Skip to main content

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.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...