Skip to main content

Dynatrace Announces Keptn

Dynatrace announced Keptn, an open source pluggable control plane to advance the industry movement toward autonomous clouds.

Keptn provides the automation and orchestration of the processes and tools needed for continuous delivery and automated operations for cloud-native environments.

To combat the growing gap between constrained IT resources and accelerating cloud scale and complexity, automation and AI have become critical weapons for IT to maintain control. However, where to start and how to map out a successful path to NoOps has been a barrier for many IT organizations.

“Keptn is an outcome of a belief and program we have been working on for years,” said John Van Siclen, CEO of Dynatrace. “In talking with CIOs and CTOs of our many enterprise customers, it’s become clear that advanced levels of automation and intelligence are required to bridge the growing gap between limited IT resources and the exponential increase in scale and complexity of dynamic enterprise clouds and the growing cloud native workloads now being deployed. We purpose built our new Dynatrace® platform with a powerful, explainable AI engine at the core to identify anomalies and degradations with precise root-cause to trigger automatic self-healing actions. But what’s been missing has been a simple, repeatable way to harness this potential and leverage it for a true NoOps approach. Keptn provides an answer and we are thrilled to offer it to the industry as an open source project.”

Dynatrace itself faced these development and operational challenges several years ago when it was reinventing its business and its core software intelligence platform. Ongoing customer interest in lessons learned and best practices developed during the company’s path to NoOps led Dynatrace to codify its know-how into Keptn. Making Keptn available to the open source community allows the entire industry to simplify and accelerate the inevitable movement to autonomous cloud operations, beginning with continuous delivery and automated operations as the first phases of the process.

“At Dynatrace we learned that the journey to NoOps starts with transforming how development and operations think, operate and align. Putting in place an unbreakable software delivery pipeline from ideation through to volume production allowed us to scale from 2 to 25 major releases per year, plus hundreds of fix and currency releases in between,” said Alois Reitbauer, Chief Technical Strategist and Head of the Dynatrace Innovation Lab. “As we spent more and more time with customers and partners interested in how we accomplished our transformation, we realized everyone was starting in different places with different tooling and different skill sets. With Keptn, we set out to simplify the journey to NoOps for others and we open sourced it to accelerate its adoption and functionality.”

Over the past 18 months, Dynatrace has been working with customers to advance autonomous cloud operations. After dozens of workshops and autonomous cloud labs (ACLs) with customers and partners, it was clear a simple pluggable control plane that automates the continuous delivery pipeline linking development with production would accelerate the NoOps journey for everyone. Keptn provides:

- A simple, declarative way to specify multiple continuous delivery pipelines for hundreds of microservices and automatically generate all the plumbing that underlies them. A multi-stage pipeline can be set up in minutes.

- An easily maintainable automation approach enabling customers to automate operational tasks like reacting to failed deployments based on performance and business feedback as well as automating remediation for production problems. Keptn’s control plane separates process definitions and actual tool integrations and orchestrates processes at runtime which increases manageability and adaptability.

- A high level of transparency, following a GitOps model, with distributed transaction tracing capabilities embedded into Keptn enables the stitching together of individual actions into traces and provides a deep level of visibility into automation tasks.

Keptn is also being leveraged by Dynatrace’s newly announced Autonomous Cloud Enablement (ACE) Practice, to help customers further accelerate DevOps’ movement to autonomous cloud through proven best practices and expert services.

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 Announces Keptn

Dynatrace announced Keptn, an open source pluggable control plane to advance the industry movement toward autonomous clouds.

Keptn provides the automation and orchestration of the processes and tools needed for continuous delivery and automated operations for cloud-native environments.

To combat the growing gap between constrained IT resources and accelerating cloud scale and complexity, automation and AI have become critical weapons for IT to maintain control. However, where to start and how to map out a successful path to NoOps has been a barrier for many IT organizations.

“Keptn is an outcome of a belief and program we have been working on for years,” said John Van Siclen, CEO of Dynatrace. “In talking with CIOs and CTOs of our many enterprise customers, it’s become clear that advanced levels of automation and intelligence are required to bridge the growing gap between limited IT resources and the exponential increase in scale and complexity of dynamic enterprise clouds and the growing cloud native workloads now being deployed. We purpose built our new Dynatrace® platform with a powerful, explainable AI engine at the core to identify anomalies and degradations with precise root-cause to trigger automatic self-healing actions. But what’s been missing has been a simple, repeatable way to harness this potential and leverage it for a true NoOps approach. Keptn provides an answer and we are thrilled to offer it to the industry as an open source project.”

Dynatrace itself faced these development and operational challenges several years ago when it was reinventing its business and its core software intelligence platform. Ongoing customer interest in lessons learned and best practices developed during the company’s path to NoOps led Dynatrace to codify its know-how into Keptn. Making Keptn available to the open source community allows the entire industry to simplify and accelerate the inevitable movement to autonomous cloud operations, beginning with continuous delivery and automated operations as the first phases of the process.

“At Dynatrace we learned that the journey to NoOps starts with transforming how development and operations think, operate and align. Putting in place an unbreakable software delivery pipeline from ideation through to volume production allowed us to scale from 2 to 25 major releases per year, plus hundreds of fix and currency releases in between,” said Alois Reitbauer, Chief Technical Strategist and Head of the Dynatrace Innovation Lab. “As we spent more and more time with customers and partners interested in how we accomplished our transformation, we realized everyone was starting in different places with different tooling and different skill sets. With Keptn, we set out to simplify the journey to NoOps for others and we open sourced it to accelerate its adoption and functionality.”

Over the past 18 months, Dynatrace has been working with customers to advance autonomous cloud operations. After dozens of workshops and autonomous cloud labs (ACLs) with customers and partners, it was clear a simple pluggable control plane that automates the continuous delivery pipeline linking development with production would accelerate the NoOps journey for everyone. Keptn provides:

- A simple, declarative way to specify multiple continuous delivery pipelines for hundreds of microservices and automatically generate all the plumbing that underlies them. A multi-stage pipeline can be set up in minutes.

- An easily maintainable automation approach enabling customers to automate operational tasks like reacting to failed deployments based on performance and business feedback as well as automating remediation for production problems. Keptn’s control plane separates process definitions and actual tool integrations and orchestrates processes at runtime which increases manageability and adaptability.

- A high level of transparency, following a GitOps model, with distributed transaction tracing capabilities embedded into Keptn enables the stitching together of individual actions into traces and provides a deep level of visibility into automation tasks.

Keptn is also being leveraged by Dynatrace’s newly announced Autonomous Cloud Enablement (ACE) Practice, to help customers further accelerate DevOps’ movement to autonomous cloud through proven best practices and expert services.

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 ...