Sentry

Know your app is broken before your users tell you

Developer Tools 4.6 / 5 Free — 5K errors/mo Updated Feb 2026

Quick Verdict

Sentry is the standard error monitoring tool for Indian engineering teams — install it in 30 minutes, get instant visibility into every unhandled exception, crash, and performance regression in your production application. The free tier (5,000 errors/month) is sufficient for most early-stage Indian apps. Sentry earns its paid tier when you need higher error volumes, performance monitoring (tracking API response times and slow database queries), and session replays — video recordings of exactly what the user did before the crash. For product managers, Sentry bridges the gap between analytics drop-offs and the specific technical errors causing them.

Error Detection
4.8
Stack Traces
4.8
Setup Speed
4.6
Performance Mon.
4.2
Free Tier Value
4.1

What is Sentry?

Sentry is an application monitoring platform founded in 2012 in San Francisco, originally built as an open-source Python error logger. It has grown into a full-stack observability tool covering error monitoring, performance tracking, session replay, and profiling — available as a cloud service and self-hosted open-source. 4+ million developers globally use Sentry, and it is deeply embedded in the Indian startup engineering stack.

The core value: Sentry captures unhandled exceptions and crashes automatically, providing the exact stack trace, the user's environment (device, OS, app version), the breadcrumbs (every action they took before the crash), and the frequency of occurrence — without engineers needing to reproduce the bug. A support ticket that says "my app crashed during payment" arrives in Sentry as the exact line of code that failed, the API response that triggered it, and confirmation that 847 other users hit the same issue in the last 24 hours.

For Indian product managers, Sentry connects the "what" of analytics (users are dropping at the payment step) to the "why" of engineering (3% of Android users on OS 11 hit a NullPointerException in PaymentService when the Razorpay callback returns a null merchant ID). That specificity is what turns a vague user experience problem into an actionable bug fix with a priority and an owner.

Key Features

Error Monitoring

Captures every unhandled exception with full stack trace, user context, device info, and breadcrumbs. Groups similar errors automatically — instead of 10,000 individual events, you see "NullPointerException in PaymentService: 847 users affected." Engineers fix what matters most, not what arrived first.

Performance Monitoring

Track API response times, database query durations, and frontend page load performance. Distributed tracing follows a request from frontend through API to database and third-party services. For Indian fintech teams, catching a slow Aadhaar OTP API before it affects 100K users is the difference between a planned fix and a P0 incident.

Session Replay

Video replay of exactly what the user did in the browser before an error — mouse movements, clicks, scrolls, network requests. When a user says "the app just stopped working during KYC," session replay shows the exact sequence and the error that triggered it. Cuts support investigation time from hours to minutes.

GitHub Integration

Links errors to the exact commit that introduced them — "this error first appeared after commit abc123 was deployed at 3:42 PM." Enables "suspect commits": Sentry identifies the likely cause without manual investigation. For Indian teams deploying multiple times per day, this tracing is invaluable for rapid root cause analysis.

What PMs Should Know About Sentry

How to use Sentry without touching code

Ask your engineering team for a read-only Sentry account. You don't need to understand the code — look at three things:

Error frequency by feature: Filter errors by the URL or page your feature lives on. A 20% error rate on /payment/confirm that didn't exist last week means something broke in the last deployment.

Affected user count: Sentry shows how many unique users hit each error. "127 users affected by checkout crash" is a business conversation, not just a technical one — it should immediately reprioritise what engineering works on next.

Error spikes after releases: Check Sentry immediately after every production deployment. A spike in new errors within 30 minutes of a deploy is a leading indicator — catch it before user complaints arrive.

Best For

  • Every Indian engineering team shipping to production — Sentry is table stakes for operations
  • Indian fintech teams where payment and KYC errors have direct revenue impact
  • Product managers wanting to connect analytics drop-offs to specific technical errors
  • Teams using session replay to understand user issues faster than traditional support
  • Engineering teams doing continuous deployment who need post-deploy error monitoring

Pricing

Sentry charges per error event volume. USD billing — 18% GST reverse charge for Indian companies. Self-hosted open-source is free on your own infrastructure.

Free

Rs 0

5,000 errors/month, 10K performance transactions, 50 session replays. Sufficient for Indian seed-stage apps. Most teams with under 50K MAU and low error rates stay free for 12+ months. The error quota resets monthly — manage it with alert volume rules.

Business

~Rs 7,500/mo

$80/month base. Custom retention, advanced permissions, SSO, custom dashboards. For Indian startups with 10+ engineers where Sentry becomes core infrastructure — multiple projects, granular permissions, compliance-grade data retention.

Self-hosting option: Sentry is fully open-source and can be self-hosted on your own servers. For Indian teams with data sovereignty requirements — regulated fintech, healthtech — self-hosting on AWS Mumbai (ap-south-1) eliminates the USD billing issue and keeps error data within India. Engineering cost to maintain self-hosted Sentry is approximately 2–4 hours per week.

Pros and Cons

Pros

  • 30-minute setup across all major languages
  • Full stack trace with user context and breadcrumbs
  • GitHub integration traces errors to specific commits
  • Session replay shows exact user journey before crash
  • Open-source and self-hostable for data sovereignty
  • Slack alerts create real-time incident awareness

Cons

  • Free 5K error quota fills fast during incidents
  • USD billing + 18% GST reverse charge
  • Noisy without alert configuration — configure before inviting team
  • Performance monitoring adds cost on high-traffic apps
  • Self-hosting requires ongoing engineering maintenance

Getting Started with Sentry

  1. Configure alert rules before inviting your team — Sentry's default "alert on every new error" creates noise overload immediately in production. Before your team starts relying on Sentry, set rules: alert only when an error affects 10+ users or occurs 100+ times in one hour. Connect Sentry to your #incidents Slack channel. Teams that skip this step end up with engineers muting Sentry notifications within a week — defeating the purpose of monitoring entirely.
  2. Add user context immediately after login — Sentry captures technical context automatically, but you need to add business context. Call Sentry.setUser() after login with at minimum user ID, email, and plan tier. This transforms "500 users hit this crash" into "500 Premium plan users hit this crash" — which changes the business priority of the fix entirely. Missing user context means every Sentry investigation starts with "who is actually affected?"
  3. Connect GitHub from day one for commit-level tracing — In Sentry settings, connect your GitHub organisation. Enable "suspect commits" — Sentry identifies which commit introduced each error by comparing the error's first occurrence against your deployment timeline. For Indian teams deploying multiple times per day, this feature alone saves 30–60 minutes per incident investigation by pointing directly at the causative code change.
  4. Run a weekly Sentry review with your engineering team — Every Monday, spend 15 minutes reviewing Sentry's top 10 errors by affected user count. Assign each error to an engineer for that sprint. Track the trend — are error rates increasing or decreasing over time? This ritual creates accountability for error reduction that most Indian engineering teams lack. Teams that do weekly Sentry reviews consistently reduce their error rates by 60–80% within three months through attention and prioritisation alone.
  5. Tag every production deployment with a release version — Configure release tracking: add Sentry.init with the release version in your code and run sentry-cli releases in your GitHub Actions deployment workflow. With release tracking, Sentry shows errors new in this release vs pre-existing ones, which errors the release resolved, and error rate trends by version. This dashboard becomes your primary post-deploy health check — a 5-minute review that catches regressions before users file support tickets.
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