LanceDB vs Vespa

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.

Vespa

Vespa Cloud publishes hourly unit prices, a 99.5% uptime agreement for paying customers and a trial with $300 of credits and no card. Every call needs an application package deployed first, no OpenAPI file was found in the reviewed documentation, and Vespa.ai says it has no official public MCP server.

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

Vespa

1. Deploy an application package with a schema before feeding or querying. Use vespa deploy and wait for the endpoint to report ready
2. Send a token as Authorization: Bearer only to the endpoint marked Token. The mTLS endpoint needs the client certificate and key
3. Set hits, offset and presentation.summary on queries. hits is capped at 400 by default and the query timeout defaults to 0.5s
4. On 429 from /document/v1 reduce the feed rate and back off. On 507 stop feeding, because the cluster is out of memory or disk
5. Use the condition parameter for test-and-set writes and expect 412 when it fails or the document is missing