Elasticsearch vs LanceDB

Elasticsearch

Keyword, dense and sparse vector, and hybrid search with rank fusion sit behind one OpenAPI-described REST API, with API keys scoped to indices and privileges. The status page lists more than 20 incidents marked major since July 2026, and Elasticsearch 9.5.0 returned incorrect results for some queries without raising an error.

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.

Elasticsearch

1. Send Authorization: ApiKey <key> to the project's Elasticsearch URL. The MCP server is on the Kibana URL at /api/agent_builder/mcp
2. Create the MCP key with feature_agentBuilder.read and only the index patterns needed, or the endpoint answers 403
3. On 429, wait and retry with exponential backoff. Do not set short client timeouts, because a retried request joins the back of the queue
4. Page past 10,000 hits with search_after, not from and size
5. Tool calls made directly through the Tools API skip Agent Builder's confirmation prompts, so restrict the key instead

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