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AppDynamics Releases Application Performance Monitoring for Python Applications

AppDynamics announced the general availability of application performance monitoring for Python applications.

The AppDynamics APM Solution for Python brings all the capabilities of the AppDynamics Application Intelligence Platform to Python applications, including automatic application flow-mapping; real-time, end-to-end, code-level visibility to enable rapid problem resolution; and database and infrastructure monitoring. The solution monitors Python applications in both development and live production environments.

First introduced in the early 1990s, Python today is used by hundreds of thousands of programmers. The Python community has a reputation for valuing clarity, simplicity, readability and extensibility — qualities that align well with the AppDynamics Application Intelligence Platform’s automatic instrumentation, clear and comprehensive flow mapping, rapid time-to-value, and extensive library of community-contributed extensions.

As the newest language in the AppDynamics APM family, Python applications now benefit from the tremendous scalability of the platform and the ability to see application and transaction performance across complex and highly distributed environments. AppDynamics APM for Python automatically discovers and maps application topology and dependencies, including other applications, web services, databases, and underlying infrastructure, providing visibility into the components and processes that can impact Python performance.

Specific functionality for Python developers and operations teams includes:

- Code-level application monitoring: Visual drill-down on transaction snapshots enables rapid identification and resolution of hot spots and slow methods to minimize impact on users.

- Key business transactions monitoring: The platform is able to correlate and trace key business transactions end-to-end across complex, distributed environments to understand how application and infrastructure performance impact business outcomes.

- Errors and exceptions detection in real time: The platform detects and shows errors so they can be quickly resolved, and enables proactive addressing of exceptions via policy-based runbook automation.

- Heterogenous database performance management: Unique agentless database monitoring technology shows how database performance impacts Python applications.

- Infrastructure performance correlation: Visibility into infrastructure behavior shows its impact on application performance, with the ability to drill down to virtual machines, containers, servers, network, or storage, whether in the cloud or on-premises.

“With the growing popularity of Python, developers and IT ops teams need the kind of visibility that AppDynamics delivers,” said Bhaskar Sunkara, chief technology officer and senior vice president of product management at AppDynamics. “With our solution, you can monitor Python applications in real time, drill down into call stacks, correlate transactions as they traverse distributed environments, and diagnose bottlenecks in development and in production. These are the necessary capabilities to keep Python applications — and all other applications — performing at their best.”

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AppDynamics Releases Application Performance Monitoring for Python Applications

AppDynamics announced the general availability of application performance monitoring for Python applications.

The AppDynamics APM Solution for Python brings all the capabilities of the AppDynamics Application Intelligence Platform to Python applications, including automatic application flow-mapping; real-time, end-to-end, code-level visibility to enable rapid problem resolution; and database and infrastructure monitoring. The solution monitors Python applications in both development and live production environments.

First introduced in the early 1990s, Python today is used by hundreds of thousands of programmers. The Python community has a reputation for valuing clarity, simplicity, readability and extensibility — qualities that align well with the AppDynamics Application Intelligence Platform’s automatic instrumentation, clear and comprehensive flow mapping, rapid time-to-value, and extensive library of community-contributed extensions.

As the newest language in the AppDynamics APM family, Python applications now benefit from the tremendous scalability of the platform and the ability to see application and transaction performance across complex and highly distributed environments. AppDynamics APM for Python automatically discovers and maps application topology and dependencies, including other applications, web services, databases, and underlying infrastructure, providing visibility into the components and processes that can impact Python performance.

Specific functionality for Python developers and operations teams includes:

- Code-level application monitoring: Visual drill-down on transaction snapshots enables rapid identification and resolution of hot spots and slow methods to minimize impact on users.

- Key business transactions monitoring: The platform is able to correlate and trace key business transactions end-to-end across complex, distributed environments to understand how application and infrastructure performance impact business outcomes.

- Errors and exceptions detection in real time: The platform detects and shows errors so they can be quickly resolved, and enables proactive addressing of exceptions via policy-based runbook automation.

- Heterogenous database performance management: Unique agentless database monitoring technology shows how database performance impacts Python applications.

- Infrastructure performance correlation: Visibility into infrastructure behavior shows its impact on application performance, with the ability to drill down to virtual machines, containers, servers, network, or storage, whether in the cloud or on-premises.

“With the growing popularity of Python, developers and IT ops teams need the kind of visibility that AppDynamics delivers,” said Bhaskar Sunkara, chief technology officer and senior vice president of product management at AppDynamics. “With our solution, you can monitor Python applications in real time, drill down into call stacks, correlate transactions as they traverse distributed environments, and diagnose bottlenecks in development and in production. These are the necessary capabilities to keep Python applications — and all other applications — performing at their best.”

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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