LLM observability
Tracing, cost and quality monitoring for AI features
What it is
LLM observability records what an AI feature actually did in production: the prompt, the retrieved context, the model's response, any tool calls, latency, token cost and user feedback, all linked together as one trace. It's how teams debug a bad answer, notice quality drifting and explain a surprising bill.
Enterprise customers expect a bad AI answer to be investigated like any other incident. They also ask what gets logged, because prompts usually contain their data.
What it takes
About 3–6 engineer-weeks to build in-house, or 1–2 using Langfuse, Helicone, Datadog.
Answer these first
- Engineering: Can we reconstruct exactly what happened for any single answer?
- Engineering: What does each AI feature cost per customer, per month?
- Security: What do traces store, for how long, and who can see them?
- Product: How do users tell us an answer was wrong?
- Finance: Do we pass AI costs on, cap them, or absorb them?
- Support: Can support look up a customer's AI interaction without an engineer?
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