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Dynatrace Extends Davis AI Engine to Enable Ad Hoc, Exploratory Analytics

Dynatrace has extended its Davis® AI engine to empower development, SRE, and IT teams to conduct ad hoc, exploratory analytics.

This allows teams to harness the power of Dynatrace’s causal AI to investigate newly emerging trends or block potential issues, like unexpected spikes in traffic or performance degradations.

This enhancement builds on the Davis engine’s existing capabilities, including automatic and continuous full-stack monitoring, processing trillions of dependencies in real time, and delivering precise answers and intelligent automation from data. As a result, teams can further optimize their applications and infrastructure and avoid problems long before they become customer-impacting issues to ensure their digital services are flawless and secure.

“Our Davis AI engine was purpose-built to help organizations overcome modern cloud complexity and ensure optimal user experience,” said Steve Tack, SVP of Product Management at Dynatrace. “By extending Davis to deliver on-demand, exploratory analytics, Dynatrace is well-positioned within the market to enable development, SRE, and IT teams to analyze any observability signal at any time with deterministic AI. This provides a more granular and comprehensive analysis of complex cloud environments, empowering teams to spend more of their time innovating.”

This enhancement to the Dynatrace platform will become generally available within 30 days of this announcement.

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Dynatrace Extends Davis AI Engine to Enable Ad Hoc, Exploratory Analytics

Dynatrace has extended its Davis® AI engine to empower development, SRE, and IT teams to conduct ad hoc, exploratory analytics.

This allows teams to harness the power of Dynatrace’s causal AI to investigate newly emerging trends or block potential issues, like unexpected spikes in traffic or performance degradations.

This enhancement builds on the Davis engine’s existing capabilities, including automatic and continuous full-stack monitoring, processing trillions of dependencies in real time, and delivering precise answers and intelligent automation from data. As a result, teams can further optimize their applications and infrastructure and avoid problems long before they become customer-impacting issues to ensure their digital services are flawless and secure.

“Our Davis AI engine was purpose-built to help organizations overcome modern cloud complexity and ensure optimal user experience,” said Steve Tack, SVP of Product Management at Dynatrace. “By extending Davis to deliver on-demand, exploratory analytics, Dynatrace is well-positioned within the market to enable development, SRE, and IT teams to analyze any observability signal at any time with deterministic AI. This provides a more granular and comprehensive analysis of complex cloud environments, empowering teams to spend more of their time innovating.”

This enhancement to the Dynatrace platform will become generally available within 30 days of this announcement.

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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 ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...