Store and search your data
Organize documents, embeddings, and metadata in collections, and choose index types for the fields you want to search or filter. Bring your own vectors or use native embeddings to generate document and query vectors from text and images. Search with full-text, dense vector, or sparse vector queries. Combine retrieval paths with hybrid search, narrow matches with filters, and optionally use native reranking to reorder retrieved results. See the search overview for query options, including keyword facets.Test and version your data
Use branches, tags, and aliases to manage changes to a collection:- Branch to isolate changes and test them without changing what applications read from another branch.
- Tag a committed snapshot to pin a data version for evaluation or later use.
- Use an alias to select a branch or tag for reads and switch the target when ready.
Run without managing servers
Documents, vectors, and indexes live in durable object storage, while compute scales independently for indexing and search. LambdaDB meters storage, reads, writes, and managed inference, with no compute charge between requests. See usage and pricing for plan allowances and charges.Get started
Connect your coding agent to LambdaDB and run your first search, or build directly with an SDK. For direct HTTP requests, see the REST API reference.Start with an AI agent
Connect your coding agent and run your first search.
Start with an SDK
Create a collection, add documents, and run your first search with an SDK.
Use the CLI
Install the CLI, import JSONL, and run your first query.
Use the MCP server
Configure an MCP client manually and troubleshoot connections.