Sentry vs Datadog vs New Relic for Errors and Performance

What you need to know

• Sentry is purpose-built for error tracking and application performance monitoring at the code level. It is the right first observability tool for most web and mobile applications.
• Datadog and New Relic are full observability platforms covering infrastructure, logs, APM, and custom metrics. They are appropriate when infrastructure visibility or distributed tracing is needed.
• Using both Sentry and Datadog is a common and sensible pattern: Sentry for error context, Datadog for infrastructure.
• Datadog's cost grows with log volume and host count. Model the cost carefully before committing at scale.
• For early-stage products, Sentry + Grafana Cloud (free tier) covers the observability needs of most applications at minimal cost.

The core argument

Observability decisions are often made by default: the team uses what they know, or uses the first tool recommended in an onboarding guide. This produces either under-instrumentation (missing critical signals) or over-instrumentation (paying for capabilities the team does not use). The right approach is to start from the questions the team needs to answer, then choose tools that answer those questions at the appropriate price point.

For most early-stage SaaS products, the primary observability questions are: what errors are users experiencing? What is causing slow API responses? Is the infrastructure healthy? Sentry answers the first two questions well. Basic infrastructure monitoring (uptime checks, CPU and memory trends) answers the third without requiring a full Datadog deployment. The full Datadog deployment makes sense when the team has services that need distributed tracing, when log search is a regular operational requirement, or when infrastructure monitoring across many hosts needs centralized dashboards.

The cost consideration is real. I have seen teams with $10,000 per month Datadog bills that could meet their actual observability requirements for $500 per month with Sentry plus a lightweight logging tool. The expensive Datadog deployment was acquired by default, not by design. Evaluate what is actually being used in the observability tooling before renewal conversations. In my experience, most small teams use 20 percent of the Datadog features they are paying for.

Common mistakes

1. Not setting up error tracking before launch. Error tracking is the observability layer closest to the user. Launching without Sentry (or an equivalent) means production errors only become visible through user complaints rather than through engineering alerting. Set up error tracking in the first week of development and connect it to an alerting channel before anything ships to production.

1. Ignoring Datadog or New Relic cost growth as infrastructure scales. Both tools have pricing that grows with data volume. A team that adds 10 new services, increases log volume by 5x, and adds a new region can see the monthly bill triple without making any explicit pricing tier change. Review the observability bill quarterly and understand which data sources are driving cost before the next scaling event.

1. Treating all errors as equally urgent. A well-configured Sentry project distinguishes between critical errors (affecting many users, occurring frequently, in payment or auth flows) and background errors (rare, in non-critical paths, expected in certain edge cases). Without this distinction, high-frequency low-severity errors flood the alert channel and create alert fatigue that causes teams to ignore the genuine critical errors. Set up Sentry alert rules that prioritize by user impact, not by raw error count.

1. Not configuring release tracking in Sentry. Sentry's release tracking connects errors to specific deployments, which is the most efficient way to identify regressions introduced by a release. Without release tracking, errors appear without context about when they first appeared. Configure Sentry to receive the current release version on each deployment and use the regressions view to detect new errors in recent releases.

1. Using infrastructure metrics without alerting. Infrastructure dashboards that are only reviewed after an incident are documentation, not monitoring. Configure alerting on the metrics that indicate degradation: CPU above 80 percent for 10 minutes, memory above 90 percent, error rate above threshold, response time above target. The dashboard shows what happened; the alert tells you when it is happening.

Where to start

1. Add Sentry to the application this week if it is not already there. The Sentry SDK for Node.js, Python, Ruby, or the browser takes 30 minutes to install and configure. Enable error tracking, configure source maps for frontend errors, and set up the alert channel. This produces immediate visibility into production errors at minimal cost.

1. Evaluate whether infrastructure monitoring is needed now or can be deferred. If the application runs on a managed PaaS (Vercel, Railway, Render), the platform provides basic infrastructure metrics. If the application runs on self-managed infrastructure, add a lightweight monitoring agent (Datadog agent, Grafana Agent) to collect CPU, memory, and disk metrics. Defer the full Datadog contract until log search at scale or distributed tracing is genuinely required.

1. Define three alert rules that would wake someone up at 2am. The three most important alerts for most web applications: error rate spike in the last 5 minutes (compared to the same window last week), response time above 5 seconds for the primary API endpoint, and database connection pool exhaustion. Configure these three alerts in whatever observability tool is in use. The three alerts represent the minimum effective on-call monitoring.

Related reading

• Observability in 2026: Metrics, Logs, Traces
• Real User Monitoring vs Synthetic Monitoring: Both, Not Either
• Runbooks That Actually Get Used During Incidents
• SLOs and SLIs for Founders: A Plain Language Guide