SUSE announced new observability capabilities as part of the general availability of SUSE Rancher Prime 3.1.
The introduction of comprehensive observability capabilities in SUSE Rancher Prime is not just about adding another tool to infrastructure management suites – it’s about delivering tangible business outcomes. Now with observability, Rancher Prime delivers full-stack visibility, empowering organizations to minimize downtime by proactively finding and resolving issues before they impact operations and revenue.
Rancher Prime now provides real-time, context rich insight and increases efficiency by streamlining troubleshooting processes, enabling IT teams to focus on innovation rather than managing crisis. Rancher Prime ensures smoother more reliable user experiences by maintaining peak performance from edge to cloud. Leveraging Rancher Prime Observability companies can optimize their infrastructure, reduce operational risks, and enhance business agility.
The new functionality results from SUSE’s acquisition of observability vendor StackState in June and the ongoing needs for organizations as they continue to adopt modern—albeit increasingly complex—cloud-native application environments. When hundreds of microservices interact with each other across distributed infrastructure from the edge, datacenter and cloud, users need more than just traditional performance metrics. Companies benefit from increased efficiencies, and more reliable experiences, when they integrate efforts such as observability into their application engineering processes.
“As the threats of service interruptions continue, the risk of widespread outages is higher than ever and our customers need a unified view of their entire mission-critical infrastructure and applications,” said Peter Smails, SVP, General Manager, Enterprise Container Management at SUSE. “Rancher Prime Observability will enable customers to ensure reliability, accelerate troubleshooting, and maximize performance while keeping their systems running smoothly.”
With the addition of observability, Rancher Prime includes an extensive set of capabilities designed to provide comprehensive visibility into everything managed by Rancher Prime. Available via both SaaS-hosted or on-premises deployment options, the solution is ideal to enhance operational insights and efficiency for companies using Rancher Prime at scale. By integrating observability into Kubernetes environments, users can proactively monitor, detect, and remediate issues, ensuring high availability and performance across their infrastructure.
Benefits of Rancher Prime Observability include:
- Application performance monitoring: Out of the box eBPF-based visibility beyond the cluster, including components like databases, endpoints, applications, and queues.
- Automated data correlation: Rancher Prime now includes a powerful correlation engine that automatically connects metrics, events, logs, traces, and change information from various sources, creating a unified timeline that simplifies the identification of root causes and trends over time.
- Advanced dependency mapping: Automatically discover and visualize the relationships between services, applications, and infrastructure components, providing a holistic view of the IT environment. This feature helps pinpoint the exact timing and impact of changes, making troubleshooting faster and more accurate.
- Out-of-the-box connected dashboards: Centralize all observability data into user-friendly, out-of-the-box dashboards that offer real-time and historical insights. This eliminates the need for multiple tools and reduces context switching, significantly enhancing the efficiency of operations teams.
- Out-of-the box issue detection and guided remediation: Benefit from step-by-step remediation guides that leverage expert knowledge to provide proven problem-solving strategies. These guides can be customized to fit specific environments.
Rancher Prime Observability is generally available now and at no additional cost to Rancher Prime customers.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.