PostHog LLM Analytics
LLM observability inside PostHog's all-in-one product analytics suite.
About
PostHog LLM Analytics brings prompt and trace observability into the same platform product teams already use for event analytics, feature flags, session replay, and experiments. Instead of wiring up a separate LLM tracing vendor, teams instrument their model calls and see traces, costs, and quality metrics next to the rest of their product data.
In 2026 the product gained cluster analysis that groups LLM traces by behavioral patterns automatically, a prompt playground for iterating on prompts, and prompt management for versioning what ships to production. Because it lives inside PostHog, teams can correlate an AI feature's usage and errors with the experiments and funnels that describe how users actually behave. PostHog itself is open source and self-hostable, with a managed cloud offering.
What it can do
- Trace LLM requests and responses with token and cost tracking
- Cluster analysis that groups traces by behavioral patterns automatically
- Prompt playground and versioned prompt management
- Unified view of LLM usage alongside events, flags, experiments, and replays
Setup
Create a PostHog account at posthog.com or self-host the open-source app, then follow the LLM analytics docs to instrument model calls.
Prerequisites
An application that makes LLM calls; a PostHog project or self-hosted instance.
Use cases
Works with
Trust score — 9/10
Scored 9/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 97; Trending score 88/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.