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PostHog to Telemetry: a reversible evaluation path
Map a bounded PostHog workflow, preserve the capabilities that remain necessary, and compare both systems over the same closed fixture before changing production coverage.
- 1
Inventory PostHog
PostHog events and properties
- 2
Map one workflow
Named event tables with stable typed columns and explicit account, actor, feature, and release fields.
- 3
Dual-run the fixture
Replay a fixture and compare unique actors, event volume, null handling, and property types.
- 4
Record the decision
Do you need session replay, feature flags, experiments, or deep user-path analysis? If yes, PostHog is likely the stronger fit.
How Telemetry is different
- Telemetry starts with a named event table and DataFusion SQL; PostHog starts with a broad event model and offers both product-analysis views and HogQL.
- Backend, worker, job, API, and agent events are first-class use cases.
- Coding agents can install instrumentation from prompts and skill.md.
When Telemetry is a good fit
- You need to debug APIs, jobs, webhooks, agents, or event pipelines.
- You want raw SQL over structured events.
- You want generated dashboards and queries from your codebase context.
Where each product is strongest
PostHog
- A mature product-analytics surface for trends, funnels, paths, stickiness, retention, lifecycle, and cohorts.
- A much broader suite that also includes session replay, feature flags, experiments, surveys, a data warehouse, error tracking, and more.
- HogQL, SQL visualizations, joins across warehouse sources, and materialized views for teams that want product analytics and a warehouse in one platform.
Telemetry
- A smaller SQL-first surface for custom backend and business events.
- Direct event-table mental model with a lightweight ingestion and query workflow.
- Coding-agent prompts, templates, and recipes designed to leave behind events, reviewed SQL, dashboards, and alerts.
Evaluation checklist
Test the decision with a real workflow
- 1Do you need session replay, feature flags, experiments, or deep user-path analysis? If yes, PostHog is likely the stronger fit.
- 2Is the first requirement a custom backend workflow with a few safe, structured events and SQL? Test Telemetry on that workflow.
- 3Run the same operational question in both products and compare setup time, query readability, result latency, and projected event cost.
Migration path
Plan the query and event migration before changing tools
Inventory the queries, alerts, exports, and retention requirements the current workflow actually uses. Map those requirements to a typed event contract, translate a representative query, and dual-run the same fixture before expanding coverage. Similar operators do not guarantee equivalent null handling, time semantics, or aggregation results.
PostHog workflow
PostHog events and properties
Telemetry mapping
Named event tables with stable typed columns and explicit account, actor, feature, and release fields.
Dual-run validation
Replay a fixture and compare unique actors, event volume, null handling, and property types.
PostHog workflow
HogQL insights, funnels, and cohorts
Telemetry mapping
Reviewed DataFusion SQL recipes, saved queries, and dashboards with documented counting rules.
Dual-run validation
Dual-run one activation funnel and one retention cohort over the same UTC interval.
PostHog workflow
Feature flags and session-oriented product context
Telemetry mapping
Explicit feature assignment and product-outcome events; keep PostHog if its flag, replay, or experimentation workflow remains required.
Dual-run validation
Verify assignment grain and joins before interpreting conversion by variant.
Try the wedge
Start with one backend workflow
Pick an API route, AI workflow, webhook, or job queue. Send structured events and query them before expanding coverage.
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