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Apollo vs People Data Labs

Apollo scores higher on the AgentReady, 64/100 against 50/100. They differ on 12 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Apollo. The AI sales platform for smarter, faster revenue growth.

People Data Labs. Talent sourcing platform that builds workforce data, so you don't have to.

Where Apollo is ahead

Apollo passes clear canonical domain and mcp discoverable, and People Data Labs does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds understand: authentication documented and limits / constraints documented. People Data Labs misses those.

And on adopt, agent-compatible signup flow, cli available and mcp integration available. People Data Labs misses those.

Finally, on operate, agent compatibility verified. People Data Labs misses it.

Where People Data Labs is ahead

People Data Labs passes llms-full.txt / full agent docs, and Apollo does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: fast time to first request and copyable quickstart. Apollo misses those.

And on operate, structured, predictable output. Apollo misses it.

What neither does

Both fail structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented, no mandatory sales call, programmatic credential creation, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable. 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. Apollo leads 93 to 80. Apollo misses llms-full.txt / full agent docs; People Data Labs misses clear canonical domain, mcp discoverable.

Understand. Apollo leads 38 to 23. Apollo misses structured api reference, openapi / spec quality, request examples provided, response examples provided, errors and status codes documented; People Data Labs misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt is whether an agent can get a key and make its first successful call without a human in the loop. Apollo leads 70 to 50. Apollo misses no mandatory sales call, programmatic credential creation, fast time to first request, copyable quickstart; People Data Labs misses no mandatory sales call, agent-compatible signup flow, programmatic credential creation, cli available, mcp integration available.

Operate. Apollo leads 56 to 47. Apollo misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable; People Data Labs misses machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, agent compatibility verified.

Pricing

Apollo does not publish a machine-readable starting price and has a free tier. People Data Labs does not publish one and has a free tier.

Signal by signal

Which to pick

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

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