LanceDB vs Upstash Vector API + MCP

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.

Upstash Vector API + MCP

$0.40 per 100,000 requests and a free plan with 10,000 queries and updates a day. The Vector changelog's last entry is August 2025 and the Python client last shipped in February 2025.

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

Upstash Vector API + MCP

1. Connect to mcp.upstash.com/mcp for Vector tools. npx @upstash/mcp-server has none
2. Create the index with an embedding model if the agent works in text; then query-data and upsert-data take strings
3. Give retrieval-only agents the index's read-only token, not the full one
4. Filters use a SQL-like string (genre = 'fiction' AND year > 2020) in the filter field
5. Ignore the FAQ line saying hybrid isn't supported. Pass queryMode HYBRID, DENSE or SPARSE on hybrid indexes