If your app runs in production, you’ll need to know when it breaks. Preferably before your users tell you.
That’s where application monitoring tools (APM) come in. They show you what’s working, what’s slow, and what’s failing, all in one place.
Here are five of the best tools developers use today. I’ll walk you through what they do, why they’re good, and how you might use them in your projects.
New Relic
New Relic is one of the oldest players in this space. It’s great for full-stack observability, meaning you can see everything from your frontend JavaScript errors to database query times.
Imagine your Node.js backend is running slow. You deploy a new endpoint, and your API response times go up.
With New Relic, you can trace that slowdown to a specific function call or database query. It shows you performance metrics, transaction traces, error rates, and alerts in real time.
For beginners, New Relic’s dashboard can feel overwhelming. But once you get used to it, you’ll see why large teams rely on it for 24/7 monitoring.
If you want one tool that does application performance monitoring (APM), infrastructure monitoring, browser monitoring, and even mobile monitoring in one place, New Relic is your tool.
New Relic is a paid tool, but it comes with a generous free tier for you to start exploring its features. Here is the full pricing plan.
Datadog
Datadog started as an infrastructure monitoring tool but has become a powerhouse for developers, too.
It integrates easily with AWS, Azure, GCP, Kubernetes, Docker, and almost any service you use.
Assume you deploy a Flask app on Kubernetes. Suddenly, users report timeouts.
In Datadog, you can view your pod metrics, CPU and memory usage, container logs, and APM traces all in one timeline. You’ll quickly see if your pod was OOMKilled, if your database had connection spikes, or if your app code itself was slow.
Datadog also shines in alerting. You can set up alerts like:
If average response time > 2000ms for 5 minutes, send Slack alert to #devops
This keeps your team proactive instead of reactive.
It also integrates behavioural targeting data from user sessions and performance metrics, helping product teams understand how performance issues affect user behaviour and conversion.
If you want seamless cloud-native monitoring with powerful dashboards, alerts, and security integrations, Datadog is your solution.
Datadog is free for up to five hosts, so the free plan would be sufficient for solo developers / small teams to get started. Here is the full pricing plan.
Prometheus + Grafana
Prometheus is an open-source monitoring system that scrapes metrics from your app, stores them in a time-series database, and lets you query them with PromQL.
Grafana is the dashboard layer on top. Together, they’re like peanut butter and jelly for monitoring.
Here’s how you can use them. Suppose you have a Go API exposing metrics on /metrics using the Prometheus client library. Prometheus scrapes that endpoint every 15 seconds. You can query:
rate(http_requests_total[5m])
This shows you the average requests per second over the last 5 minutes.
Then, in Grafana, you build dashboards to visualise that data with graphs, gauges, and alerts. Many teams use Grafana for system health dashboards displayed on TVs in the office.
Prometheus is free, flexible, and used heavily with Kubernetes because of its service discovery features. But it requires setup and maintenance compared to SaaS tools.
If you want a powerful open-source solution with custom dashboards and PromQL querying, Prometheus + Graphana is your solution.
Sentry
Unlike New Relic and Datadog, Sentry focuses on error and performance monitoring.
It’s a favourite for frontend and backend developers because it gives detailed stack traces, breadcrumbs, and release tracking.
For example, say your React app throws an error when users click “Submit”. Sentry captures:
The exact error and message
The function and file that caused it
The user’s browser and OS
The recent events (breadcrumbs) before the error
This helps you reproduce and fix the issue fast.
On backend apps, it works similarly to the frontend. You can integrate Sentry with Django, Express, Flask, or almost any framework to capture exceptions and performance bottlenecks.
If you want to track bugs and performance issues in real time, with deep context to debug them quickly, Sentry is your solution.
Sentry is free for a single user with minimal features. Here is the full pricing plan.
PostHog
PostHog is a modern open-source tool for product analytics, session recording, feature flags, and application monitoring.
Unlike traditional APM tools, PostHog focuses on understanding how users interact with your app.
For example, suppose users aren’t completing your signup flow. With PostHog, you can:
Record user sessions to see exactly where they drop off
Track funnel conversion rates step by step
Analyse feature usage to prioritise improvements
Use behavioural targeting to trigger in-app prompts for specific user segments
You can self-host PostHog on your infrastructure or use their cloud offering. Developers like it because it combines product analytics and user insights without sending data to third parties if self-hosted.
If you want to combine product analytics, session replays, feature flags, and event-based monitoring in one tool to understand and improve user behaviour in your app, Posthog is your solution.
PostHog has a generous free tier for up to 1 million events per month. Paid plans start from $0.00045 per event after the free tier, with enterprise features and advanced plugins. So there is no fixed pricing and you pay as your application scales.
So which APM tool should you pick?
If you’re a solo developer or a small team, start with Sentry for errors and Prometheus + Grafana for open-source metrics.
As you grow and need unified monitoring with alerts and APM, tools like Datadog or New Relic become valuable.
If you want full control of your data with modern APM features and pricing that scales with your app, Posthog is your solution.
Conclusion
Remember, monitoring isn’t just about fixing failures. It’s about learning how your app behaves under real usage. This helps you optimise performance, spot bottlenecks, and build resilient software that users trust.
Take some time to integrate at least basic monitoring into your apps. Even simple HTTP request metrics and error alerts can save you hours of blind debugging later.
Hope you found this article useful. Get in touch with me on LinkedIn.
Source: freeCodeCamp Programming Tutorials: Python, JavaScript, Git & MoreÂ
