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Dynatrace Introduces Observability for AI

Dynatrace announced extended capabilities for the observability of customers’ GenAI initiatives. 

These advancements provide teams with access to comprehensive insights into their AI applications to drive reliability, performance, security and compliance. With this visibility, organizations now have clarity into their AI initiatives and can understand their return on investment (ROI).

Dynatrace is introducing a series of platform advancements, including:

  • Enhanced LLM Model Analytics: In addition to monitoring standard KPIs such as input and output errors, response times, and token consumption, the predictive capabilities of Dynatrace Davis AI® detect changes in usage behavior to predict and forecast cost changes associated with LLM usage. This helps teams understand model performance and optimization opportunities, including how they can better manage costs and control ROI.
  • LLM Input and Output Guardrails: Dynatrace safeguards the quality of AI application input and output to help build trust in AI. This enables customers to recognize model hallucinations, identify attempts at LLM misuse such as malicious prompt injection, prevent Personally Identifiable Information (PII) leakage, and detect toxic language.
  • Multi-model Tracing: Dynatrace maps dependencies between multiple LLMs that work in concert with Retrieval Augmented Generation (RAG) pipelines or agentic frameworks to provide end-to-end observability of the entire system, not just the component parts. This gives teams the insight to verify that dependencies are interacting seamlessly so they can deliver optimal end-user experiences.
  • Responsible AI Integrations: Dynatrace helps organizations with AI governance by tracking every input and output without sampling to provide an audit trail of monitoring and observability, including documenting what training data was used for a given model. Through Dynatrace Grail™, all data can be queried in real time and stored for future reference.

“We see a large portion of our global customer base moving their AI applications into production. AI Observability is key for ROI, governance, and explainability,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “Dynatrace delivers AI-powered observability with real-time insights which enables data and systems to work together effortlessly. At Perform 2025, we’re excited to showcase how we’re leveraging these capabilities to power new possibilities for our customers, highlighting the transformative innovation they’re driving through the ability to effectively understand and optimize their AI deployments.”

Dynatrace supports customers now with its Observability for AI solutions.

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 ...

Dynatrace Introduces Observability for AI

Dynatrace announced extended capabilities for the observability of customers’ GenAI initiatives. 

These advancements provide teams with access to comprehensive insights into their AI applications to drive reliability, performance, security and compliance. With this visibility, organizations now have clarity into their AI initiatives and can understand their return on investment (ROI).

Dynatrace is introducing a series of platform advancements, including:

  • Enhanced LLM Model Analytics: In addition to monitoring standard KPIs such as input and output errors, response times, and token consumption, the predictive capabilities of Dynatrace Davis AI® detect changes in usage behavior to predict and forecast cost changes associated with LLM usage. This helps teams understand model performance and optimization opportunities, including how they can better manage costs and control ROI.
  • LLM Input and Output Guardrails: Dynatrace safeguards the quality of AI application input and output to help build trust in AI. This enables customers to recognize model hallucinations, identify attempts at LLM misuse such as malicious prompt injection, prevent Personally Identifiable Information (PII) leakage, and detect toxic language.
  • Multi-model Tracing: Dynatrace maps dependencies between multiple LLMs that work in concert with Retrieval Augmented Generation (RAG) pipelines or agentic frameworks to provide end-to-end observability of the entire system, not just the component parts. This gives teams the insight to verify that dependencies are interacting seamlessly so they can deliver optimal end-user experiences.
  • Responsible AI Integrations: Dynatrace helps organizations with AI governance by tracking every input and output without sampling to provide an audit trail of monitoring and observability, including documenting what training data was used for a given model. Through Dynatrace Grail™, all data can be queried in real time and stored for future reference.

“We see a large portion of our global customer base moving their AI applications into production. AI Observability is key for ROI, governance, and explainability,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “Dynatrace delivers AI-powered observability with real-time insights which enables data and systems to work together effortlessly. At Perform 2025, we’re excited to showcase how we’re leveraging these capabilities to power new possibilities for our customers, highlighting the transformative innovation they’re driving through the ability to effectively understand and optimize their AI deployments.”

Dynatrace supports customers now with its Observability for AI solutions.

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 ...