Analytics and monitoring decision
FullStory
A small team can reproduce a useful subset (capture, storage, replay, basic analytics) for internal use, but FullStory’s scale, server-side Fullcapture pipeline, enterprise SLAs, and StoryAI capabilities are durable advantages that are costly to replicate; pricing is not publicly listed for paid tiers.
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.
What a replacement has to do
- Collect user events from client pages/apps, store and index session event streams, render session replays and heatmaps, provide queryable analytics/filters and dashboards, redact/mask sensitive data for privacy
What it still won’t have
- enterprise-grade scale and real-time ingest pipeline
- the vendor’s StoryAI / AI agents and advanced behavioral models
- polished UI/UX, integrations, and managed dashboards
- formal compliance, SLAs, and enterprise support
- feature-complete add-ons like Workforce, Anywhere, and multi-org management
What remains hard
- Infrastructure at scale
Fullcapture by Fullstory is autocapture, upleveled. It automatically collects and translates customer behavior on both web and mobile apps, giving your team the most precise, comprehensive behavioral data available. Plus, behavioral data de
- Brand trust
The most trusted name in AI analytics
First-year cost
No published price
FullStory 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
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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 behavioral analytics service using Node.js (Express) + React admin UI, Postgres for metadata, S3-compatible object storage for session event streams, and OpenSearch for indexing. Implement: 1) a client-side JavaScript SDK that autocaptures DOM events and sends batched events to a secure ingestion API; 2) ingestion service that assembles per-session streams and writes to object storage while indexing key events into OpenSearch; 3) a session-replay renderer that reconstructs timeline and exposes a playback UI in React; 4) basic analytics endpoints (funnels, simple aggregate metrics, segment filters) and a dashboard UI; 5) privacy masking: configurable selectors to redact data client-side and server-side retention rules. Out of scope: mobile SDKs, advanced AI/ML behavioral models, multi-tenant enterprise features, SSO, and professional services. Include error handling, request validation, and unit + integration tests; provide Docker Compose and deployment docs for AWS (S3, RDS, OpenSearch) and cost notes.
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
- Hard moats found in the evidence-3
- Evidence score61
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 productFullStory homepage
- official pricingFullStory Plans & Pricing
- official productFullstory product tour — Analytics
- open sourcePostHog/posthog
- open sourcehyperdxio/hyperdx
Integrity checks
What held up, and what did not.
