PostHog

PostHog is the rare product analytics platform that took the all-in-one bet and actually shipped it. Sessions, events, replays, flags, experiments, surveys, errors, LLM traces, and a managed data warehouse, all sharing one identity graph and one billing line. For mid-market teams running four overlapping SaaS contracts, the math gets interesting fast.

What PostHog Does

PostHog ships as a single platform with overlapping products that share the same event store. Every product reads from the same user and group identity, which is why teams stop reconciling four sets of numbers.

• Product Analytics, web analytics, funnels, paths, retention, and SQL-backed insights on the same event stream.
• Session Replay with console logs, network requests, and rage-click detection on web and mobile.
• Feature Flags and Experiments with cohort targeting, multivariate tests, and rollback rules.
• Surveys triggered by behaviour, cohort, or release, with NPS, CSAT, and open-text response capture.
• LLM Analytics for AI-native products, capturing prompts, completions, cost, latency, and user reaction in one trace.
• Error Tracking with stack traces tied back to the user session that produced them.
• Managed Data Warehouse with 120+ source connectors, SQL editor, and BI views for teams without a dedicated data stack.

PostHog AI and LLM Observability

PostHog AI is a working co-pilot inside the platform. Ask it for a funnel in plain English and it writes the query, picks the right events, and hands back a saved insight. The LLM analytics product is the other half of the story. Every model call, prompt, completion, token cost, and downstream user action lands in one trace, linked to the same person profile that powers the rest of the platform. For teams building AI features, that link between model behaviour and product behaviour is the whole game.

Automations We Build with PostHog

PostHog catches signal. It does not act on it by default. The automations sit on top of the events, the replays, and the flags. This is the part most teams skip, and it is where the leverage sits.

• Rage-click and error-spike alerts piped into a Slack triage channel with the replay link and user context attached.
• Drop-off detection in onboarding funnels that auto-files a Linear ticket with the affected cohort and a five-second replay clip.
• Feature flag kill-switches that read PostHog error rates and disable a rollout automatically when a threshold is breached.
• LLM cost and latency reports generated weekly by an AI agent that reads PostHog traces and posts a Slack summary every Monday.
• Stripe revenue stitched to PostHog person profiles so cohort retention is measured in dollars, not events.
• Survey responses with negative sentiment routed to a CS owner with the prior 24 hours of session context attached.

Why Teams Choose PostHog

• One billing line, one identity graph, one source of truth across analytics, replay, flags, and LLM observability.
• Usage-based pricing with generous free tiers, so cost tracks growth instead of seat count.
• Open-source core with the option to self-host on your own infrastructure when compliance demands it.
• Engineering-first culture and changelog cadence that ships meaningful product every week, not every quarter.
• Native LLM analytics that mid-market AI teams cannot find at the same price point anywhere else.

PostHog integrates natively with Slack, Linear, Salesforce, HubSpot, Stripe, Segment, BigQuery, Snowflake, and 120+ other sources and destinations through its CDP layer. Pricing starts free and scales as usage grows, with platform tiers for teams that need RBAC, SSO, and dedicated support. That is the build we do: take PostHog from a data layer to an operating system that triggers Slack alerts, files Linear tickets, kills bad rollouts, and writes the weekly report for you. Talk to Moonira when you want PostHog actually running your product feedback loop.