Observability
Logging, metrics, and tracing for LLM calls so you can debug, audit, and optimize cost.
Last updated: April 26, 2026
Definition
LLM observability captures three things per call: input (full prompt + tools), output (response + tool calls), and metadata (model, latency, tokens, cost, success/error). Without it, debugging an agent that "sometimes does the wrong thing" is impossible. Production stacks usually combine: a dedicated LLM platform (Langfuse, Helicone, Arize Phoenix) for traces, plus standard observability (CloudWatch, Datadog, Sentry) for infrastructure. Cost tracking is a must. Surprise bills happen otherwise.
When To Use
Set this up on day one. Adding observability after a production incident is too late.
Building with Observability?
I've shipped this pattern in real production systems. If you want a second pair of eyes on your architecture, that's what I do.