Analytics and monitoring decision

PostHog

A small engineering team can build a useful, limited analytics replacement (event ingestion, storage, aggregates, dashboards); PostHog's integrations and brand scale are valuable but not impossible for a competent team to match for narrow use cases, and pricing/support advantages remain reasons some teams will prefer the hosted product.

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Subscription$250/month ✓ verified
Initial build30 hours
Monthly upkeep8 hours + $35
Evidence2/3 runs agree

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All PostHog alternatives, with the arithmetic →

What a replacement has to do

  • Collect client events, store them, run aggregations/queries, and display simple dashboards/insights.

What it still won’t have

  • Session replay recordings and replay UX
  • Managed warehouse features and 120+ source/destination connectors
  • Usage-based billing, SLA'd support, and enterprise features (RBAC, dedicated proxies)
  • AI features (PostHog AI, AI Observability, Replay Vision) and built-in agent automation
  • Scale and operational reliability without SRE investment

What remains hard

  • Integration maintenanceData from 120+ external sources like Stripe, Postgres, and HubSpot
  • Brand trustJoin 500,000+ teams already shipping with PostHog.
Read the build prompt

First-year cost

Keep paying

Paying ischeaper in year one.

On cash alone, building overtakes the subscription at 1 seat.

Paid seatsseats

Money you would actually spend

Keep paying

Subscription price × seats × 12

Build it

AI build APIs + hosting

Time you would spend

What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal self-hosted product-analytics stack using Node.js (Express) for ingestion, ClickHouse (or Postgres) for event storage, Redis for short-term dedupe, a background worker in Python/Node to pre-aggregate time-series/funnels, and a React dashboard. In scope: event ingestion API with validation, storage schema and retention, hourly aggregation jobs, basic auth and single-project UI, dashboards for event counts, funnels, and simple cohort queries, and a small metrics endpoint for usage. Out of scope: session replay recording/player, AI features, managed warehouse connectors, multi-tenant billing, and enterprise RBAC. Include error handling, request validation, retries for failed writes, retention cleanup jobs, and unit tests for ingestion and aggregation logic. Provide deployment scripts for a single VPS (Docker Compose) and a one-page README with run and backup instructions.
How we checked5 sources · 2/3 runs agreed · evidence score 89

How the score was reached

  • Build verdict base78
  • An open-source build was found+5
  • 5 cited sources+3
  • Price verified on pricing page+3
  • Evidence score89

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 5

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 2 moats quoted from the page