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Monitor a NestJS app in layers: check whether it can serve traffic, collect safe and searchable logs, then add metrics, traces, error monitoring, and runtime signals to explain slowdowns and failures. NestJS’s production guidance treats health checks and logging as foundations; deeper observability adds the detail needed to diagnose what happened.
Start with health checks for the app and its dependencies
A health endpoint lets your deployment platform check whether the application is able to serve work. NestJS documents @nestjs/terminus for checks covering databases, external services, and custom indicators. An infrastructure component can call the endpoint periodically and use the response when deciding whether to route traffic.
Decide what each health signal means for your deployment. Liveness generally answers whether the process should continue running; readiness answers whether it should receive traffic. A dependency outage may make an instance unready without meaning the process itself must restart. Choose behavior that matches your platform and service architecture rather than treating every failed dependency check the same way.
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Use production logs to explain events safely
Logs provide the event context that a health status cannot: what the application was doing, which operation failed, and what error detail is useful to an operator. NestJS recommends choosing severity levels, logging useful error information, and reducing debug or verbose output in production.
- Never log passwords, access tokens, or other secrets.
- Use correlation IDs in distributed systems so related events can be followed across services.
- Send logs to a centralized system when you need to search and analyze activity across application instances.
NestJS’s deployment documentation names Elasticsearch, Loggly, and Datadog as examples of centralized logging services. They are examples in the documentation, not a ranking or endorsement. The framework’s broader production guidance is in NestJS logging and NestJS deployment.
Add application-level observability to find causes
Health checks tell you whether a service is healthy; logs provide event detail. Metrics, traces, and error monitoring help show how often a problem occurs, where time is spent, and which failures deserve investigation.
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- Traces follow an operation across components or services and help identify where time went.
- Error monitoring groups failures and provides details for investigating them.
For a NestJS-specific option, the official documentation describes NestJS Observe as an auto-instrumented APM and observability platform built around Nest request-lifecycle components, including controllers and providers. Its overview covers HTTP, GraphQL, gRPC, and microservice requests, as well as queue and cron jobs; it lists throughput, latency, failure rates, traces, and error details among its monitoring capabilities. It also describes runtime profiling, release versioning, and custom metrics. See the NestJS Observe overview.
Check compatibility before installing NestJS Observe
The SDK guide lists @nestjs/core v11.1.4 or later as a requirement for @nestjs/observe. Applications using GraphQL require @nestjs/graphql v13.4.4 or later. Confirm these minimums and the current setup instructions in the NestJS Observe SDK guide before adding the package.
Know the documented plan allowances
The NestJS Observe overview lists the following included event allowances and retention periods. The live page does not state a publication year for these figures, and plan terms can change; check the page for current availability and terms.
| Plan | Included events per month | Retention |
|---|---|---|
| Free | 300k | 3 days |
| Pro | 25M | 30 days |
Monitor runtime behavior and instrument your own metrics
Request-level data may show that an operation is slow without revealing whether the cause is application code, runtime pressure, or an external dependency. Runtime signals such as memory, CPU, garbage collection, and event-loop latency help make that distinction. The NestJS Observe SDK guide documents runtime sampling, enabled by default at a default interval of 60,000 milliseconds. Confirm live defaults and configuration in the guide because they may change.
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For behavior specific to your application, NestJS manual instrumentation supports counters, gauges, and summaries. Use a counter for totals, a gauge for a value that changes over time, and a summary for a distribution. Custom metrics can also be reported outside an active request trace, including during startup or scheduled work. The SDK guide also describes filtering inbound health checks from instrumentation and telemetry-silence alerts as a lightweight heartbeat mechanism.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose monitoring by the question you need to answer
There is no single signal that replaces the others. Match each instrument to its job, then evaluate implementation choices against your NestJS version, deployment, and operational constraints.
| Approach | What it answers |
|---|---|
| Health checks | Can the app or a required dependency serve work? |
| Logs | What events and errors occurred? |
| Metrics | How are volume, latency, failures, or other values changing? |
| Traces | Where did time go across operations and services? |
| Error monitoring | Which failures can be grouped and investigated? |
When comparing monitoring tools or designing your own stack, consider transport and queue coverage, instrumentation detail, export and data-control requirements, telemetry volume and retention, alerting needs, and the work required to operate collectors or backends. NestJS’s documentation describes these monitoring categories but does not provide a neutral, head-to-head vendor test.
Quick Recap
Build the monitoring setup in practical layers
- Make health actionable. Add a health endpoint and check the dependencies that determine whether the application can accept work. Align readiness and liveness behavior with your deployment platform.
- Make logs useful and safe. Choose production severity levels, exclude secrets, and use correlation IDs where requests cross services. Centralize logs if operators need to search across instances.
- Measure service behavior. Track latency, throughput, and failures, then add traces and error details to investigate problematic requests or jobs.
- Separate runtime pressure from external causes. Use CPU, memory, garbage-collection, and event-loop signals alongside request and dependency telemetry.
- Instrument business-specific activity. Add counters, gauges, or summaries where generic request monitoring does not answer an operational question.
- Verify alerts and noise controls. Confirm that health checks are not generating misleading telemetry, and decide how to detect missing telemetry as well as reported failures.
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