Capabilities:
mxbai embedding models that generate dense vectors for semantic search
Reranking models that reorder retrieval candidates by relevance
Open weights on Hugging Face for local and offline use
A hosted API for embeddings and reranking with one key
Managed vector stores for storing and querying embeddings
Matryoshka and quantization support for smaller, cheaper vectors
Alternative tools
- RAGFlow
Open-source RAG engine with deep document understanding
- LanceDB
Embedded multimodal vector database on the Lance format
- Milvus
Open-source vector database built for billion-scale search
- Sentence Transformers
Python framework for dense text and image embeddings
- R2R
Production retrieval system with ingestion and an API
- Mem0
Long-term memory layer for AI agents and assistants