Latten

What are the best LLM cost monitoring tools for AI apps?

The best LLM cost monitoring tool depends on the question. If you need traces and model-call analytics, use an LLM observability tool. If you need to know which actor, agent, or data domain drove spend — and what the same AI could reach — use Latten.

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Cost without ownership is only a bill

Token totals are not enough. Teams need to know which actor, model, data domain, and workflow created the spend.

Look for cached and reasoning tokens

A real cost tool should break out input, cached input, output, and reasoning tokens instead of treating every call the same.

Watch for loops and retries

Recoverable spend often hides in stuck loops, repeated context, silent retries, and calls that no one can attribute.

How it works

  1. 1. Instrument the app Wrap model calls and data crossings with @latten/sdk.
  2. 2. Attribute actors Use withActor so cost has an owner.
  3. 3. Review cost by model and domain See where spend concentrates.
  4. 4. Fix the recoverable patterns Cut loops, retries, and oversized context without cutting useful AI usage.

Common questions

Can Latten monitor OpenAI and Anthropic cost?

Yes. @latten/sdk normalizes model usage across common OpenAI, Anthropic, and Gemini response shapes.

Does Latten charge by model call?

No. The cost product is positioned around flat, predictable pricing rather than charging more when you use more AI.

Is cost monitoring the same as AI governance?

No. Cost is one lens. Latten also maps reach, attribution, and PII exposure.

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