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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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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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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 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...