Sentry vs PostHog: which one does your team need?

Two tools keep coming up when a team asks how to see what is happening in a live application: Sentry and PostHog. They overlap enough to confuse buyers, and they started from different questions. Disclosure: Kastling uses PostHog for analytics on its own website. Features and pricing below were read from each vendor's official pages in October 2026, and both vendors change them often, so check the current pages before you buy.

Sentry vs PostHog: which one should you use?

Use Sentry when developers need to find and fix errors and performance problems. Use PostHog when product teams need to understand what users do and whether a change helped. Each tool has expanded into the other's area, so the choice is about which one is deeper for your main question.

What is each tool built to do?

Sentry

Sentry started as error monitoring. It groups similar events into issues based on a fingerprint, so one bug appears as one issue, and it shows how often the problem happens and how many users it affects. Suspect commits point to the most recent code change in the stack trace. Its product overview now lists tracing, session replay, profiling, logs, cron monitoring, uptime monitoring, AI agent monitoring and user feedback.

PostHog

PostHog started as product analytics. It analyzes the events your product sends through trends, funnels, paths, stickiness and lifecycle insights, and it adds session replay, feature flags, experiments, surveys, error tracking, AI Observability, logs and distributed tracing in one project. The common thread is that each product shares the same user and event data.

Where they overlap

PostHog's error tracking turns exceptions into issues that you can assign and resolve, and each issue includes the affected user's session replay. Sentry's Session Replay connects errors to a replay timeline, and it links rage clicks, dead clicks and user feedback to replays. Both now cover LLM features. The overlap is real, but depth follows origin. Sentry's list includes profiling, cron and uptime monitoring that PostHog's error tracking page does not mention, and PostHog's analytics, flags, experiments and surveys have no counterpart in Sentry's overview.

How do errors, stack traces and performance tracing compare?

Both tools need the same groundwork for readable errors. Browser code is usually minified, so a stack trace is useless until the tool can map it back to your source. Sentry says that to get readable stack traces you need to upload your source maps, and it offers a setup wizard, plugins for webpack, Rollup, Vite and esbuild, and a command-line tool. PostHog's error tracking uses a CLI to upload source maps and debug symbols and lets you tag exceptions with the release and commit that introduced them. Budget an afternoon to wire this into your build pipeline, because an error tool without source maps is much less useful.

On performance, Sentry's Trace Explorer follows a request across your frontend, backend and other services. A span is one operation, such as a database query or an HTTP request, and you can group spans to find slow queries. Sentry samples spans to control volume and extrapolates the results. PostHog's distributed tracing works with any OpenTelemetry client, which means your services send spans using standard OpenTelemetry libraries, and its Node.js and Python SDKs can create spans themselves. On the documentation alone, Sentry has the broader performance toolset for a backend-heavy team, while PostHog's tracing keeps spans in the same project as replays, errors and analytics.

How do session replay, analytics, flags and surveys compare?

Session replay exists in both. PostHog's replay plays sessions back like a recording, with a synced panel for console logs, network requests and errors, and recording rules that sample sessions or gate them by URL or feature flag. Sentry's replay is framed around debugging, showing what a user did before, during and after a bug.

Product analytics, experiments and surveys are PostHog's territory. Experiments randomize users into variants, track exposure and run Bayesian or frequentist statistics, and each experiment is backed by a feature flag. Surveys collect NPS, product-market fit and churn answers, targeted by the same events, properties and flags. Sentry documents integrations with flag providers such as LaunchDarkly, Statsig and Unleash, and it treats feedback as a bug-report widget connected to errors and replays.

How do pricing, hosting and data location compare?

Both are priced by usage, so cost tracks volume. On its pricing page, Sentry offers a free Developer plan for one user, a Team plan at $29 a month billed monthly or $26 billed annually, and a Business plan at $89 a month billed monthly or $80 billed annually. Enterprise is custom. Usage is metered across errors, spans, replays, logs and other products. The included quotas listed there are 5,000 errors on Developer and 50,000 on Team and Business, with 5 million spans and 50 replays on all three.

PostHog's pricing is usage-based per product, billed monthly, with a free monthly allowance on each. The page lists 1 million analytics events, 5,000 session recordings, 1 million feature flag requests, 1,500 survey responses, 100,000 error tracking exceptions and 100,000 AI Observability events, and the base plan has no platform fee, though optional Platform Packages carry flat monthly fees. The units differ, so you cannot compare the two free tiers line by line. Estimate your own monthly events, recordings and spans first.

On hosting, Sentry's docs say self-hosted Sentry comes with no guarantees or dedicated support and needs at least 4 CPU cores, 16 GB of RAM plus 16 GB of swap and 20 GB of disk. Some features, including Seer and its AI features, and the choice of data location, are unavailable there. PostHog calls self-hosting officially unsupported, offers no customer support for it, and recommends its cloud. For a 20 to 200 person company, running either one yourself adds upgrades, backups and capacity planning to someone's job, and that cost rarely beats the cloud plan.

For data location, Sentry offers the US and the EU, chosen when the organization is created and not changeable afterward. PostHog Cloud has US and EU regions, and its EU region is hosted in Frankfurt. If contracts require data to stay in a region, settle this before instrumenting anything.

Which should you pick for your situation?

Match the tool to the question your team asks most often.

• Your pain is bugs, slow pages and outages. Choose Sentry. Grouped issues, release tracking and tracing are built for this.
• Your pain is that signups, orders or renewals drop and nobody knows why. Choose PostHog. Funnels, replay and experiments are built for this.
• You are a small team with one app and no dedicated analyst. Start with one tool. PostHog's free allowances include errors, replay and analytics in one place, and Sentry is the better start if your app is mostly backend services and someone is on call.
• You run an API with little user interface. Choose Sentry, because tracing and profiling matter more than funnels.
• You need to test a pricing page or onboarding change. Choose PostHog, because experiments and feature flags are the point.
• You have a regional data requirement. Check the region options above for both, then confirm what each tool captures before you turn on replay.

When should you run both?

Run both when engineering and product each have recurring questions and the volume justifies two bills. Send the same user identifier to each tool, so that a drop in a PostHog funnel can be matched to a Sentry issue for the same people. Use only one of them for session replay. Two recorders on every page add weight to the browser and double the privacy review.

Illustrative example: Consider a 60-person company whose customer ordering portal was built by five developers and is watched by two product managers. After a release, completed orders fall. A PostHog funnel shows the drop at the payment step for one browser family. A Sentry issue created by the same release shows a type error in the payment form, with a readable stack trace pointing at one changed file. Neither tool alone would have given both halves of the answer, and the fix ships the same day.

What about monitoring AI features?

Both vendors now cover LLM features. Sentry's AI agent monitoring traces agent workflows, LLM calls, tool executions, token usage and errors. PostHog's AI Observability records the prompt, response, token counts, cost and latency of each call, links traces to the user and their replay, and supports evaluations of output quality.

These tools show what happened in production, and they do not decide what good looks like. Decide that first, as covered in monitoring AI in production, and test before launch with the approach in testing AI before production.

How Kastling approaches monitoring a custom application

Kastling would treat monitoring as part of the build and not as a launch-week addition. That means agreeing what a failure looks like for the people using the application, naming who is alerted and who decides what happens next, and choosing the tooling after those answers. It would connect to the systems you already run and test against real business scenarios before release, with optional maintenance afterward under a separate agreement. This sits within Custom Solutions.