Skip to main content

Gremlin Launches Native App for Dynatrace

Gremlin, the reliability management platform trusted by the world's largest enterprises, announced the Gremlin app for Dynatrace

The app lets engineering teams run Gremlin resilience tests, see their impact on systems, and track forward-looking reliability scores directly within Dynatrace, extending the world-class observability platform's capabilities so teams can also validate and measure the resilience of every service.

An effective reliability practice is built on two complementary disciplines: observability, which gives teams real-time visibility into system performance, and reliability testing, which validates how those systems will respond to failures. The Gremlin app for Dynatrace brings these together into a single workspace, using the Dynatrace metrics and alerts teams already trust as the foundation for proactive reliability testing.

With the app, teams can:

  • Test services within Dynatrace: run a service's full test suite or specific tests using existing Dynatrace alerts and events as health checks and automated halt conditions to stop tests if services move outside defined thresholds.
  • See results using trusted metrics: every test result is cross-referenced against key Dynatrace metrics, including response time, requests per minute, and failure rate, with a full pass/fail history for each service.
  • Bring reliability scores into existing dashboards: Gremlin reliability scores, test results, and pass rates appear in Dynatrace dashboards, so forward-looking reliability data joins the view executives and stakeholders already trust.

The app builds on Gremlin's existing Dynatrace integration, adding a native user interface and reliability scores within Dynatrace dashboards. It supports continuous reliability testing, disaster recovery validation, risk detection, dependency mapping, and reliability reporting and governance. Gremlin's production safety controls—blast radius management, halt conditions, and automatic rollback—apply throughout, and the app works across bare metal, on-prem, multi-cloud, and serverless environments.

"Dynatrace shows teams exactly how their systems are performing," said Kolton Andrus, CEO and Co-founder of Gremlin. "Gremlin takes that same trusted data and uses it to prove how those systems will perform under failure, then turns it into a reliability score teams can track over time.Together, they give teams the full picture: what's happening now, and what will happen when something breaks."

"Our customers rely on Dynatrace as their source of truth for the health of their systems," said Philippe Deblois, Global Vice President, Solutions Engineering at Dynatrace. "Bringing Gremlin's best-in-class enterprise reliability testing and scoring into that environment is a natural extension of the value our platform delivers. It gives teams a way to validate resilience and act on forward-looking insight, all powered by the Dynatrace data and alerts they already depend on every day."

Customers using Gremlin have achieved outcomes including a 50% reduction in downtime, a 90% reduction in disaster recovery testing time, and 99.99% availability on critical platforms.

The Gremlin app for Dynatrace is available now in the Dynatrace Hub. 

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

Gremlin Launches Native App for Dynatrace

Gremlin, the reliability management platform trusted by the world's largest enterprises, announced the Gremlin app for Dynatrace

The app lets engineering teams run Gremlin resilience tests, see their impact on systems, and track forward-looking reliability scores directly within Dynatrace, extending the world-class observability platform's capabilities so teams can also validate and measure the resilience of every service.

An effective reliability practice is built on two complementary disciplines: observability, which gives teams real-time visibility into system performance, and reliability testing, which validates how those systems will respond to failures. The Gremlin app for Dynatrace brings these together into a single workspace, using the Dynatrace metrics and alerts teams already trust as the foundation for proactive reliability testing.

With the app, teams can:

  • Test services within Dynatrace: run a service's full test suite or specific tests using existing Dynatrace alerts and events as health checks and automated halt conditions to stop tests if services move outside defined thresholds.
  • See results using trusted metrics: every test result is cross-referenced against key Dynatrace metrics, including response time, requests per minute, and failure rate, with a full pass/fail history for each service.
  • Bring reliability scores into existing dashboards: Gremlin reliability scores, test results, and pass rates appear in Dynatrace dashboards, so forward-looking reliability data joins the view executives and stakeholders already trust.

The app builds on Gremlin's existing Dynatrace integration, adding a native user interface and reliability scores within Dynatrace dashboards. It supports continuous reliability testing, disaster recovery validation, risk detection, dependency mapping, and reliability reporting and governance. Gremlin's production safety controls—blast radius management, halt conditions, and automatic rollback—apply throughout, and the app works across bare metal, on-prem, multi-cloud, and serverless environments.

"Dynatrace shows teams exactly how their systems are performing," said Kolton Andrus, CEO and Co-founder of Gremlin. "Gremlin takes that same trusted data and uses it to prove how those systems will perform under failure, then turns it into a reliability score teams can track over time.Together, they give teams the full picture: what's happening now, and what will happen when something breaks."

"Our customers rely on Dynatrace as their source of truth for the health of their systems," said Philippe Deblois, Global Vice President, Solutions Engineering at Dynatrace. "Bringing Gremlin's best-in-class enterprise reliability testing and scoring into that environment is a natural extension of the value our platform delivers. It gives teams a way to validate resilience and act on forward-looking insight, all powered by the Dynatrace data and alerts they already depend on every day."

Customers using Gremlin have achieved outcomes including a 50% reduction in downtime, a 90% reduction in disaster recovery testing time, and 99.99% availability on critical platforms.

The Gremlin app for Dynatrace is available now in the Dynatrace Hub. 

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...