OpsCruise's Kubernetes and Cloud Service observability platform is certified to run on the Red Hat OpenShift Kubernetes platform.
This further supports the company’s observability platform in enabling organizations with enhanced performance while optimizing use of resources and cost.
Red Hat OpenShift is a cloud-native application platform that can help enterprise organizations build a more stable, security-focused Kubernetes environment with extended security and development workflow capabilities.
With OpsCruise running on Red Hat OpenShift, organizations can gain deeper visibility into every layer of their Red Hat OpenShift environments in order to reduce troubleshooting time and more confidently resolve performance issues. OpsCruise is an open cloud-native observability platform that enables Ops and App teams to troubleshoot all of their application components in context with configurations, connections, metrics, logs, traces and changes.
Beyond traditional telemetry, OpsCruise adds a unique eBPF-based flow feature that builds real-time topology, and a novel TracePath technology that makes distributed tracing usable by infrastructure and operations teams. By bringing everything into one place, app teams do not have to swivel across multiple tools from CI/CD, Kubectl tools and monitoring tools to understand and analyze the state of their applications.
In addition, to address temporal blindspots such as those from auto-scaling, OpsCruise provides a time travel feature that retains snapshots of the past so DevOps can look back in time to visualize changes that are often the source of problems.
“Red Hat OpenShift is supported by a robust partner ecosystem, extending the power of cloud-native application development and open source innovation across the hybrid cloud. With the OpsCruise observability platform certified to run on Red Hat OpenShift, customers can experience added security capabilities to further bolster their Kubernetes workflows and achieve real business results,” said Mark Longwell, director, Hybrid Platforms Business Unit, Red Hat.
OpsCruise on Red Hat OpenShift can be installed on-premises and in the cloud with Azure Red Hat OpenShift or Red Hat OpenShift Service on AWS. OpsCruise supports all of these options, so customers can now leverage OpsCruise observability anywhere they use Red Hat OpenShift. With instantaneous full-stack visibility and ability to detect and isolate problems with OpsCruise, DevOps teams can now more quickly identify sources of problems such as application SLO breaches caused by hard to find long chain dependencies on a failed service resulting from an incorrect Kubernetes configuration.
“Like Red Hat, OpsCruise has many large complex enterprise customers, so certifying our observability platform on Red Hat OpenShift was a no-brainer, " said Scott Fulton, Co-Founder & CEO of OpsCruise. “We look forward to continued collaboration with Red Hat to serve customers together.”
The Latest
Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...
As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...
For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...
I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...
Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...