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Dynatrace Announces Early Access for Joint Google Cloud Customers

Dynatrace announced early access for joint Google Cloud customers to its most recent platform innovations. 

These innovations are powered by the Dynatrace Grail™ data lakehouse that retains context across all data types – including logs, metrics, traces, events, and more – to provide customers with precise, actionable answers.

Dynatrace Grail enables organizations to extract real-time, actionable intelligence from their data. It brings together observability, security, and business data, allowing businesses to swiftly derive insights, boost operational performance and stay ahead in Google Cloud environments.

With real-time data processing and advanced automation, Grail also helps organizations improve efficiencies to make smarter, data-driven decisions that directly contribute to business growth and competitive advantage. Other key benefits include:

  • AI-Driven Precision: The combination of Davis® AI and Grail enables accurate, real-time insights for informed decision-making and faster issue resolution.
  • Scalable Data Processing: Grail’s cloud-native architecture delivers the access and speed of hot storage for all data with the cost efficiency of cold-tier storage. It eliminates the time-consuming and costly re-indexing and rehydration operations that are inherent to competitive observability solutions.
  • Seamless Integration with Google Cloud: Organizations can unify and analyze data within their existing cloud ecosystems, enhancing performance and security.
  • Easy Access to Dynatrace Data for Developers: With Google’s Gemini Code Assist, an AI-powered coding assistant that helps developers with various tasks – including code generation, completion, and debugging, directly within their IDEs – developers can access critical Dynatrace data, including data related to potential issues, without disrupting their flow state. This enables faster issue resolution and continuous innovation.
  • Observability for End-to-End Multimodal AI Models: Users can track and monitor the consumption, cost, and performance of AI services and models provided by Google’s Gemini models – a family of multimodal AI models designed to understand and generate text, images, audio, videos, and code – at scale.

“AI-powered observability has the power to transform how organizations manage their cloud-native environments,” said Ritika Suri, Managing Director of AI & Data ISV Partnerships at Google Cloud. “Solutions like Dynatrace Grail, integrated with Google Cloud's leading AI and infrastructure, enable customers to streamline operations, enhance efficiency, and confidently drive innovation.”

“Grail has been a game changer for Dynatrace, setting us apart by delivering real-time insights at an unprecedented scale,” said Jay Snyder, SVP of Partners and Alliances, Dynatrace. “By combining the capabilities of Grail with the flexibility and power of Google Cloud, Dynatrace empowers customers to innovate faster, operate more efficiently, and achieve their digital transformation goals with confidence.”

The early access program presents an opportunity for Google Cloud customers to adopt next-generation observability technology, positioning them at the forefront of cloud-native transformation. Through this integration, enterprises can modernize operations, improve system reliability, and unlock new opportunities for growth and efficiency.

Dynatrace expects the Grail data lakehouse on Google Cloud to be generally available by June 30. 

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Dynatrace Announces Early Access for Joint Google Cloud Customers

Dynatrace announced early access for joint Google Cloud customers to its most recent platform innovations. 

These innovations are powered by the Dynatrace Grail™ data lakehouse that retains context across all data types – including logs, metrics, traces, events, and more – to provide customers with precise, actionable answers.

Dynatrace Grail enables organizations to extract real-time, actionable intelligence from their data. It brings together observability, security, and business data, allowing businesses to swiftly derive insights, boost operational performance and stay ahead in Google Cloud environments.

With real-time data processing and advanced automation, Grail also helps organizations improve efficiencies to make smarter, data-driven decisions that directly contribute to business growth and competitive advantage. Other key benefits include:

  • AI-Driven Precision: The combination of Davis® AI and Grail enables accurate, real-time insights for informed decision-making and faster issue resolution.
  • Scalable Data Processing: Grail’s cloud-native architecture delivers the access and speed of hot storage for all data with the cost efficiency of cold-tier storage. It eliminates the time-consuming and costly re-indexing and rehydration operations that are inherent to competitive observability solutions.
  • Seamless Integration with Google Cloud: Organizations can unify and analyze data within their existing cloud ecosystems, enhancing performance and security.
  • Easy Access to Dynatrace Data for Developers: With Google’s Gemini Code Assist, an AI-powered coding assistant that helps developers with various tasks – including code generation, completion, and debugging, directly within their IDEs – developers can access critical Dynatrace data, including data related to potential issues, without disrupting their flow state. This enables faster issue resolution and continuous innovation.
  • Observability for End-to-End Multimodal AI Models: Users can track and monitor the consumption, cost, and performance of AI services and models provided by Google’s Gemini models – a family of multimodal AI models designed to understand and generate text, images, audio, videos, and code – at scale.

“AI-powered observability has the power to transform how organizations manage their cloud-native environments,” said Ritika Suri, Managing Director of AI & Data ISV Partnerships at Google Cloud. “Solutions like Dynatrace Grail, integrated with Google Cloud's leading AI and infrastructure, enable customers to streamline operations, enhance efficiency, and confidently drive innovation.”

“Grail has been a game changer for Dynatrace, setting us apart by delivering real-time insights at an unprecedented scale,” said Jay Snyder, SVP of Partners and Alliances, Dynatrace. “By combining the capabilities of Grail with the flexibility and power of Google Cloud, Dynatrace empowers customers to innovate faster, operate more efficiently, and achieve their digital transformation goals with confidence.”

The early access program presents an opportunity for Google Cloud customers to adopt next-generation observability technology, positioning them at the forefront of cloud-native transformation. Through this integration, enterprises can modernize operations, improve system reliability, and unlock new opportunities for growth and efficiency.

Dynatrace expects the Grail data lakehouse on Google Cloud to be generally available by June 30. 

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.