Skene vs traditional product analytics stacks
This page is for teams deciding whether their event data should live in hosted analytics tools like Segment, Amplitude, and Mixpanel, or in their own Supabase, validated by Skene on every pull request.
Who this category view is for
You already understand event tracking and dashboards. The open question is whether you keep shipping events to hosted analytics tools or keep your telemetry in your own Supabase and guard the code that writes it.
Category-level model difference
A traditional stack collects events in your code, ships them to a hosted tool, and charts them there. Your data lives in someone else's cloud, and the tracking is only as good as the instrumentation nobody reviews. Skene keeps your telemetry in your own Supabase and checks, on every pull request, that the code writing those events still matches your schema.
When a hosted analytics stack is the wrong choice
A hosted stack is the wrong fit when you would rather own your event data in your own Postgres, and when your tracking breaks silently because no one reviews the instrumentation a coding agent rewrites.
When Skene is the wrong choice
Skene is not ideal if you are building a data organization whose main output is analysis and experimentation across many internal customers. Skene validates the writes; it does not store or chart events.
Architectural comparison
| Dimension | Skene | Hosted analytics stack |
|---|---|---|
| Where your data lives | Your own Supabase. Skene moves no data and duplicates nothing. | Collected and stored across hosted tools (Segment, Amplitude, Mixpanel). |
| What Skene adds | Checks the event-writing code against your Supabase schema on every PR. | Dashboards and insights that require human interpretation and action. |
| Tracking correctness | Guarded in code review. Drift is flagged on the PR that introduced it. | Implicit. A broken call is found later, when a chart goes flat. |
| Setup | Connect the repo and Supabase, read-only. | Instrument the SDKs and maintain a tracking plan across tools. |
| Ongoing work | Runs on every PR. The baseline updates with the code. | Schema management, integration monitoring, and dashboard updates. |
Relevant comparisons
Dive deeper into specific tool comparisons:
From here, the next step is usually to look at the product overview and a focused use case.
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