RightScale announced it is the first partner to resell Google Compute Engine.
RightScale integration of its cloud management platform with Google Compute Engine provides customers with comprehensive management and automation for the Google Infrastructure-as-a-Service cloud.
As a Google strategic partner and reseller, RightScale will also offer tailored onboarding packages through its professional services organization and world-class support for customers.
RightScale is now the first Google reseller to provide a streamlined and fully supported path for users wanting to trial and purchase Google Compute Engine. When developers and organizations choose to deploy their workloads on Google Compute Engine, the RightScale cloud management platform creates efficient, automated provisioning and operations.
Using RightScale, customers can automate and customize the build-up, operation, and break-down of many types of workloads, from on-demand data analysis clusters, to batch processing, to 3-tier web apps built to specific customer requirements. With this announcement, customers will be able to purchase Google Compute Engine services directly from RightScale and leverage on-boarding and support services from RightScale’s professional services team.
RightScale and Google will also offer custom solutions created for industry verticals, including Advertising, Media, Entertainment and Gaming. The industry specific solutions will provide speed to market, scalability and consistent performance — critical for the global applications deployed by those verticals.
"RightScale is the leader in cloud management with deep expertise helping customers take full advantage of the cloud. They allow organizations to start quickly and provides them a path to accelerate their cloud projects by streamlining ongoing operations," said Dan Powers, Director Cloud Platform Sales & GTM, Google. "RightScale is an important partner in helping customers leverage the agility and power of Google Compute Engine."
"Google is poised to be a dominant player in Infrastructure-as-a-Service, particularly where Internet-scale, reliability and dynamic resources are required," said Thorsten von Eicken, CTO of RightScale. "Our own internal tests and early response from our beta customers demonstrate that Google Compute Engine has a remarkably consistent performance level. The combination of our cloud management technology, support and professional services with Google Compute Engine creates the positive experience and quality of service that meets the high standards for which RightScale is known."
The Latest
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...