Analytics and monitoring decision
Statsig Pro
A competent engineer can build a narrow replacement (flags + basic A/B analysis) using OSS prior art (e.g., GrowthBook), but reproducing Statsig's scale, advanced stats features, session replay, and enterprise integrations would be costly and time-consuming.
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.
Code Statsig Pro publishes itself
Not a way out of the subscription — these are the vendor’s own repositories. Worth a look for how they build, and for anything you would have to integrate with.
What a replacement has to do
- Evaluate feature flag rules in apps, collect exposure events, compute simple A/B test metrics, show results in a dashboard.
What it still won’t have
- Massively scalable infra and SLA-backed uptime
- Warehouse-native deployment and managed real-time logging
- Advanced statistical features (CUPED, interaction detection, multi-arm bandits)
- Session replay and 3rd-party integrations out of the box
- Enterprise features (SSO, RBAC, HIPAA eligibility, priority support)
What remains hard
- Infrastructure at scale
1+ Trillion Events processed per day
- Infrastructure at scale
99.99% Infra uptime for API & Console
- Infrastructure at scale
Massively scalable infra
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 1 seat.
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 feature-flag + experimentation service using Postgres, Node.js (Express) for APIs, a React admin UI, and a small worker (Node) for aggregations. In scope: (1) a flag evaluation endpoint and lightweight JS/browser SDK that returns treatment/config; (2) an ingestion API that records exposures and custom events into Postgres; (3) a stats service that computes difference-in-means with optional sequential p-value correction and exposes JSON results; (4) a React admin to create flags, assign experiment variants, and view basic charts (treatment vs control for 1 metric, cohort filter). Out of scope: session replay, multi-arm bandits, warehouse-native integrations, enterprise SSO/SCIM, and trillion-event scale. Include input validation, error handling, basic unit tests for SDK and stats calculations, Docker Compose for local deployment, and a README with setup and run instructions.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 4 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- 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 · 4
Every page the run actually retrieved.
- official productStatsig home
- official pricingStatsig pricing
- official docsProduct Management role page
- open sourcegrowthbook repo
Integrity checks
What held up, and what did not.
