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Datadog vs Hyperdx

Datadog scores higher on the AgentReady, 48/100 against 43/100. They differ on 15 of the 41 signals. Which ones decides whether an agent can adopt them without a person watching.

What each one is

Datadog. Datadog is an observability platform company named a Leader in the Gartner Magic Quadrant for Observability Platforms

Hyperdx. An open source observability platform that unifies session replays, logs, traces, metrics and errors.

Where Datadog is ahead

Datadog passes clear product positioning, llms.txt published, mcp discoverable and machine-readable metadata, and Hyperdx does not. That is discover, whether an agent can find the product at all without being told it exists.

It also holds adopt: cli available and mcp integration available. Hyperdx misses those.

And on operate, rate-limit behavior predictable and observable execution. Hyperdx misses those.

Where Hyperdx is ahead

Hyperdx passes pricing understandable, and Datadog does not. That is understand, whether an agent can read the docs and work out how the API behaves before calling it.

It also holds adopt: self-service signup, no mandatory sales call, free trial or free allowance, fast time to first request, official typescript sdk and official python sdk. Datadog misses those.

What neither does

Both fail llms-full.txt / full agent docs, structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented, programmatic credential creation, structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, 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 is whether an agent can find the product at all without being told it exists. Datadog leads 93 to 53. Datadog misses llms-full.txt / full agent docs; Hyperdx misses clear product positioning, llms.txt published, llms-full.txt / full agent docs, mcp discoverable, machine-readable metadata.

Understand. Hyperdx leads 23 to 15. Datadog misses structured api reference, openapi / spec quality, authentication documented, pricing understandable, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented; Hyperdx misses structured api reference, openapi / spec quality, authentication documented, request examples provided, response examples provided, errors and status codes documented, limits / constraints documented.

Adopt. Hyperdx leads 70 to 35. Datadog misses self-service signup, no mandatory sales call, programmatic credential creation, free trial or free allowance, fast time to first request, official typescript sdk, official python sdk; Hyperdx misses programmatic credential creation, cli available, mcp integration available.

Operate. Datadog leads 47 to 24. Datadog misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, agent compatibility verified; Hyperdx misses structured, predictable output, machine-readable errors, retry behavior documented, idempotency support, rate-limit behavior predictable, observable execution, agent compatibility verified.

Pricing

Datadog does not publish a machine-readable starting price. Hyperdx starts at $20/mo and has a free tier.

Signal by signal

Which to pick

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

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