
Catchpoint® unveiled the Test Suite for Google Cloud, a tool designed to ease the end-to-end monitoring of Google Cloud services from different customer-chosen endpoints including customer premises, other public clouds and Internet endpoints. This suite is specifically developed to streamline the configuration and management of cloud service tests, thereby providing immediate benefits in operational performance and user experience. The Test Suite is part of Catchpoint’s Internet Performance Monitoring (IPM) platform, a new generation of solutions that provides deep visibility into every aspect of the Internet that impacts your business and revenue. It is like Application Performance Management (APM), but not for your app stack – for your Internet Stack. The Test Suite for Google Cloud empowers IT teams to rapidly deploy multiple tests for Google Cloud services, utilizing Google Cloud’s and Catchpoint’s best practices for prompt issue detection and troubleshooting. Its design focuses on minimizing complexity and time investment for effective Google Cloud service monitoring, making it particularly user-friendly for newcomers. “Our collaboration with Google Cloud is fundamentally about simplifying and enhancing the network performance monitoring experience for businesses leveraging Google Cloud services. The Test Suite for Google Cloud is a direct response to this need, facilitating more efficient implementation and streamlining the monitoring process for Google Cloud services,” said Mehdi Daoudi, CEO and co-founder of Catchpoint. “In an era where cloud computing is crucial for business efficiency, ensuring optimal performance, resilience, and user experience through sophisticated and precise monitoring is paramount. Our ongoing partnership with Google Cloud promises to yield further innovative solutions in this domain.” This initiative is a part of Catchpoint’s integration into the Google Cloud network observability partner ecosystem. “We believe that a robust Google Cloud network observability partner ecosystem not only provides choice but also enables you to leverage your current observability solutions, workflows, and skills to effectively monitor your Google Cloud workloads, tailored to your specific needs, including hybrid and multi-cloud deployments,” said Raj Gulani, Director of Product Management for Network Experiences at Google Cloud. The Test Suite includes pre-set test templates for key Google Cloud services such as BigQuery, Spanner, Cloud Storage, and Compute Engine. These templates are easily customizable, allowing users to quickly adapt tests to their specific requirements, which is particularly beneficial for businesses needing swift deployment and monitoring of their cloud services.
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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 ...