AI Observability in PostHog Web
Contents
The PostHog web app is where you actually read your AI product's traces. Open AI Observability in PostHog and you land on a dashboard of cost, latency, token usage, and error rate, with tabs for traces, sessions, users, tools, and errors alongside it.
From there you can open any trace to see the full conversation – every generation and span in order, with inputs, outputs, model, tokens, and cost on each one. Traces carry the person who triggered them, so you can jump straight to their session replay or any exception the request threw.
What you can do here
- Analyze LLM performance – track cost, latency, tokens, and error rate across models, features, and users.
- Read traces and sessions – follow a single request end to end, or a whole multi-visit session.
- Test prompts in the playground – rerun a prompt against a different model or wording without redeploying.
- Cluster similar traces – group conversations by what users were actually trying to do, so failure patterns stand out.
- Check sentiment – see which conversations left users frustrated, classified automatically.
- Summarize a trace – get a written summary and flow diagram of a long agent run instead of reading every message.
- Analyze traces with PostHog AI – ask PostHog AI, right here in the web app, about spend, latency, and errors across your trace data, and have it write custom parsers for trace formats it doesn't recognize. It's an interaction mode inside the web app, not a separate surface.
Related
- Start with the data model if traces, spans, and generations are new to you.
- Query the same data from your editor over MCP.
- Drive the same things from your own code with the API.
- Score your traces automatically with evaluations.