Vector storage
With CrateDB's vector store, you can easily store and retrieve embeddings generated by ML models, seamlessly integrating vectorized data with your existing datasets. It allows you to enrich your existing data with semantics, providing context that aligns with your data and enhancing explainability.
Advanced search capabilities
CrateDB offers advanced search capabilities through its similarity search and flexible filtering, combining full-text and vector search. Similarity search allows users to find similarities across any data represented as vectors, while the combination of full-text and vector search improves the search precision by enhancing semantic similarity and keyword matching. These features facilitate enhanced recommendations, anomaly detection, and other AI/ML use cases.
Ingestion
Native SQL support
CrateDB is a distributed database that implements native SQL and the PostgreSQL Wire Protocol. With CrateDB, you can easily query even complex and dynamic schemas in a familiar SQL format, without the need to learn custom languages. The massive parallel execution of queries ensures fast response times, making it ideal for handling ad-hoc queries across large datasets, including those commonly encountered in AI/ML applications.
Ecosystem
CrateDB seamlessly integrates with your AI/ML stack (LangChain, MLflow, PyCaret ...) and analytics stack (Tableau, PowerBI ...) by leveraging the support of the PostgreSQL Wire Protocol.
Reduced TCO
CrateDB offers a low Total Cost of Ownership (TCO) by eliminating the need to manage multiple systems. It seamlessly integrates your data, keeping your (meta-)data and vector representations aligned without the complexity of data synchronization processes. With its use of native SQL, CrateDB simplifies development and ensures compatibility with existing systems.
