An Introduction to Microservices Monitoring—Strategies, Tools, and Key Concepts

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Users have higher expectations than ever when it comes to performance and reliability in the apps they use every day. A critical part of meeting these expectations is having a robust monitoring system in place. This article focuses on monitoring applications using a microservice architecture—it will go over key concepts, common challenges, and useful tools every engineer should know. Whether you’re looking to enhance system reliability, improve user experience, or drive efficiency, this guide will help get you started in the complex landscape of microservices monitoring.

Why monitoring microservices is important

Microservices architecture has become vital to modern application development thanks to its scalability, flexibility, and efficiency. However, to reap those benefits, you need to ensure everything works as expected, which is where monitoring comes in. Here are some of the key benefits of having a solid monitoring system:

  • Improved End User Experience - By monitoring microservices, you can ensure that each service performs optimally, leading to faster and more reliable user experiences.
  • Improved availability - Monitoring helps identify and mitigate issues before they affect your application’s availability, ensuring your app remains up and running smoothly.
  • Cost Savings - Effective monitoring can pinpoint inefficiencies within your services, allowing you to optimize resource usage and save costs.
  • Enhanced Observability - At the heart of microservices monitoring is observability, which is crucial for understanding the state of your distributed system. It encompasses logging, metrics, and distributed tracing, providing a holistic view of your services’ health and performance.

Microservices monitoring key concepts

There are a variety of metrics used to measure the performance of microservice applications. Here are some common metrics you’ll see used by many different organizations:

  • Latency and Response Time - These metrics are crucial for assessing how quickly your services respond to requests. High latency can lead to ‌poor user experience.
  • Error Rate - This measures the frequency of errors within your services. A high error rate could indicate underlying issues affecting your application’s reliability.
  • Resource Utilization - Monitoring CPU, memory, and other resources helps ensure your services aren’t over or underutilized.
  • SLO/SLI - Service Level Objectives (SLOs) and Service Level Indicators (SLIs) are critical for measuring the performance of your services against set benchmarks and, in some cases, need to be met in order not to face penalties with customers

The above metrics are derived from the three following types of data collected when monitoring applications.

Logs Logs are detailed textual records generated by software applications and infrastructure components. They contain events, transactions, and other activities that occur within the system. Logs can include a wide range of information, from error messages and warning alerts to informational messages about the application’s state or user actions.
Metrics Metrics are quantitative data points that measure various aspects of system performance and health. Common metrics include CPU usage, memory consumption, response times, throughput, and error rates, among others.
Traces Traces provide a detailed, step-by-step account of a single transaction or request as it travels through the various components of a distributed system. Each step, known as a span, captures important information about the operation performed by each service involved in processing the request. Traces are valuable for understanding the behavior of microservices and how they work together to fulfill requests.

Together, logs, metrics, and traces enable better observability and monitoring of your microservices.

Microservices monitoring challenges

Microservices monitoring has several unique challenges compared to simpler monolithic applications. In this section, you’ll learn about some of these challenges and ways to mitigate the issues.

Tracking service dependencies

One of the biggest challenges of a microservice architecture compared to a monolith application is tracking how microservices interact and depend on each other to fulfill user requests. Mapping and monitoring dependencies are critical in larger applications that may include dozens or even hundreds of microservices.

If these dependencies aren’t properly tracked, one team deploying changes to their microservice could break downstream services. For example, if the user authentication service in an e-commerce platform goes down, users won’t be able to checkout, put items in their cart, or see personalized recommendations.

Root cause analysis

The distributed nature of microservices architectures can significantly complicate the process of troubleshooting and identifying the root causes of issues. When a problem arises, it may manifest in one service but originate from another, making it difficult to trace back to the source.

Imagine a scenario where a video streaming service experiences intermittent outages. Users report that videos fail to load, but the issue is sporadic. It could be due to the UI, authentication, or other backend services. Using distributed tracing to track requests, you find that the root cause is due to caching failures with the CDN hosting video content.

Tech stack diversity

Another challenge is finding a balance between using the best tech stack for a service’s specific needs versus maintaining a common set of standard tools to make integration and long-term maintenance easier. Determining a list of approved technologies can help with this, and having a set of common libraries that abstract things like data collection regardless of tech stack can also help.


One of the benefits used to support the adoption of microservices is scalability, but getting it right isn’t easy. While theoretically, you can scale microservices up and down independently, this can be complex due to how the services interact with each other. Scaling down one microservice for efficiency and cost savings may create a bottleneck that impacts the entire application. You also need to consider reliability, disaster recovery, and sufficient capacity to handle traffic spikes. All this requires robust monitoring and analysis of historical data to forecast infrastructure requirements.

Implementation complexity

Implementation is often one of the biggest challenges for microservices, especially if you are migrating legacy applications. For monitoring specifically, you’ll need to configure each microservice to generate metrics, logs, and traces and then integrate a data collection service for your monitoring system. This involves upfront planning and maintenance to ensure compliance across multiple different teams.

Tools for microservices monitoring

Effective microservices monitoring hinges on leveraging the right set of tools. Each tool serves a unique purpose, from data collection to visualization, and understanding how to integrate them into your microservices architecture can significantly enhance observability and operational efficiency.


OpenTelemetry is an open source tool created to provide a unified way to instrument applications for collecting telemetry data like metrics, logs, and traces. OpenTelemetry provides a set of libraries and SDKs for many common programming languages, so companies don’t have to reinvent the wheel.


InfluxDB is an open source time series database optimized for storing and querying time series data like metrics, logs, traces, and events. InfluxDB can efficiently query recently ingested data for real-time monitoring and supports affordable object storage for historical data analysis.


Grafana is a data visualization and dashboarding tool commonly used for monitoring. Grafana also has built-in support for alerting and other useful features.


Telegraf is a server agent with over 300 different plugins for data input and output. Telegraf also supports data processing, so you can transform your data as needed before sending it to storage without requiring a separate data processing pipeline.


K6 is a load-testing tool that can be used as part of your deployment pipeline to find issues before they enter production. For example, K6 can test if a change would result in a drastic performance issue before the change goes live and impacts users.

Getting started with microservices monitoring

If you want your monitoring strategy to succeed, you’ll need a solid plan before getting started. Here are some foundational steps to ensure you’re on the right track.

Determine monitoring strategy and requirements

The first step is to figure out what specific data points you’ll be collecting and which are the most relevant for monitoring the performance of your microservices. You will then need to decide how to collect, store, and analyze this data. This involves choosing things like push vs pull for data collection, determining data latency limits, estimating data volume and velocity, and how the data will be analyzed to extract value and insights.

The key here is not just to collect data but to map out strategically how this data ties to the performance of your application and how that impacts the business itself.

Tool selection

Once you determine your requirements, you can start looking into implementation and what tools fit your requirements best. This will be a balancing act between cost, performance, and usability. You may want to test multiple solutions with production data to determine the best fit.

Some things to consider here are how well the tool fits into your existing tech stack and integrates with the other tools that make up your monitoring system. Consider the existing experience and skill set of your team as well. An important decision is whether to build a custom solution using open source tools or to go with a more complete platform solution. Some trade-offs will be implementation speed, potential vendor lock-in, and cost.

Implementation and integration

Once you select your tools, you must implement them into your application architecture. The first step will be to deploy collection agents, configure service meshes, or set up collection endpoints.

Next, you’ll want to integrate your monitoring system with your deployment pipeline for visibility as new services are created or updates are made to existing services. Once in production, you’ll want to set up dashboards and alerts for key metrics that notify you when issues arise.

Wrapping up

Properly monitoring your microservices is an ongoing process that involves continuous optimization to make your software as efficient and reliable as possible. Following this guide will equip you to take your first steps toward this end goal.