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Dynatrace Expands Support for Kubernetes

Dynatrace announced that its open AI engine, Davis, now provides even smarter and more precise answers and actionable insights about Kubernetes environments.

Through automatically ingesting new Kubernetes cluster and node health, and utilization metrics into Davis and combining them with the rich, high-fidelity application and transaction data that Dynatrace already collects, enterprises can create successful Kubernetes deployments, accelerate innovation through DevOps and increase competitiveness by bringing new services to market faster.

New, out-of-the-box Kubernetes dashboards and advanced filtering capabilities allow cloud teams to filter and find the right information quickly, so they can analyze and optimize Kubernetes workloads and manage cluster and node health with ease.

Kubernetes is being widely adopted to accelerate digital transformation and achieve greater agility. But the highly dynamic nature of Kubernetes, and the sprawl of Kubernetes orchestrated cloud native workloads can be problematic for enterprises to manage without real-time visibility and automatic intelligence.

“We anticipated this highly dynamic, hybrid cloud world five years ago and purpose-built our Dynatrace platform for microservice and container-based environments like Kubernetes,” explains Steve Tack, SVP of Product Management at Dynatrace. “Not only did we figure out how to automatically instrument a Kubernetes environment, both container and container payloads, we can also analyze a Kubernetes orchestrated cloud in real-time with a deterministic AI engine we call Davis. We did this so that DevOps and IT Operations teams can innovate and automate faster with confidence. Today, we are making Dynatrace even smarter by bringing Kubernetes cluster and node health, and utilization metrics and dashboards into our open platform.”

“Dynatrace works seamlessly with our Kubernetes environment to provide precise answers that help us to innovate faster,” says Felix Gratz, Application Performance Management and System Architecture at Daimler AG. “We adopted Kubernetes because it would help us accelerate time-to-market, and Dynatrace helps us to do just that. Dynatrace is a great solution that automates the monitoring of Kubernetes workloads at scale and provides AI-powered answers, allowing us to focus our efforts on innovation.”

Purpose-built for dynamic, container-based cloud environments, Dynatrace’s software intelligence platform has three critical differentiators that overcome challenges faced by do-it-yourself or traditional monitoring solutions in Kubernetes environments:

1. Automatic - With OneAgent, Dynatrace automatically configures, and discovers all components of the full stack, including short-lived containers and new services as they spin up. Other solutions require each container to be manually instrumented, which can’t be done and creates microservices and container blind spots.

2. Full stack - Our SmartScape technology dynamically maps the complete topology of the full stack and its dependencies across the enterprise cloud. This map is continuously updated in real-time to provide a comprehensive view of the infrastructure, the container orchestration, the services, and the applications, including how they are connected, and how they are performing. This is particularly valuable in a highly dynamic environment like Kubernetes.

3. Precise Answers - Our AI engine, Davis continually learns what normal performance is, for a Kubernetes cloud environment, processing billions of dependencies in milliseconds. Davis provides precise root cause answers to problems, automatic insight into user experience and behavior, and real-time business impact of issues. This enables faster decision making, greater optimization of IT resources, and better business outcomes. The automatic ingestion of Kubernetes cluster and node health, and utilization metrics now makes Davis that much smarter.

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Dynatrace Expands Support for Kubernetes

Dynatrace announced that its open AI engine, Davis, now provides even smarter and more precise answers and actionable insights about Kubernetes environments.

Through automatically ingesting new Kubernetes cluster and node health, and utilization metrics into Davis and combining them with the rich, high-fidelity application and transaction data that Dynatrace already collects, enterprises can create successful Kubernetes deployments, accelerate innovation through DevOps and increase competitiveness by bringing new services to market faster.

New, out-of-the-box Kubernetes dashboards and advanced filtering capabilities allow cloud teams to filter and find the right information quickly, so they can analyze and optimize Kubernetes workloads and manage cluster and node health with ease.

Kubernetes is being widely adopted to accelerate digital transformation and achieve greater agility. But the highly dynamic nature of Kubernetes, and the sprawl of Kubernetes orchestrated cloud native workloads can be problematic for enterprises to manage without real-time visibility and automatic intelligence.

“We anticipated this highly dynamic, hybrid cloud world five years ago and purpose-built our Dynatrace platform for microservice and container-based environments like Kubernetes,” explains Steve Tack, SVP of Product Management at Dynatrace. “Not only did we figure out how to automatically instrument a Kubernetes environment, both container and container payloads, we can also analyze a Kubernetes orchestrated cloud in real-time with a deterministic AI engine we call Davis. We did this so that DevOps and IT Operations teams can innovate and automate faster with confidence. Today, we are making Dynatrace even smarter by bringing Kubernetes cluster and node health, and utilization metrics and dashboards into our open platform.”

“Dynatrace works seamlessly with our Kubernetes environment to provide precise answers that help us to innovate faster,” says Felix Gratz, Application Performance Management and System Architecture at Daimler AG. “We adopted Kubernetes because it would help us accelerate time-to-market, and Dynatrace helps us to do just that. Dynatrace is a great solution that automates the monitoring of Kubernetes workloads at scale and provides AI-powered answers, allowing us to focus our efforts on innovation.”

Purpose-built for dynamic, container-based cloud environments, Dynatrace’s software intelligence platform has three critical differentiators that overcome challenges faced by do-it-yourself or traditional monitoring solutions in Kubernetes environments:

1. Automatic - With OneAgent, Dynatrace automatically configures, and discovers all components of the full stack, including short-lived containers and new services as they spin up. Other solutions require each container to be manually instrumented, which can’t be done and creates microservices and container blind spots.

2. Full stack - Our SmartScape technology dynamically maps the complete topology of the full stack and its dependencies across the enterprise cloud. This map is continuously updated in real-time to provide a comprehensive view of the infrastructure, the container orchestration, the services, and the applications, including how they are connected, and how they are performing. This is particularly valuable in a highly dynamic environment like Kubernetes.

3. Precise Answers - Our AI engine, Davis continually learns what normal performance is, for a Kubernetes cloud environment, processing billions of dependencies in milliseconds. Davis provides precise root cause answers to problems, automatic insight into user experience and behavior, and real-time business impact of issues. This enables faster decision making, greater optimization of IT resources, and better business outcomes. The automatic ingestion of Kubernetes cluster and node health, and utilization metrics now makes Davis that much smarter.

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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 ...

Image
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 ...

Image
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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...