Grafana vs Sentry

What Grafana is

Grafana turns metrics, logs and traces into dashboards, and connects to almost any data source. The open-source version is fully capable; the cloud version removes the operational work. Its strength is that it does not own your data: it queries Prometheus, Loki, SQL databases and cloud providers where they already live. That also means Grafana alone is not a monitoring system, and you still have to choose and run the stores behind it.

What Sentry is

Sentry provides real-time error tracking and performance monitoring for your software. It notifies you exactly which line of code failed and what the user was doing at the time. Beyond bugs, it analyzes slow page loads and DB queries. Its 'Session Replay' lets you watch a video of what the user saw during the error, allowing you to solve issues in seconds.

What Grafana is built to do

Data-source agnostic

Queries Prometheus, Loki, SQL and cloud APIs without moving data into Grafana.

Alerting

Evaluates rules against queries and routes notifications to external channels.

Dashboard as code

Stores dashboards as JSON so they can be versioned and provisioned.

What Sentry is built to do

Error Monitoring

View software crashes and errors in real-time, down to the specific line of code.

Session Replay

Visually watch the user's session to understand exactly how an error occurred.

Performance

Automatically detect slow API requests and sluggish page transitions.

Issue Linkage

Assign errors directly to the team via integrations like Jira or GitHub.

Choose Grafana if

• Any team that needs to see what its systems are doing, from one server to a large fleet.

Choose Sentry if

• Software Developers, DevOps Engineers, and Product Managers.

Grafana: pros & cons

• Connects to dozens of data sources out of the box
• Open source and self-hostable
• Alerting built into the same tool as the dashboards
• Dashboard design takes real effort to get right
• Cloud pricing scales with data volume
• Configuration sprawl on large teams

Sentry: pros & cons

• Unrivaled Detection: Learn about errors before your users even have time to complain.
• Wide Support: Compatible with almost every language from Python to Swift.
• Rich Context: Provides logs, device info, and more to help you fix bugs faster.
• Performance Monitoring: Shows exactly where your application is slowing down.
• Noise Management: Can send thousands of unimportant alerts if not tuned correctly.
• Cost: Data storage costs can rise for applications with very high traffic volume.
• Setup: Properly integrating it into every part of an app takes some dedicated time.