Marqo
Open-source vector search with built-in embedding inference
Marqo is profiled here as a Vector Database tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Marqo is an open-source vector search engine, created by Tom Hamer and Jesse Clark, that folds embedding generation and vector storage into a single system. A developer sends text or images and Marqo embeds, indexes, and serves them, which removes the separate embedding step that most vector setups require. It handles multimodal data and supports tensor-based search, and Marqo Cloud offers a managed deployment for teams that prefer not to run the engine themselves.
Key Capabilities:
End-to-end search that embeds, indexes, and queries from one engine
Built-in inference that removes the need for a separate embedding service
Multimodal indexing of text and images in the same store
Tensor-based retrieval for fine-grained semantic matching
A simple API for adding documents and running queries
Marqo Cloud for managed, scalable deployments
Alternative tools
- Deep Lake
Database for AI that stores tensors and embeddings
- Chalk
Feature platform for real-time machine learning data
- Hopsworks
Feature store and ML platform for batch and real-time data
- turbopuffer
Serverless vector and full-text search on object storage
- LanceDB
Embedded multimodal vector database on the Lance format
- pgvector
Vector similarity search as a Postgres extension