At its LM Elevate user conference, LogicMonitor today formally launched LM Envision, an observability platform that incorporates artificial intelligence (AI) to both discover and then remediate issues.
LogicMonitor CEO Christina Kosmowski said LM Envision is the only observability platform available via a software-as-a-service (SaaS) model that can be applied to both on-premises and cloud applications. In total, there are already more than 2,000 integrations for the platform as part of the open LM Envision ecosystem, she noted.
Over the last year, additional capabilities have been developed and added to the observability platform. These include application performance monitoring (APM) tools to identify bottlenecks and optimize performance, expanded support for public clouds and Kubernetes environments, integration with the StackStorm automation platform, integration with Terraform infrastructure-as-code tools and more advanced tools for searching logs.
The company has also extended its AI operations tools to provide more context when troubleshooting issues by aggregating logs and including topology tools and graphs.
Finally, LogicMonitor has added a push metrics application programming interface (API) to make it simpler to ingest data, pre-integrated dashboards and support for open source OpenTelemetry agent software.
Kosmowski said it’s only a matter of time before observability and AIOps converge to the point where IT teams will finally be able to spend less time managing platforms. One of the major challenges IT teams face is that each platform they use requires its own framework to monitor and manage it. As observability and management frameworks converge, the overall overhead associated with managing modern IT environments should steadily decline as machine learning algorithms make it easier to surface issues before they create a major disruption, she noted.
The overall goal is to close the loop between the discovery of an IT issue and its remediation, Kosmowski added.
Observability, of course, is one of the core tenets of any set of DevOps best practices. Achieving it, however, has always been a challenge. The goal now is to eventually replace the hodgepodge of monitoring tools that track a limited set of pre-defined metrics with an observability platform that not only aggregates metrics but enables DevOps teams to launch queries and surface IT issues. In some instances, the rationalization of monitoring tools will help justify the acquisition of an observability platform. The paradox is that, as IT environments become more complex, it may actually become easier to manage those environments as more applications become instrumented and the various silos that isolate IT teams from one another start to collapse.
In the meantime, there is no shortage of observability platform options. The primary difference between each of them is the degree of integration they provide and their ability to employ AIOps to automatically resolve issues, said Kosmowski. Each minute DevOps teams don’t spend on managing IT platforms is a minute that can be applied to spurring innovation, she added.
One way or another, DevOps teams will soon have more access to information about their IT environments than ever. Less clear is how much of the data being collected by observability platforms will actually constitute actionable intelligence. But as observability continues to evolve, it’s now more a question of the degree to which that goal will be achieved versus if or when.
