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Zombie Workloads Haunt Data Center Efficiency Efforts Abandoned jobs, instances, and volumes can run indefinitely. FinOps tools help, but GPU-era AI demands new zombie-hunting methods. September 17, 2026 Companies generally don’t post jobs for “Zombie Workload Hunter.” But the need is there. With each new technology generation, remnants of abandoned libraries, programs, services, and storage volumes continue to consume resources. Good sense eventually says it’s time to find and pare them down. What you don’t turn off will cost you. But finding them is a hunt. These days, Cloud FinOps engineers, cost optimization engineers, and inventory managers do the seek-and-destroy work. This new engineering cohort uses observability, CloudOps, and FinOps automation to track resources that go by many names – orphans, strays, and, yes, zombies. They plan for new capacity and automate the decommissioning of idle assets – ideally before those assets show up on cloud bills. This search for efficiency has never mattered more. A new AI wave has put the data center industry in the national spotlight, and GPU-based generative and agentic AI has focused public scrutiny on resource use. Enterprise teams, meanwhile, are putting new emphasis on the cost of computing. Why Zombie Workloads Persist in Hybrid and Multicloud Zombie workloads can quietly bleed power, rack space, and budget across multicloud and on-premises environments. They often follow a common pattern, Roger Strukhoff, chief research officer at IDCA, told Data Center Knowledge. Cloud zombie workloads frequently trace back to “individuals or departments that stop using apps but never delete them,” he said. “They appear when internal organizations are consolidated, or companies are acquired, and no one is tasked with cleaning up unused cloud instances and applications.” According to Strukhoff, IDCA research indicates that as much as 13% of US cloud usage comes from such zombie workloads. FinOps tool providers like Broadcom (VMware Aria Cost, formerly CloudHealth) and AWS (e.g., Cost Explorer, Compute Optimizer) – often enlisted to assist with zombie cleanup – estimate the overall cloud waste even higher, commonly at 25% to 30% or more, with orphaned volumes and forgotten compute instances frequently in the mix. The problem is stubborn, and ongoing shifts in application architecture don’t make it easier. From Scale-to-Zero and Beyond Today’s most common solutions to cloud clutter trace back to the famed Unix kill process. In serverless architectures, scale-to-zero configurations enforce runtime limits so that idle services stop consuming resources and incurring costs. But there are tradeoffs. Mistakenly targeting a live service can lead to cold starts and unwanted latency in spinning up instances. For certain spiky traffic patterns, that can be a problem. FinOps and Observability A growing array of cloud-native and platform enhancements aims to take up the slack. Vendors such as Google, Flexera, Datadog, and IBM offer tools that correlate billing and performance metrics to highlight charged resources with zero use; continuously index assets to detect inactivity and broken parent-child services; and apply policies with auto-remediation to tear down processes that lurk unnoticed in data center environments. Microservices Made Zombies Harder to Kill The endlessly running program, of course, predates the cloud. It dates to mainframes and persisted through client/server and virtual machines. A tried-and-true operating model was to “pull the plug and wait for somebody to scream,” said Eric Newcomer, analyst at Intellyx and a veteran of middleware and distributed computing. That changed when cloud-native microservices gained traction. “Cloud computing brought with it ‘headless’ services that run in the background to serve up some data to another service or the like. A lot of such microservices-based applications can be composed of even hundreds of services,” Newcomer said. “If the application stops, the zombie services might still keep going – and do nothing.” Cloud computing pioneered the use of commodity hardware, massive scale, aggressive replication, and container orchestration tools like Kubernetes. But these innovations don’t easily map to today’s LLMs and agentic AI. The GPU-based hardware behind modern AI is far different from the commodity setups built for cloud-style distributed computing – and AI zombies cost more, in both money and electricity. Newcomer noted that, under the auspices of the Linux Foundation and the Cloud Native Computing Foundation, Kubernetes is evolving to better support AI workloads, where new kinds of zombies emerge. Kubernetes innovations are spreading across new AI-focused cloud providers, but much of this work is still in its early stages. It’s a New Age: GPU-Era AI Raises the Stakes Supporting AI on Kubernetes is complicated by the moving-target nature of generative and agentic AI. There’s still a lot of R&D. Teams routinely encounter long-running jobs, mid-flight pipeline crashes, and orchestration scripts that fail to clean up jobs. Add to that the complexity of managing model weights, data ingestion, and more. That creates a very different workload profile, according to Graziano Castro, a developer relations engineer at AI optimization platform maker Akamas and a Cloud Native Computing Foundation Ambassador, in an email exchange with Data Center Knowledge. “GPUs are more expensive than CPU cores, so inefficiencies that were just rounding errors on a cloud bill become very real numbers. A GPU sitting idle isn’t a minor waste,” Castro said. “What changed with the LLM era is that the cost of ignoring inefficiency went up by an order of magnitude almost overnight.” “What changed with the LLM era is that the cost of ignoring inefficiency went up by an order of magnitude almost overnight.” – Graziano Castro, developer relations engineer at Akamas and Cloud Native Computing Foundation Ambassador While expected in R&D, that inefficiency becomes a major problem in production, introducing a costly new strain of zombies: idle or abandoned GPUs. “Kubernetes is growing into this space, and it's happening fast, and the ecosystem is visibly building new primitives to catch up,” Castro said. “But it's still exposing every place where the platform’s original assumptions were built for a cheaper, more elastic kind of workload than the one it’s increasingly being asked to run.” Kubernetes is evolving for AI with dynamic resource allocation for specialized hardware and smarter batch job scheduling that accounts for the topology and locality requirements many AI jobs have. Not surprisingly, efficiency monitoring and zombie workload hunting now rely on vigilant GPU health and utilization checks. Chip-level observability is becoming table stakes for smooth operations. Castro pointed to NVIDIA’s Data Center GPU Manager (DCGM) as a monitoring mainstay, useful for gauging how busy the GPU appears to be. “The catch is that ‘appears to be’ part," he said. “A GPU can look busy on a DCGM’s utilization number while it is actually just sitting there waiting on [some other action], like data that hasn’t arrived yet, or another GPU running behind on the same job.” Network activity can be a blind spot in these setups. That’s why ongoing OpenTelemetry standards work matters, Castro added: different tools must interoperate to correlate performance signals. Those efforts are creating a common vocabulary for this AI job monitoring, he said. Back to Basics: Policies and Ownership The hunt for new zombie variants shouldn’t overshadow the basics, Strukhoff emphasized. Organizations need clear policies to eliminate zombie workloads. “Individual users must be reminded to close their instances when finished with them. Or better, organizations should institute monitors that remind administrators of zombie workloads and set policies to close those that are not being used,” he said. In practice, that means routine environment-wide scans of all usage and a cleanup methodology – especially after internal consolidations or acquisitions. The tools are improving, and the workloads are changing, but the rule remains: what you don’t turn off will cost you.
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