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Dynatrace Releases AI Observability

Dynatrace released Dynatrace® AI Observability, extending its analytics and automation platform to provide holistic observability and security for large language models (LLMs) and generative AI-powered applications.

This enhancement to the Dynatrace® platform enables organizations worldwide to embrace generative AI confidently and cost-effectively as part of their focus on increasing innovation, productivity, and revenue.

Dynatrace® AI Observability is a comprehensive solution. It covers the end-to-end AI stack, including infrastructure, such as Nvidia® GPUs, foundational models, such as GPT4, semantic caches and vector databases, such as Weaviate, and orchestration frameworks, such as LangChain. It also supports the major platforms for building, training, and delivering AI models, including Microsoft® Azure OpenAI Service, Amazon® SageMaker, and Google® AI Platform.

Dynatrace AI Observability leverages the platform’s Davis® AI and other core technologies to deliver a precise and complete view of AI-powered applications. As a result, organizations can provide great user experiences while identifying performance bottlenecks and root causes automatically. Dynatrace AI Observability with Davis AI also helps them comply with privacy and security regulations and governance standards by tracing the origins of the output created by their apps with precision. Additionally, it helps them forecast and control costs by monitoring their consumption of tokens, which are the basic units that generative AI models use to process queries.

“Generative AI is the new frontier of digital transformation,” said Bernd Greifeneder, CTO at Dynatrace. “This technology enables organizations to create innovative solutions that boost productivity, profitability, and competitiveness. While transformational, it also poses new challenges for security, transparency, reliability, experience, and cost management. Organizations need AI observability that covers every aspect of their generative AI solutions to overcome these challenges. Dynatrace is extending its observability and AI leadership to meet this need, helping customers to embrace AI confidently and securely with unparalleled insights into their generative AI-driven applications.”

Dynatrace AI Observability is available now for all Dynatrace customers.

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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Dynatrace Releases AI Observability

Dynatrace released Dynatrace® AI Observability, extending its analytics and automation platform to provide holistic observability and security for large language models (LLMs) and generative AI-powered applications.

This enhancement to the Dynatrace® platform enables organizations worldwide to embrace generative AI confidently and cost-effectively as part of their focus on increasing innovation, productivity, and revenue.

Dynatrace® AI Observability is a comprehensive solution. It covers the end-to-end AI stack, including infrastructure, such as Nvidia® GPUs, foundational models, such as GPT4, semantic caches and vector databases, such as Weaviate, and orchestration frameworks, such as LangChain. It also supports the major platforms for building, training, and delivering AI models, including Microsoft® Azure OpenAI Service, Amazon® SageMaker, and Google® AI Platform.

Dynatrace AI Observability leverages the platform’s Davis® AI and other core technologies to deliver a precise and complete view of AI-powered applications. As a result, organizations can provide great user experiences while identifying performance bottlenecks and root causes automatically. Dynatrace AI Observability with Davis AI also helps them comply with privacy and security regulations and governance standards by tracing the origins of the output created by their apps with precision. Additionally, it helps them forecast and control costs by monitoring their consumption of tokens, which are the basic units that generative AI models use to process queries.

“Generative AI is the new frontier of digital transformation,” said Bernd Greifeneder, CTO at Dynatrace. “This technology enables organizations to create innovative solutions that boost productivity, profitability, and competitiveness. While transformational, it also poses new challenges for security, transparency, reliability, experience, and cost management. Organizations need AI observability that covers every aspect of their generative AI solutions to overcome these challenges. Dynatrace is extending its observability and AI leadership to meet this need, helping customers to embrace AI confidently and securely with unparalleled insights into their generative AI-driven applications.”

Dynatrace AI Observability is available now for all Dynatrace customers.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...