Last reviewed . Product packaging and pricing can change; verify the linked vendor sources before buying.
Try PostHog alongside Telemetry
Choose one PostHog workflow and test both systems with the same fixed dataset. Check which capabilities you still need before changing production monitoring.
- 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 stores named event tables and uses DataFusion SQL. PostHog has a shared event model, product-analysis views, and HogQL.
- Query events from your backend, workers, jobs, APIs, and agents.
- 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
- Product analytics for trends, funnels, paths, stickiness, retention, lifecycle, and cohorts.
- Session replay, feature flags, experiments, surveys, a data warehouse, and error tracking alongside product analytics.
- HogQL, SQL visualizations, joins across warehouse sources, and materialized views for teams that want product analytics and a warehouse in one platform.
Telemetry
- SQL queries over custom backend and business events.
- Send a JSON event, then query its table with SQL.
- Coding-agent prompts and templates for adding events, SQL queries, dashboards, and alerts. Recipes include queries you can check against sample results.
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
List the queries, alerts, exports, and retention you use today. Define the event fields they need and translate one query. Run both systems with the same test data. Check null handling, timestamps, and aggregates before moving more queries.
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 one workflow
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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