Analytics and monitoring decision
Cometly
A technical user can build a workable self-hosted pipeline that captures events, links Stripe revenue, computes simple multi-touch attribution, and sends conversions back to ad platforms, but reproducing Cometly's full product (wide integrations, AI Ads Manager, Agent/MCP, enterprise features, and match-quality tuning) is large and operationally heavier than a single-person project.
Visit website↗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 Cometly alternatives, with the arithmetic →
What a replacement has to do
- Collect pageviews and events, link revenue from Stripe, compute multi-touch attribution and cohort LTV, send paid-conversion events to ad platforms, and display ROAS/cohort dashboards.
What it still won’t have
- 70+ native integrations and quick one-minute connectors
- AI Ads Manager and automated campaign rebalancing
- Agent (natural-language query) and MCP LLM server
- Enterprise onboarding, dedicated solutions engineer, and SLAs
- Conversion API match quality tuning and platform-specific enrichment optimizations
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Cometly does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
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
Build a minimal self-hosted marketing-attribution service using Node.js (Express) + Postgres + TypeScript + React. Include: 1) a client-side pixel script that posts pageviews and click events to an ingestion API; 2) a server ingestion service that deduplicates events, fingerprints clients, and stores raw events in Postgres; 3) a Stripe webhook handler that links subscription/payment events to stored fingerprints/identifiers and computes paid-customer records; 4) a simple multi-touch attribution job (daily cron) that attributes revenue to touchpoints and computes cohort LTV and ROAS; 5) a Conversion API adapter to send hashed PII and fingerprint-enriched paid-customer events to Meta CAPI and Google (configurable endpoints); 6) a React dashboard showing top sources by closed-won ARR, campaign ROAS, and cohort LTV with date filters. Out of scope: full 70+ integrations, AI Ads Manager, LLM Agent/MCP. Require error handling, retries for external API calls, input validation, unit tests for ingestion/attribution logic, and docker-compose for local deployment.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score64
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.
- official productCometly — official product
- official pricingCometly — pricing
- official pricingCometly — Product-Led Growth solution page
- open sourcePostHog repository
- open sourceUmami repository
Integrity checks
What held up, and what did not.
