Skip to main content

AppDynamics Releases Version 2.0 of Free Java Performance Solution

AppDynamics, a provider of application performance management (APM) for the cloud generation, released version 2.0 of its free java troubleshooting solution, AppDynamics Lite.

The new version dramatically expands the solution’s capabilities by adding visibility into JMX metrics as well as offering proactive alerting, enabling application Development and Operations teams to be notified when application performance degrades in production.

The free Java troubleshooting product, which recently surpassed over 50,000 downloads, supports IT Ops and Dev teams who need to rapidly troubleshoot and diagnose application performance problems in production. The solution installs in minutes, identifies and monitors an application’s business transactions, and gives immediate insight into common application issues such as slow SQL, stalls, errors, and slow response time.

AppDynamics Lite runs with less than 2% overhead in most production environments, yet gives complete visibility into a single Java Virtual Machine (JVM), as well as code-level details when performance problems arise. This makes the solution suitable for tomcat monitoring, jboss monitoring, weblogic monitoring, websphere monitoring, and any other application sever that runs on Java 1.5 and above.

The Latest

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...

AppDynamics Releases Version 2.0 of Free Java Performance Solution

AppDynamics, a provider of application performance management (APM) for the cloud generation, released version 2.0 of its free java troubleshooting solution, AppDynamics Lite.

The new version dramatically expands the solution’s capabilities by adding visibility into JMX metrics as well as offering proactive alerting, enabling application Development and Operations teams to be notified when application performance degrades in production.

The free Java troubleshooting product, which recently surpassed over 50,000 downloads, supports IT Ops and Dev teams who need to rapidly troubleshoot and diagnose application performance problems in production. The solution installs in minutes, identifies and monitors an application’s business transactions, and gives immediate insight into common application issues such as slow SQL, stalls, errors, and slow response time.

AppDynamics Lite runs with less than 2% overhead in most production environments, yet gives complete visibility into a single Java Virtual Machine (JVM), as well as code-level details when performance problems arise. This makes the solution suitable for tomcat monitoring, jboss monitoring, weblogic monitoring, websphere monitoring, and any other application sever that runs on Java 1.5 and above.

The Latest

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...