Best Python Application Performance Monitoring (APM) Tools in 2022

Python Applications Performance Management and Monitoring tools enable code-level observability, faster recovery, troubleshooting, and easier maintenance of any python project. Measuring the performance of python applications is handled by tracking logs and performance metrics from all around the infrastructure. Subsequent data analysis helps developers to understsand errors in context and trace them across the stack to the root cause.

Python is still gaining momentum and popularity, and the market is full of various python APM tools. Let's take a look at some of the best of them in 2022.

1. Logtail

Logtail is a structured log management platform based on ClickHouse. It allows you to start collecting, analyzing, and visualizing logs from Python and Django in real-time. Logtail's advanced built-in collaboration features, resource-efficient ClickHouse, and visually pleasing, dark-mode UI help you to spend less time debugging and focus on shipping higher-quality software faster.

Logtail is a part of the Better Stack ecosystem, and together with Better Uptime, they create a complete observability solution with incident management built-in. With one-click Better Uptime integration, you can easily alert on-call team members of any irregularities in your application's behavior. Be it a specific log message error or a predefined usage trend.

2. Sentry.io

Sentry.io started out as a Django monitoring side project and, after a while, became the backbone for tracking errors and exceptions of one of the biggest industry players. It now offers error tracking and performance monitoring for the most popular Languages and Frameworks for app development.

3. New Relic

New Relic's Python Monitoring enables visibility into python apps and helps with identifying and troubleshooting performance issues. It tracks key transactions, monitors critical metrics, and visualizes everything in dashboards. New Relic's Python monitoring promises improved performance, query optimization, and instant observability.

4. Site24x7

Site24x7's Python APM supports Django and Flask frameworks and multiple MySQL and NoSQL databases. Its Python insights agent collects data about response times, throughput, database operations, and errors of python applications.

5. Paessler PRTG

Paessler's python probes allow you to use probes to monitor Python applications and create a complex and custom APM solution for python apps.

Paessler offers international support in multiple languages, allows you to deploy custom scripts, and offers pay-as-you-go pricing. However, its documentation often lacks even in English, and it recommends Windows as a platform for best compatibility and performance.

6. Dynatrace

Dynatrace's OneAgent allows for python auto-instrumentation of Python applications. However, it requires additional settings and configuration for frameworks, such as Django.

Dynatrace traces and extracts data about hosts, processes, and services used in automated naming, tagging, and analysis. Dynatrace also has an SLO monitor built-in allowing you to keep track of the service's performance in terms of SLO.

7. AppOptics

AppOptics offers a python agent for automated tracing and metric collection from Python Apps. AppOptics offers on-prem, hybrid, and cloud-based, distributed applications monitoring. It allows you to combine dashboards to visualize metrics from application and infrastructure in one place.

Apart from Python, SolarWinds' AppOptics offers Monitoring for Java, .Net, PHP, Scala, Node.js, Ruby, and Golang applications.

8. Retrace

Stackify's Retrace APM combines code profiling, error tracking, and application log monitoring in one. It offers deep insights into your infrastructure, dependencies, and code itself. It tracks methods in application code via a lightweight profiling approach.

Retrace offers automatic instrumentation for Django, Redis cache, and flask, to name a few.

Stackify's APM for Python supports centralized logging, code profiling, error tracking, App and Server Monitoring, and RUM.

9. Atatus

Atatus offers instant insight into Python applications and frameworks. It enables end-to-end visibility for python applications. Atatus allows you to monitor API failures, track python errors and exceptions, trace transactions, monitor external requests, monitor databases performance, and much more.

Atatus also offers auto instrumentation for the most popular frameworks, enables smart notifications, compare the impact of different code versions, and is fully scalable.

10. AppDynamics

AppDynamics offers real-time insights into Python apps and maps critical performance metrics and transactions. During monitoring, it correlates transactions across environments and platforms and offers comprehensive, contextual performance metrics of Python apps in general.

It offers rapid root-cause identification and issue resolution, offers APIs for extended compatibility and functionality, and works well both on-prem and in hybrid environments.

11. SigNoz

SigNoz supports OpenTelemetry as the primary approach to instrumentation. It supports Python and some Python Frameworks, such as Django. It runs on either Kafka+Druid or OLAP database ClickHouse for the backend. The Query service is built-in GO, and the front-end is Typescript-powered.

SigNoz offers a broad and vibrant community of contributors and well-written, holistic documentation.

12. Prometheus

Prometheus is an open-source monitoring tool offering support for Python application monitoring. It offers an official Python client library used for continuous monitoring.

You can monitor four python metrics using Prometheus - Counter, Gauge, Summary, and Histogram. It's often deployed alongside open-sourced visualization tools such as Grafana for visualizations of collected and monitored metrics.

Conclusion

With the wide range of free, open-source, and reasonably priced, reliable SaaS tools, there is no excuse for not having a python performance monitoring tool. A proactive approach helps to prevent or mitigate issues before they hit the end-user, while the reactive approach only hurts your reputation and drains funds but also your engineers.