Dynatrace announced an expanded strategic collaboration with Microsoft.
As part of this, Dynatrace now delivers its observability platform for purchase through the Microsoft Azure Marketplace. This means Microsoft customers can now easily implement Dynatrace’s automatic and intelligent observability for their Microsoft Azure and hybrid-cloud environments through a streamlined process covering everything from procurement to automated deployment and configuration.
The collaboration also includes joint go-to-market activities, including:
- Microsoft Azure customers can now use their committed Azure spend to purchase Dynatrace.
- Microsoft and Dynatrace are engaging in joint marketing, including events, sponsorships, and customer solutions workshops.
- Microsoft and Dynatrace will provide their sales representatives with co-selling incentives to encourage a simple, unified go-to-market motion.
“We are pleased that Dynatrace has made their observability platform available in the Azure Marketplace, so customers can easily implement automatic and intelligent observability for their Azure and hybrid-cloud environments,” said Casey McGee, VP of Global ISV Sales at Microsoft. “The Dynatrace platform enables customers’ business transformation by providing real-time information about application performance and security, underlying infrastructure, and user experiences, so they can focus on driving innovation and delivering new value.”
“We designed the Dynatrace platform to enable the largest organizations in the world to accelerate their digital transformations,” said Mike Maciag, CMO at Dynatrace. “Together with Microsoft, we’re making it even easier for our joint customers to build, deploy, and grow applications in Azure and hybrid environments at scale. In addition to the metrics, logs, and traces of observability, our unique approach unifies AIOps and continuous automation to help organizations innovate faster and achieve better business results with greater efficiency and confidence.”
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
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 ...