Skip to main content

Dynatrace Joins Forces with Technology Leaders to Launch OpenFeature

Dynatrace, along with a consortium of other technology leaders, has submitted OpenFeature to the Cloud Native Computing Foundation (CNCF) for consideration as a Sandbox project.

The consortium includes Dynatrace, LaunchDarkly, GitLab, Split, Flagsmith, and CloudBees.

Feature flagging and management solutions enable DevOps and SRE teams to turn application functionality on and off at runtime without deploying new code. This simplifies and accelerates the delivery of personalized experiences, A/B testing, and the mitigation of potential issues. This approach is essential for modern continuous delivery practices. However, individual teams within organizations often use a variety of feature flagging and management solutions, each with a unique, proprietary approach. This results in significant integration work and vendor lock-in.

OpenFeature will address these challenges by enabling teams to use any feature flagging or management solution that supports this standard without additional integration or re-architecting. In addition, by making it easier to integrate feature flagging and management into the DevOps toolchain, OpenFeature will allow Dynatrace customers to seamlessly extend answers and intelligent automation to feature-flag enabled applications at massive scale.

The need for a standard that simplifies approaches to feature flagging and management is underscored by companies in the consortium, as well as industry analysts:

Alois Reitbauer, CPO of Cloud Automation at Dynatrace, said: “Currently, the extensive variety of approaches makes it difficult to configure and integrate feature flagging into a broader development toolchain and delivery pipeline. OpenFeature enables cloud-native teams to optimize the release of new features and embrace progressive delivery, along with other modern SRE and DevOps practices. At Dynatrace, we will natively embed this information into our Davis AIOps engine to help teams understand the impact of features on performance, resilience, and digital experience. By working with a consortium of industry leaders to create this new open standard, we are taking a big step to reduce complexity and free more time for innovators to deliver new and amazing digital experiences for their customers.”

This announcement builds on Dynatrace’s involvement in open source initiatives, including Keptn, W3C trace-context, and OpenTelemetry, which enable organizations to more effectively drive large-scale automation.

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

Dynatrace Joins Forces with Technology Leaders to Launch OpenFeature

Dynatrace, along with a consortium of other technology leaders, has submitted OpenFeature to the Cloud Native Computing Foundation (CNCF) for consideration as a Sandbox project.

The consortium includes Dynatrace, LaunchDarkly, GitLab, Split, Flagsmith, and CloudBees.

Feature flagging and management solutions enable DevOps and SRE teams to turn application functionality on and off at runtime without deploying new code. This simplifies and accelerates the delivery of personalized experiences, A/B testing, and the mitigation of potential issues. This approach is essential for modern continuous delivery practices. However, individual teams within organizations often use a variety of feature flagging and management solutions, each with a unique, proprietary approach. This results in significant integration work and vendor lock-in.

OpenFeature will address these challenges by enabling teams to use any feature flagging or management solution that supports this standard without additional integration or re-architecting. In addition, by making it easier to integrate feature flagging and management into the DevOps toolchain, OpenFeature will allow Dynatrace customers to seamlessly extend answers and intelligent automation to feature-flag enabled applications at massive scale.

The need for a standard that simplifies approaches to feature flagging and management is underscored by companies in the consortium, as well as industry analysts:

Alois Reitbauer, CPO of Cloud Automation at Dynatrace, said: “Currently, the extensive variety of approaches makes it difficult to configure and integrate feature flagging into a broader development toolchain and delivery pipeline. OpenFeature enables cloud-native teams to optimize the release of new features and embrace progressive delivery, along with other modern SRE and DevOps practices. At Dynatrace, we will natively embed this information into our Davis AIOps engine to help teams understand the impact of features on performance, resilience, and digital experience. By working with a consortium of industry leaders to create this new open standard, we are taking a big step to reduce complexity and free more time for innovators to deliver new and amazing digital experiences for their customers.”

This announcement builds on Dynatrace’s involvement in open source initiatives, including Keptn, W3C trace-context, and OpenTelemetry, which enable organizations to more effectively drive large-scale automation.

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...