
Sysdig announced the global expansion of its support for IBM Cloud Monitoring with Sysdig to IBM Cloud services, infrastructure, and applications.
Administrators, DevOps teams, and developers can now access a fully managed enterprise-grade monitoring service that provides a single view across the IBM public cloud portfolio, including IBM public cloud services such as IBM Watson, Event Streams, Cloud Databases, Cloud Object Storage, and Cloud Foundry.
This expansion builds on the existing capabilities of IBM Cloud Monitoring with Sysdig to manage application and infrastructure complexities, help identify threats, and address problems throughout the software lifecycle. As organizations adopt cloud-native solutions, visibility and security are some of the biggest barriers for adoption. Cloud-native applications can be complex and they generate volumes of data that must be correlated and contextualized so that organizations can understand the health of their applications. Deep-data granularity from Sysdig allows cloud teams to monitor performance and health of their environment for better insight using a single tool that scales with enterprise demand. In the event of an issue, having system-wide visibility can facilitate quicker resolutions.
IBM Cloud Monitoring with Sysdig was released in June 2018. This expanded monitoring and troubleshooting enables customers worldwide to have access to a single-user interface that compiles metrics, such as response times and performance from IBM public cloud services, in addition to infrastructure and applications. IBM clients benefit from Sysdig service monitoring and Sysdig’s native Prometheus compatibility and ability to monitor the largest environments. Last month Sysdig announced full Prometheus-compatibility and cloud scaling. With Sysdig services, IBM has visibility into its metrics in a single, scalable repository across the entire IBM public cloud portfolio.
“As enterprises increasingly migrate workloads to the public cloud, they need a simple and efficient way to monitor performance and availability across their infrastructure, applications, and services,” said Jason McGee, vice president and chief technology officer for IBM Cloud Platform at IBM. “Sysdig’s expertise in cloud-native monitoring and modern cloud workloads has been instrumental in helping us deliver a solution that gives our global customers the unified view they need across the entire IBM public cloud portfolio. This is critical as our customers look to reduce complexities in managing the operations of their cloud-native workloads and focus more on accelerating their IT modernization.”
Working with Sysdig allows IBM public cloud users to:
- Maximize performance and availability: Sysdig performance and health monitoring gives IBM public cloud users deep visibility into infrastructure, applications, and services to anticipate and prevent issues.
- Speed time to resolution: Sysdig collects and correlates data across IBM resources, applications, and services that run on IBM public cloud and on-premises servers. Granular data with rich context provides a single source for insight and troubleshooting.
- Scale Prometheus monitoring: Sysdig scales to tens of millions of metrics with long-term data retention.
- Get started quickly: Out-of-the-box dashboards and automatic service discovery accelerate the adoption of containers and Kubernetes, including IBM Cloud Kubernetes Service.
“IBM Cloud Monitoring with Sysdig scales to provide enterprise customers with a single view across their IBM services, addressing data silos,” said Suresh Vasudevan, CEO at Sysdig. “We have built our monitoring framework around Prometheus and this deployment will be one of the largest Prometheus deployments to date.”
IBM Cloud Monitoring with Sysdig is available in all regions where IBM public cloud is accessible. Sysdig is part of the IBM Public Cloud Ecosystem, a new initiative to support global system integrators and independent software vendors to help clients modernize and transform mission-critical workloads on the IBM public cloud.
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.