Dynatrace, today at its Perform 2025 conference, extended the reach of its Davis artificial intelligence (AI) engine to provide predictive and generative capabilities that complement the causal analytics already provided.
Additionally, Dynatrace unveiled an Observability for Developers module for its platform that includes a Live Debugger tool that will be made generally available in 90 days. Based on technology Dynatrace gained with the acquisition of Rookout, that tool makes it possible for developers to, for example, understand in real time how changes to code are impacting the performance of applications running in a production environment.
Finally, Dynatrace revealed that within 90 days it will be adding a Cloud Security Posture Management (CSPM) offering that extends the application security reach it enables beyond previous capabilities it made available for Kubernetes clusters to now include continuous monitoring for a wider range of platforms that can be used to ensure compliance with a range of regulations.
Alois Reitbauer, chief technology strategist for Dynatrace, said the goal is to provide IT operations, application developers and business leaders with dashboards based on the same data that is presented in a dashboard designed for the specific needs. The Dynatrace Grail data lakehouse that is at the core of the observability platform combined with causal, generative and predictive AI models adds the ability to maintain context as these teams collaborate to resolve issues, he added. Dynatrace is further working to extend the capabilities of Grail to include support for a vector database that can be used to enable usage of retrieval-augmented generation (RAG) to expose AI models to additional external data.
Historically, IT teams have often found it challenging to collaborate because it wasn’t possible for IT operations teams to, for example, surface issues in a way that application development teams could easily comprehend using a set of templates and, if needed, pre-configured instances of different types of software, such as the Backstage integrated development platform (IDP). As AI continues to evolve, it will becoming increasingly easier for IT teams to first drill down into the root cause of the issue and then evaluate the recommendations being made by AI models to resolve them, said Reitbauer.
The cost of achieving the level of observability will be contained as Dynatrace moves toward a consumption-based pricing model versus a seat-based pricing model that is commonly used by other providers of DevOps tools and platforms, he added.
Mitch Ashley, vice president and practice lead for DevOps and application development at The Futurum Group, added that the integration of live production observability will streamline the arduous tasks for developers by matching up code to telemetry data such as events and logs at scale. In effect Dynatrace is unifying observability for developers, platform engineers, site reliability engineers (SREs) and cybersecurity teams, he noted.
However, Dynatrace will still rely on partners such as Tricentis NeoLoad to collect data and integrate application testing tools into its observability platform.
Bryan Cole, director for customer engineering at Tricentis, said that level of integration is why Dynatrace is a strategic partner that enables the two companies to collaboratively close the divide that currently exists between many application development and IT operations teams.
Overall, Dynatrace is working toward enabling organizations to embrace platform engineering as a methodology for managing DevOps workflows at scale. The challenge each organization will need to determine for itself is to what degree to standardize on specific interfaces and platforms to reduce costs and toil versus leaving other tooling decisions in the hands of software engineers that might have specific preferences of their own.
