Sentry vs Datadog: Error Tracking or Full Observability?
We run Sentry in every project and Datadog for complex infrastructure. Compared on error tracking depth, pricing at scale, self-hosting and when to use both together.
Sentry and Datadog complement each other more than they directly compete. Sentry is the undisputed leader in error tracking: no other platform offers comparable depth in debugging application errors with session replay, breadcrumbs, and issue grouping. Datadog, on the other hand, provides the full observability picture: from server metrics to logs to distributed traces. Many organizations use both: Sentry for application error tracking and Datadog for infrastructure monitoring. Your choice depends on whether you primarily need to track errors or require full observability.
Sentry
The leading error tracking and performance monitoring platform with an open-source core. Sentry provides real-time error tracking, session replay for reproducing bugs, performance monitoring with distributed tracing, release health tracking, and a powerful alerting engine. The platform integrates with over 100 frameworks and is particularly popular among frontend and full-stack developers.
Datadog
A comprehensive observability platform covering the full monitoring spectrum. Datadog offers Application Performance Monitoring (APM), log management, infrastructure monitoring, real-time metrics dashboards, synthetic monitoring, and security monitoring. The platform is designed for DevOps teams needing complete observability across their entire infrastructure and application stack.
What are the key differences between Sentry and Datadog?
| Feature | Sentry | Datadog |
|---|---|---|
| Focus | Error tracking and performance monitoring with depth in application errors | Full observability: APM, logs, infra, metrics, security |
| Error tracking | Best-in-class: stack traces, breadcrumbs, session replay, issue grouping | Error tracking available but less deep than Sentry |
| Infrastructure | Limited. Focus on application layer, not servers/containers | Extensive: server metrics, container monitoring, Kubernetes, cloud |
| Log management | No log management; that is not the core focus | Full log management with indexing, search, and log-to-trace correlation |
| Open-source | Open-source core (self-hostable) with paid cloud version | Fully proprietary SaaS without self-hosting option |
| Pricing | Free tier with 5,000 errors/month, Team from $26/month | Free trial, then per host/GB/event (can quickly scale to thousands of euros) |
When to choose which?
Choose Datadog when...
Choose Datadog when you need full-stack observability across infrastructure, applications, and logs in a single platform, when your DevOps team manages complex microservice architectures with Kubernetes, or when correlating logs, metrics, and distributed traces is essential for diagnosing production issues.
What is the verdict on Sentry vs Datadog?
Sentry and Datadog complement each other more than they directly compete. Sentry is the undisputed leader in error tracking: no other platform offers comparable depth in debugging application errors with session replay, breadcrumbs, and issue grouping. Datadog, on the other hand, provides the full observability picture: from server metrics to logs to distributed traces. Many organizations use both: Sentry for application error tracking and Datadog for infrastructure monitoring. Your choice depends on whether you primarily need to track errors or require full observability.
Which option does MG Software recommend?
At MG Software, we use Sentry as our primary error tracking platform. The deep error context, session replay, and seamless integration with our Next.js/React ecosystem make it indispensable for quickly finding and fixing bugs. The open-source nature aligns with our preference for transparent tooling. For clients with complex infrastructure, we combine Sentry with Datadog or Grafana for infrastructure monitoring. This combination offers the best of both worlds: superior error tracking and full observability.
Migrating: what to consider?
Moving from Sentry to Datadog for error tracking involves replacing Sentry SDKs with Datadog APM agents. Datadog's error tracking is less granular than Sentry's, so evaluate whether you lose critical debugging context like session replay and breadcrumbs. Many teams keep Sentry alongside Datadog rather than fully replacing it.
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