Latten

How is Latten different from Datadog LLM observability?

Datadog is strongest when the team wants LLM observability inside a broad enterprise monitoring platform. Latten is narrower and more specific: one observe-only graph for company AI cost, data reach, PII exposure, actor attribution, and blast radius.

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Company-AI graph, not a metrics tab

Latten is shaped around the question security and finance ask together: who did the AI act for, what did it reach, and what did it cost?

Values-free posture

Latten is explicit that PII is reported as types and counts, never values, and that the SDK is observe-only.

Agent-installable

@latten/installer lets Codex or another coding agent provision, scan, instrument, verify, and return a receipt through a reviewable PR.

How it works

  1. 1. Add the installer MCP Let the coding agent provision if no token exists.
  2. 2. Open the PR Review the @latten/sdk instrumentation before merging.
  3. 3. Run real traffic Let crossings arrive from the app.
  4. 4. Read the receipt See cost, reach, PII types, and attribution.

Common questions

When is Datadog the better fit?

Datadog is a better fit when the team wants all LLM metrics inside an existing Datadog stack.

When is Latten the better fit?

Latten is a better fit when the primary question is AI authority: cost plus what the AI can reach.

Does Latten require a platform migration?

No. It is observe-only instrumentation and can coexist with existing monitoring.

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