Comparison

Agentry vs PostHog

Both run on the same query engine (HogQL) and cover the same surface: product analytics, session replay, feature flags, surveys, A/B tests. PostHog is built around a polished web UI for analysts; Agentry exposes those same capabilities through an AI agent in your editor, with first-class error monitoring layered in. Pick the interface model that matches how your team works.

TL;DR

Pick PostHog if

  • You have a dedicated analytics team that lives in PostHog's UI all day
  • You need data warehouse sync (Snowflake, BigQuery, Redshift)
  • You rely on web autocapture and don't want to instrument events explicitly
  • Stakeholders need to self-serve dashboards without involving an engineer

Pick Agentry if

  • The same person who builds the product also queries the data
  • You want errors and analytics in one product, queryable together
  • Your "analyst" is increasingly your AI agent
  • You don't want to operate PostHog yourself (Agentry manages it)

Feature comparison

Capability PostHog Agentry
Product analytics (HogQL) Yes — native Yes — same HogQL, queried via agent
Error monitoring Basic add-on First-class (cases, fingerprints, suppressions)
Session replay Yes — native Yes — PostHog-backed, included
Feature flags, cohorts, surveys, A/B tests Yes Yes — same surface via MCP
Data warehouse sync Yes (Snowflake, BigQuery, Redshift) No
Web autocapture Yes No — explicit instrumentation only
Investigation surface Web UI (mature, broad) Agent in your editor (MCP)
Self-hosted option Yes No — managed only
SDK install required Yes (posthog-js, posthog-node, etc.) No — ~25 lines of fetch
Deploy attribution No Yes — first-class signal
Pricing model Free tier + per-event after threshold Free during beta, usage-based later

When PostHog is the right call

PostHog is the right tool when product analytics is a job your organisation does, not a question your engineers ask in passing. If you have product managers, growth analysts, or marketers who need to build funnels, slice cohorts, and publish dashboards themselves — without filing a ticket — PostHog's UI is what they want. It's been polished over years specifically for that workflow, and self-service is genuinely good.

PostHog also wins when you need its data platform features: warehouse sync to Snowflake / BigQuery / Redshift, web autocapture for marketing sites where you can't reasonably instrument every click, and the self-hosted option for teams with data residency requirements. Agentry doesn't have any of those.

If your stakeholders need to log into a product analytics tool and get answers without engineering involvement, that's PostHog. Routing those questions through an agent in someone's editor is the wrong shape.

When Agentry is the right call

Agentry is the right tool when the person asking the analytics question is also the person writing the code — typical of small-to-medium engineering teams where there is no separate analyst role. In that workflow, opening a dashboard tab is friction. You want to ask "how many users hit the new pricing page and then bounced?" in the same Cursor / Claude Code conversation you're already in, get the HogQL result, and have the agent draft the follow-up A/B test.

Agentry uses PostHog under the hood for analytics, replay, and flags — so the underlying capability surface is the same. The difference is the interface: MCP tools instead of a web UI, and errors as a first-class signal in the same query plane (PostHog treats errors as a separate add-on, which makes "did the deploy that caused this error also cause the funnel drop?" awkward in their model).

Agentry also handles the PostHog operational layer for you — provisioning, project isolation, key management. If you'd rather not run PostHog yourself and don't need warehouse sync, that's a real saving.

Migrating from PostHog

Agentry's /v1/analytics/<project_id>/ endpoint accepts PostHog-shaped event payloads, and /v1/track/ is a drop-in alias. Your existing event names and property keys keep working — distinct_id semantics are identical. You don't have to migrate all at once; dual-write to both for a week, compare what you get, then cut over.

These recipes cover the PostHog use cases most product teams care about:

Try Agentry against your real data.

Dual-write for a week. Compare. Switch if it's better. The agent handles install — you just paste one prompt.

Install https://agentry.sh/agentry.md and set it up
Agentry vs Sentry → Agentry vs Mixpanel → Agentry vs Datadog → Agentry vs Amplitude → Agentry vs LaunchDarkly → Agentry vs LogRocket → Agentry vs Highlight.io →