Dynatrace announced that Cloud Infrastructure Monitoring, part of its all-in-one Software Intelligence Platform, Dynatrace, is now individually licensable.
Cloud Infrastructure Monitoring provides an infrastructure-only, automated approach to cloud infrastructure and container monitoring at web scale.
With automated discovery and AI at the core of Cloud Infrastructure Monitoring, enterprises benefit from easier deployment and accurate and actionable insights versus alternative, multi-tool, manual approaches. Precise, root-cause problem detection reduces alert noise, improves visibility and enhances automation across cloud environments.
“The enterprise cloud demands an all-in-one approach to monitor cloud platforms and supporting infrastructure instead of the siloed views provided by traditional monitoring tools. These tools require manual integration and reporting and only band-aid customers’ problems, drowning them in alert noise,” said Steve Tack, SVP of Product, Dynatrace. “We are pleased to offer Dynatrace Cloud Infrastructure Monitoring for these customers which includes the complete AI power of Dynatrace to provide a unified view of their enterprise cloud, consolidate point tools for increased efficiency at reduced cost, and provide actionable answers to IT Operations and DevOps teams.”
Dynatrace natively and automatically monitors containers and the microservices running inside of them, without the need to manually instrument the container itself. Its analysis includes full visibility into server metrics, including CPU, memory, network performance, and processes running on these hosts, including virtualized components. With AI at its core and built-in Log Analytics, Dynatrace® captures all relevant log files and puts them in context of a transaction or a problem analysis to allow for richer detail and faster decision making.
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 ...