Elasticsearch vs Vespa

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.

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.

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

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