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PostHog

What it is

An all-in-one product OS that includes product analytics, session replay, feature flags, and A/B testing. In July 2026, it serves as a critical observability hub for Gemma 3 and other frontier models, providing a comprehensive suite for monitoring user behavior and system performance in real-time.

What problem it solves

It helps teams understand how users interact with their applications and allows for data-driven product decisions. For AI teams, it provides visibility into how LLM responses affect user conversion and retention, with deep integration for MCP 3.0 based tool-calling traces.

Where it fits in the stack

Category: Process & Understanding / Product Analytics. It serves as the primary observability layer for user-facing applications and agentic workflows, sitting alongside Agentic Session Orchestration components.

Typical use cases

  • Full-Funnel Analytics: Tracking user behavior from the first click to the final AI-generated response.
  • A/B Testing AI Models: Comparing the performance and user satisfaction of different LLMs (e.g., Gemma 3 vs Claude 4.8) using feature flags.
  • Session Replay: Watching recordings of users interacting with AI agents to identify friction points and hallucination impacts.
  • Conversion Tracking: Measuring how AI features impact key business metrics like signups or purchases.

Strengths

  • All-in-One: Combines analytics, session recording, and feature flagging in a single platform.
  • AI Observability Dashboard: Specialized views for cost, latency, and error rates across different LLM providers via MCP 3.0.
  • Integrated Session Recordings: Visualize UI changes triggered by LLM responses directly in the trace timeline.
  • HogQL: Powerful, SQL-like query language for advanced data analysis and custom dashboarding.

Limitations

  • Indexing Latency: In high-volume environments, there can be a slight delay before traces appear in the dashboard.
  • Complexity: The sheer number of features can make the learning curve steeper for new users compared to point solutions.

When to use it

  • When you want to see the "big picture" of how AI features affect your overall product metrics.
  • For teams that need built-in A/B testing and feature flagging to roll out AI changes safely.
  • When you want to link specific AI traces back to actual user session recordings.

When not to use it

  • If you only need deep, low-level AI engineering traces and don't care about broader product analytics.
  • For extremely simple applications where a basic log aggregator would be enough.

Getting started

Installation

pip install posthog

Basic Capture

import posthog

posthog.project_api_key = '<ph_project_api_key>'
posthog.host = 'https://us.i.posthog.com'

posthog.capture('user_id', 'llm_interaction', {
    'model': 'gemma-3-27b',
    'prompt_tokens': 150,
    'completion_tokens': 200,
    'total_cost': 0.005,
    'user_feedback': 'helpful'
})

CLI examples

posthog-cli login

Authenticates the CLI with your PostHog instance: 

posthog-cli login

posthog-cli query

Executes a HogQL (SQL) query against your PostHog data: 

posthog-cli query "SELECT event, count() FROM events GROUP BY event"

posthog-cli capture

Sends a manual event for testing: 

posthog-cli capture --distinct-id user_123 --event test_event --properties '{"source": "cli"}'

API examples

Python (AI Trace Instrumentation)

PostHog supports a structured trace API for LLM monitoring (v2026.7+):

import posthog

# Capture a full LLM generation trace with MCP context
posthog.capture('user_123', '$ai_generation', {
    '$ai_model': 'gemma-3-27b',
    '$ai_provider': 'ollama',
    '$ai_input_tokens': 150,
    '$ai_output_tokens': 200,
    '$ai_latency': 1.2,
    '$ai_cost': 0.0,
    '$ai_trace_id': 'trace-uuid-456',
    '$ai_input': 'Summarize the latest sales data.',
    '$ai_output': 'Summary: Sales are up 20%...',
    '$mcp_protocol_version': '3.0'
})

JavaScript (Feature Flag Evaluation)

import posthog from 'posthog-js'

posthog.init('<ph_project_api_key>', { api_host: 'https://us.i.posthog.com' })

// Check if a new AI model feature flag is enabled
if (posthog.isFeatureEnabled('use-gemma-3-model')) {
    // Use Gemma 3
} else {
    // Use fallback model
}

Sources / References

Contribution Metadata

  • Last reviewed: 2026-07-21
  • Confidence: high