This article will cover the top 6 AWS Lambda monitoring tools and explain how they work and are used.

Monitoring AWS Lambda performance plays a crucial part in your everyday AWS Lambda usage. Monitoring helps you identify any performance issues, and it can also send you alerts and notify you of anything you might need to know. The world is slowly getting to a point where machines and computers will be flawless, but until then, if we let them perform various tasks for us, we could at least monitor their performance.

We need another kind of program that would monitor our automated work's activity for this to happen. Some tools can offer significant help with the monitoring of your AWS Lambda performance. 

Dashbird

Dashbird is excellent in providing error alerts and also in monitoring support. Dashbird collects and analyzes CloudWatch logs while zeroing the effects on your AWS Lambda performance. Integration with the Slack account is also possible, and that brings alerts about early exits, crashes, cold starts, timeouts, runtime errors, etc., to your development chat. Dashbird's error diagnostics, advanced log searching, and function statistics are only a few of the benefits Dashbird offers to its users.

All the needed information is available on a dashboard, including an overview of all invocations, top active functions, system health, and recent errors. Going down to invocation level data is yet another offering from Dashbird, and there you can analyze all of the functions separately. It's very user-friendly while it provides all the information you could ask for.

Dashbird detailed views for performance tracking, optimization and error handling, tracking and error monitoring, and troubleshooting are what make Dashbird a tool you always wanted. Providing a quick overview of everything going on with your serverless infrastructure, including invocation volumes, latency, failures, and overall health.

Check out the documentation if you wish to learn more about each platform's specific technical working principles and compare them for pros and cons, or even see what benefits they have. You'll be able to find much more information.

Datadog

Datadog provides the unity of metrics, logs, and traces. Aggregating events and metrics from more than 200 technologies such as Amazon Web Services, MongoDB, Slack, Docker, Chef, and many others. Datadog also explores enriched data, searches and analyzes log data while tracing requests across the distributed systems, and alerts you on app performance.

Datadog also provides its users with real-time insights allowing you to drag-and-drop dashboards to graphs. It also allows you to analyze and to have a correlation between metrics and events. Seamless AWS integration is not science fiction anymore. Datadog allows you to discover and monitor your AWS services like EBS, ELB, EC2, RDS, ElastiCache, and many others.

Datadog will notify you if any performance problems arise, whether they have affected a massive cluster or just a single host. You can choose between various channels to be notified from, such as Slack, e-mail, PagerDuty, and others. Building a complex alert logic using several trigger conditions is also possible with Datadog. At the same time, you can mute all alerts with a single push of a button when the system is upgrading or during the maintenance period.

Logz.io (ELK)

Logz.io offers ELK service the best choice for scaling and performance with ease while there's no need to perform upgrades or capacity management. Logz.io security is enterprise-grade, and it keeps your data private and secure while also complying with key industry standards. Logz.io goes way beyond the ELK service to provide an Enterprise-Class log analytics platform consisted of features like integrated alerts and multiple sub-accounts.

Fast issue resolving is happening because of their advanced machine learning setup, which locates critical and unnoticed errors and exceptions in real-time in combination with actionable and contextual data for faster resolutions.

Logz.io also uses an AI-Powered analytics system that applies pre-built machine learning across data specified by use-case, user behavior, and community knowledge, allowing anomaly identification and surface the value hidden in the data. Providing a suite of analytics and optimizing tools that help organizations reduce the overall logging expenses as their data grows is yet another perk offered by Logz.io.

Thundra

Thundra started as a serverless monitoring platform but later switched to targeting more general services. While they're still a good choice for serverless systems, their tools can now be used for containers and virtual machines too.

Thundra's monitoring approach differs from Dashbird mainly in how the instrumentation of Lambda functions is conducted. Dashbird gets all its data from CloutWatch, requiring no code changes. Thundra, on the other hand, requires a Lambda extension or software library integration to do its work. Thundra offers extensions for Node.js, Python, Java, Go, and .NET.

Thundra's focus is online debugging. Because of their code instrumentations inside a Lambda function, they can gather information about code lines, which allows you to retroactively step through every line of code in your function for every invocation that had debugging enabled.

Lumigo

Lumigo offers visual debugging, and it also comes with tracing, metrics, and alert support, but, differing from Thundra, it is more focused on serverless monitoring, from the architecture down to function logs and traces. Lumigo also comes with a Lambda Layer/Extension for Python and Node.js runtimes to instrument Lambda functions.

Lumigo seems to be the odd one out here that poured some time and money into a polished UI. At least, at first sight, Lumigo seems to be a bit more well-thought-out than other competitors in the monitoring space.

Epsagon

Epsagon offers serverless monitoring but, like Thundra, also includes containers in their offering. Overall they seem to be more general than all of the other companies listed here. They offer integrations for many AWS services, Azure Functions, and more purpose-built service providers like Auth0 and Slack.

Their Kubernetes integration also makes them a good fit for Google's Cloud Platform.

Conclusion

Learning about how to approach the serverless monitoring architecture will for sure make your life (and work) much easier. With a proper understanding of the AWS infrastructure, you are one step closer to a new skill called "observability" regarding the lambda functions. The price is set, but it's a small one compared to the lambda function benefits you'll obtain.

If you feel like we missed something or wish to contribute to this discussion, don't hesitate to fill in the comment sections below and let us know your thoughts, ideas and share them with the rest of our readers and us.