OpenTelemetry in 2026: How to Monitor Applications With Logs, Metrics, and Traces

Modern applications are becoming more complex.

A single website may depend on several APIs, databases, background services, and external platforms. As a result, finding the cause of a performance problem can be difficult.

For example, imagine an ecommerce application that suddenly becomes slow.

The frontend appears to work correctly. However, customers experience delays during checkout.

Is the problem coming from the database, an API, or the payment service?

This is where OpenTelemetry becomes useful.

OpenTelemetry helps developers collect information about application performance and behavior. In addition, it provides a standardized approach to monitoring distributed systems.

In this guide, we’ll explore OpenTelemetry in 2026, explain logs, metrics, and traces, and show how to get started with Node.js.

What Is OpenTelemetry?

OpenTelemetry, also known as OTel, is an open-source observability framework.

It provides tools and standards for generating, collecting, and exporting application telemetry.

Telemetry is information that describes how software behaves.

For example, it can show how long an API request takes or where an error occurs.

OpenTelemetry focuses on three important signals:

• Logs: Records of application events.
• Metrics: Numerical measurements collected over time.
• Traces: Information about how requests move through an application.

Together, these signals help developers understand complex systems.

However, OpenTelemetry isn’t a complete monitoring dashboard.

Instead, it sends telemetry to compatible observability platforms for storage and analysis.

Why OpenTelemetry Matters in 2026

Applications increasingly rely on multiple services.

For example, a SaaS platform may use a frontend application, an authentication service, a database, and several external APIs.

When something fails, developers need to understand how these components interact.

Traditional monitoring can show whether a server is running. However, it may not explain why a particular request is slow.

OpenTelemetry helps close that gap.

In addition, its vendor-neutral design allows developers to work with different monitoring platforms.

Therefore, teams can build more flexible observability workflows.

1. Understanding Logs in OpenTelemetry

Logs are records of events that happen inside an application.

For example, a Node.js application might record:

• A user login
• An API error
• A failed payment
• A database connection issue
• A completed background task

Consider this simple example:

console.log("User login successful");

console.error("Database connection failed");

These messages provide useful information.

However, plain text logs can become difficult to manage as an application grows.

Why Structured Logs Matter

Structured logs organize information into fields.

For example:

const logEntry = {
  level: "error",
  service: "checkout-api",
  message: "Payment request failed",
  orderId: "ORD-1234"
};

console.error(JSON.stringify(logEntry));

This format makes logs easier to search and analyze.

In addition, structured logs can include trace identifiers that connect an event to a specific request.

As a result, developers can investigate problems more efficiently.

OpenTelemetry provides a standardized model for log data. However, collecting application logs still requires appropriate instrumentation or integration.

2. Understanding Metrics in OpenTelemetry

Metrics are numerical measurements that describe application behavior.

Unlike individual log messages, metrics help developers identify patterns over time.

For example, a monitoring system might track:

• Requests per second
• API response times
• Error rates
• Memory usage
• CPU utilization
• Queue length

These measurements can reveal performance problems before they become serious.

Common Application Metrics

Metric	What It Measures
Request rate	Number of incoming requests
Error rate	Percentage of failed requests
Response time	Time needed to complete requests
CPU usage	Processor resource consumption
Memory usage	Memory used by an application
Queue length	Tasks waiting for processing

For example, imagine your API normally responds within 150 milliseconds.

Suddenly, response times increase to 900 milliseconds.

This change suggests a performance problem.

Therefore, developers can investigate before more users are affected.

Why Metrics Are Useful

Metrics provide a broad view of application health.

However, they don’t always explain the cause of a problem.

For that reason, developers often combine metrics with logs and traces.

3. Understanding Distributed Tracing

Distributed tracing is one of OpenTelemetry’s most useful capabilities.

A trace represents the path of an operation through a system.

For example, imagine a customer placing an order.

The request might follow this path:

Frontend → API → Authentication → Database → Payment Service

Each step takes time.

However, without tracing, developers may struggle to identify which component causes a delay.

What Is a Span?

A span represents an individual operation within a trace.

For example, an order request might contain these spans:

• Receive HTTP request
• Validate user
• Query product inventory
• Process payment
• Save order
• Return response

Each span can include timing information and additional attributes.

As a result, developers can understand how different operations contribute to the total request time.

Example: Finding a Slow Database Query

Imagine a checkout request takes 2.5 seconds.

Distributed tracing reveals the following:

Operation	Duration
Authentication	40 ms
Inventory lookup	1,800 ms
Payment processing	450 ms
Other processing	210 ms

The inventory lookup accounts for most of the delay.

Therefore, developers can focus on the database query instead of investigating every service.

This is especially useful for applications with multiple backend components.

4. OpenTelemetry vs. Traditional Monitoring

Traditional monitoring often focuses on infrastructure.

For example, developers might monitor CPU usage, server availability, and memory consumption.

These measurements remain important.

However, modern applications need additional visibility.

OpenTelemetry helps connect infrastructure information with application behavior.

Traditional Monitoring	OpenTelemetry-Based Observability
Server health	Application and service behavior
CPU and memory	Custom application metrics
Basic error logs	Contextual logs
Individual server alerts	Distributed request tracing
Infrastructure focus	Cross-service visibility

However, OpenTelemetry doesn’t replace every traditional monitoring tool.

Instead, it complements existing monitoring systems.

5. How OpenTelemetry Works

OpenTelemetry uses several components to collect and export telemetry.

A typical architecture looks like this:

Application → OpenTelemetry SDK → Collector → Observability Backend

Let’s explore each component.

Application Instrumentation

Instrumentation allows an application to generate telemetry.

For example, a Node.js application can record HTTP requests and database operations.

Developers can add instrumentation manually or use supported automatic instrumentation libraries.

OpenTelemetry SDK

The SDK processes telemetry generated by an application.

It also manages configuration, sampling, and exporting.

OpenTelemetry Collector

The Collector receives, processes, and exports telemetry data.

For example, it can receive traces from several applications and forward them to a monitoring platform.

In addition, it can help centralize telemetry configuration.

Observability Backend

The backend stores and displays telemetry.

Popular options include Jaeger, Prometheus, and commercial observability platforms.

However, different backends support different telemetry signals.

Therefore, developers should choose tools that match their monitoring requirements.

6. Getting Started With OpenTelemetry in Node.js

Node.js is a practical starting point for learning OpenTelemetry.

For this example, we’ll use a simple Express application.

The example follows the current OpenTelemetry Node.js SDK approach.

Step 1: Create a Node.js Project

First, create a new directory.

Then, initialize your project:

mkdir otel-demo
cd otel-demo
npm init -y

Next, install Express:

npm install express

Step 2: Install OpenTelemetry

Install the required packages:

npm install @opentelemetry/api \
  @opentelemetry/sdk-node \
  @opentelemetry/sdk-trace \
  @opentelemetry/auto-instrumentations-node

These packages provide the core instrumentation tools.

Step 3: Create an Express Application

Create a file named app.js.

const express = require("express");

const app = express();

app.get("/api/products", (req, res) => {
  res.json({
    products: ["Laptop", "Keyboard", "Mouse"]
  });
});

app.listen(3000, () => {
  console.log("Server running on port 3000");
});

This application exposes a simple API endpoint.

Next, we’ll configure tracing.

Step 4: Configure OpenTelemetry

Create a file named instrumentation.cjs.

const { NodeSDK } = require("@opentelemetry/sdk-node");

const {
  ConsoleSpanExporter
} = require("@opentelemetry/sdk-trace");

const {
  getNodeAutoInstrumentations
} = require(
  "@opentelemetry/auto-instrumentations-node"
);

const sdk = new NodeSDK({
  traceExporter: new ConsoleSpanExporter(),
  instrumentations: [
    getNodeAutoInstrumentations()
  ]
});

sdk.start();

This configuration enables automatic instrumentation for supported libraries.

In addition, it exports spans to the console.

Therefore, developers can inspect tracing information without configuring an external monitoring platform.

Step 5: Start the Application

Run the application with instrumentation loaded first:

node --require ./instrumentation.cjs app.js

Next, open:

http://localhost:3000/api/products

The application returns a JSON response.

Meanwhile, OpenTelemetry generates tracing information for supported operations.

You can inspect the spans in your terminal.

This example is intended for local development. Production applications should use appropriate exporters, access controls, and telemetry storage.

7. What’s New in OpenTelemetry JavaScript 3.0?

OpenTelemetry JavaScript SDK 3.0 was released in November 2026.

This release introduces several important changes for JavaScript developers.

Stable Logs Support

Logs join traces and metrics as stable signals in the JavaScript SDK.

As a result, developers have a more consistent foundation for all three observability signals.

Improved ESM Support

The SDK now provides proper ECMAScript Module and CommonJS exports.

Therefore, developers using modern JavaScript tooling can benefit from improved module compatibility.

Updated Node.js Requirements

The minimum supported Node.js version is now 22.15.0.

Consequently, applications running older Node.js versions need to review compatibility before upgrading.

API and Package Changes

Several packages and public interfaces have changed.

In addition, deprecated packages have been removed.

Therefore, teams upgrading from SDK 2.x should follow the official migration guide.

8. OpenTelemetry for Docker and Containerized Applications

OpenTelemetry can also help developers monitor containerized applications.

For example, imagine a Docker environment containing:

• A Node.js API
• A PostgreSQL database
• A Redis cache
• A background worker

Each service performs different tasks.

However, an issue in one service can affect the entire application.

OpenTelemetry helps developers follow requests across supported instrumented components.

For example, a slow API response might originate from a database operation.

In addition, the OpenTelemetry Collector can run as a container alongside application services.

If you’re working with containerized applications, our guide to Docker vs. Podman in 2026 explains the differences between two popular container management tools.

9. OpenTelemetry for Database Performance

Database operations are another useful area for observability.

For example, an application might experience slow queries during periods of high traffic.

Metrics can reveal increased response times.

Meanwhile, traces can show which requests spend the most time waiting for database operations.

Logs may provide additional error details.

Together, these signals help developers identify bottlenecks.

However, telemetry alone won’t fix inefficient queries.

Developers still need to review indexing, query structure, and database configuration.

For additional database guidance, read our SQLite vs. PostgreSQL in 2026 comparison to understand how database architecture affects application workloads.

10. OpenTelemetry and Website Performance

Application observability can also support website performance improvements.

For example, a slow page may result from delayed API responses.

OpenTelemetry can help developers investigate the backend operations involved.

However, it doesn’t replace browser performance tools.

Metrics such as Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift measure different aspects of user experience.

Therefore, frontend and backend monitoring should work together.

To explore browser performance, check our guide to Core Web Vitals in 2026, including practical LCP, INP, and CLS optimization techniques.

11. OpenTelemetry Best Practices

Collecting more data isn’t always better.

Instead, developers should focus on useful telemetry.

Start With Important Services

Begin by monitoring your most critical application components.

For example, an ecommerce platform might prioritize checkout and payment services.

Use Consistent Service Names

Clear naming makes telemetry easier to understand.

For example, use names such as checkout-api or inventory-service.

Avoid Sensitive Information

Telemetry can accidentally include personal data, authentication tokens, or passwords.

Therefore, review attributes and log content before exporting them.

Configure Sampling

Large applications can generate significant tracing data.

Sampling helps control the amount of telemetry collected.

However, sampling policies should preserve enough information for effective troubleshooting.

Monitor Telemetry Costs

Observability platforms may charge based on data volume.

Therefore, avoid collecting unnecessary information.

Common OpenTelemetry Mistakes

Developers sometimes expect OpenTelemetry to solve every monitoring problem automatically.

However, successful observability requires planning.

Collecting Too Much Data

Excessive telemetry can increase storage costs.

Instead, prioritize useful measurements.

Ignoring Context Propagation

Distributed traces rely on context passing between services.

Without proper propagation, a single request may appear as unrelated operations.

Forgetting About Security

Logs and traces can contain sensitive information.

Therefore, apply appropriate access controls and data retention policies.

Skipping Performance Testing

Instrumentation introduces some processing overhead.

As a result, developers should measure its impact under realistic workloads.

Frequently Asked Questions About OpenTelemetry

What is OpenTelemetry used for?

OpenTelemetry helps developers generate, collect, and export application telemetry, including logs, metrics, and traces.

Is OpenTelemetry free?

Yes. OpenTelemetry is an open-source project. However, telemetry storage and commercial monitoring services may have additional costs.

Does OpenTelemetry replace Prometheus?

No. OpenTelemetry can collect and export metrics, while Prometheus provides monitoring and time-series storage capabilities.

Does OpenTelemetry work with Node.js?

Yes. OpenTelemetry provides JavaScript instrumentation libraries and SDKs for Node.js applications.

What is distributed tracing?

Distributed tracing follows an operation across application components. As a result, developers can identify where delays and errors occur.

Can OpenTelemetry monitor Docker containers?

Yes. OpenTelemetry can collect telemetry from instrumented applications running inside containers. In addition, compatible integrations can collect container and infrastructure metrics.

Is OpenTelemetry difficult to learn?

Basic instrumentation is relatively approachable. However, advanced observability setups require knowledge of telemetry pipelines, sampling, and monitoring platforms.

Final Thoughts

OpenTelemetry in 2026 provides developers with a standardized approach to application observability.

Logs explain what happened.

Meanwhile, metrics reveal performance trends.

Traces show how operations move through different services.

Together, these signals make complex applications easier to understand.

In addition, OpenTelemetry’s vendor-neutral approach allows teams to choose monitoring platforms that fit their requirements.

However, effective observability isn’t about collecting every possible measurement.

Instead, developers should focus on the information that helps them detect and resolve real problems.

The goal of OpenTelemetry is simple: understand your applications better so you can build more reliable software.