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Deepset vs Vespa

Vespa scores higher on the AgentReady, 56/100 against 44/100. They differ on 10 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Deepset. Deepset is a company that provides Haystack, an open platform to build, run, and govern AI agents and applications.

Vespa. Vespa.ai develops the Vespa AI Search Platform, a distributed serving engine that unifies retrieval, ranking, machine learning inference, and real-time serving for business-critical AI applications.

Where Deepset is ahead

Deepset passes mcp discoverable, and Vespa does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: mcp integration available. Vespa misses it.

Where Vespa is ahead

Vespa passes clear product positioning, public docs discoverable and llms-full.txt / full agent docs, and Deepset does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: structured api reference and limits / constraints documented. Deepset misses those.

And on adopt, fast time to first request, copyable quickstart and official typescript sdk. Deepset misses those.

What neither does

Both fail openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, no mandatory sales call, agent-compatible signup flow, programmatic credential creation, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified. If your agent needs any of those, you will be building it yourself either way.

Score, pillar by pillar

The AgentReady splits into four pillars, scored separately, because a product can be easy to find and still impossible to adopt.

Discover. Vespa leads 87 to 67. Deepset misses clear product positioning, public docs discoverable, llms-full.txt / full agent docs; Vespa misses mcp discoverable.

Understand is whether an agent can read the docs and work out how the API behaves before calling it. Vespa leads 46 to 23. Deepset misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Vespa misses openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented.

Adopt. Vespa leads 55 to 50. Deepset misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, fast time to first request, copyable quickstart, official typescript sdk; Vespa misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, mcp integration available.

Operate. Both sit at 35/100 here. Deepset misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified; Vespa misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

Deepset starts at $0/mo and has a free tier. Vespa starts at $0.05/hour with no free tier.

Signal by signal

Which to pick

Vespa clears more of the signals an agent needs, so it is the safer default for unattended use. Full profiles: Deepset and Vespa. Alternatives to each: Deepset, Vespa.

An agent can fetch this as data: POST /v1/compare {"slugs": ["deepset", "vespa"]}