LanceDB vs Meilisearch

LanceDB

Embedded, no server, and tables can live directly in S3, GCS or Azure storage. No self-serve hosted API. Enterprise is sold through a contact form.

Meilisearch

Keyword, vector and hybrid search share one request, behind keys scoped by action and index and a published OpenAPI 3.1 file with 143 operations. The built-in MCP server is experimental and read-only, and Cloud access starts with a browser sign-up and a 14-day trial.

LanceDB

1. Use it through the Python or TypeScript library. There's no public endpoint an agent can call without an Enterprise deployment
2. Build an FTS index before calling query_type="hybrid", or hybrid search has no keyword side
3. Call optimize() after many small writes, since each write adds a new file version
4. Use select() to drop the vector column from results unless the task needs it
5. On object storage, set a read consistency interval if several processes write the same table

Meilisearch

1. Create a key with only search, documents.get and indexes.get on the indexes needed. The default search key fails listIndexes and describeIndex over MCP
2. Enable the mcpRoute experimental feature first. Until then /mcp answers feature_not_enabled
3. For hybrid search send hybrid with an embedder name configured in the index settings and a semanticRatio from 0 to 1
4. Writes return a taskUid and run asynchronously. Poll the task before searching for the new documents
5. On 503 too_many_search_requests, wait for the Retry-After header (10 seconds) before retrying