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Kubernetes can run AI inference. But can it count the real cost?
Welcome to another edition of Road to KubeCon, where we’re tracking the Kubernetes and cloud-native ecosystem on the way into KubeCon + Cloud Native Con NA 2026, to be held in Salt Lake City, Utah, November 9-12.
This week, we look back at the past week of significant movements in the Kubernetes space. Most notably, we see interesting advances in cloud-native architectures for AI inference. We take a look at that, plus a new Gartner quadrant, new Kubernetes hardening updates, and important CNCF project updates.
HPE challenges server virtualization platforms
On Monday, Gartner published its Magic Quadrant for Server Virtualization Platforms, a guide comparing solution providers in the server virtualization market. The quadrant names HPE as a Challenger based on Ability to Execute and Completeness of Vision.
Hewlett Packard Enterprise (HPE) is a presenting sponsor of Road to KubeCon. HPE Software helps IT organizations modernize infrastructure, streamline operations, and accelerate AI initiatives across hybrid, multi-vendor environments.
According to the HPE newsroom, the recognition reflects ongoing momentum behind HPE Morpheus Software, its virtualization and cloud operations portfolio. HPE was positioned in the Challengers quadrant alongside Canonical and Oracle.
The announcement comes as enterprises rethink their virtualization strategies. Rather than simply swapping in another hypervisor, HPE argues that organizations increasingly need unified governance and ways to provision, orchestrate, observe and secure workloads — including VMs, containers and AI workloads — across clouds.
Kubernetes hardens container storage
On Wednesday, Red Hat’s Nispriha Jagan and Neeraj Krishna wrote on the Kubernetes project blog about two new storage security features shipped as Alpha in Kubernetes v1.37, which included 67 enhancements.
The notable security features are new bind mount options and emptyDir permissions. The enhancements come as multiple security findings have surfaced regarding emptyDir volumes, one of the most common writable volume types. The additions are made possible by low-level Linux security mechanisms.
According to the authors, these enhancements give users native controls to harden Kubernetes workload storage better. “Supporting noexec, nodev, and nosuid gives users a native way to harden volume mounts to match security benchmarks and policy,” the authors write.
KubeCon adds AI Inference + Agentic track
Last month, CNCF announced it will feature an AI Inference + Agentic track at KubeCon + CloudNativeCon North America 2026, exploring the intersection of generative AI and cloud native infrastructure. Attendees can explore the sessions here.
The added track underscores the growing use of Kubernetes for production AI workloads, particularly as the focus shifts from training models to serving them in production. It also reflects emerging practices for building agentic systems around protocols like MCP and A2A, as well as infrastructure such as AI gateways.
China Merchants Bank unifies AI inference on Kubernetes
China Merchants Bank, a leading Chinese commercial bank, recently showcased its cloud-native AI infrastructure at a CNCF event in China. Its infrastructure team won the CNCF End User Case Study Contest with an architecture combining Kubernetes with several cloud native projects:
- Kueue, for job queueing and quotas,
- KEDA, for event-based auto-scaling,
- Prometheus, for systems monitoring and metrics,
- HAMi, for sharing accelerator capacity across Kubernetes workloads,
- and Fluid, for accelerating access to datasets.
The bank has a large pool of nearly 10,000 accelerator cards used for AI computation. These are heterogeneous, meaning they are not all the same type or configuration.
According to the CNCF announcement, the architecture unified management of 99% of its AI compute resources, while increasing average utilization from 35% to more than 60%. It also cut the cost of processing 1 million tokens by 60% under comparable conditions.
The case study shows how cloud-native infrastructure can improve utilization and efficiency for AI training and inference, even in regulated areas like financial services.
Industry take: Can AI inference on Kubernetes handle token cost issues?
Interest in AI inference on cloud native infrastructure is palpable. However, this week Val Bercovici, chief AI officer at WEKA, an AI-native data platform, questions whether Kubernetes’ existing resource model fits the changing economics of large-scale AI inference.
Bercovici tells The New Stack: “With AI inference, it’s cost per token, and that cost depends on state Kubernetes was never designed to manage: request mix, KV cache occupancy, the balance of prefill and decode, and how memory and bandwidth are consumed inside the accelerator after a pod is already running.”
“My view is that Kubernetes doesn’t go away,” Bercovici says. “But unless its resource model evolves, it becomes a tax on inference economics.”
He foresees a new scheduling and memory layer to emerge around Kubernetes that can compute what a token actually costs to serve. Then platforms could make more informed, cost-based decisions about how inference workloads are scheduled and served.
As Kubernetes evolves, so do the demands on the teams running it. Presenting sponsor HPE helps teams address that complexity with software spanning virtualization, cloud management, observability, and automation.
Move over, platform engineering. Hey, agentic engineering.
A new Weave Intelligence report, State of AI in Platform Engineering Volume 2, authored by Sam Barlien, Luca Galante, and Florian Lipp, surveyed 242 platform engineering leaders on the before-and-after effects of introducing agentic AI into platform engineering.
38% of teams are shipping at least twice as much as before AI. When assessing ROI across the software delivery life cycle, 20% report efficiency gains and 11% report operational savings. Yet only 8% report a transformative, structural shift. Meanwhile, 29% are still prototyping without realized gains, with some outliers reporting negative results.
The biggest roadblock to scaling AI usage? A lack of platform readiness, including APIs, deterministic pathways, and standardization. Weave’s takeaway is that platform engineering must increasingly account for AI readiness and agentic experience as agents become another key platform consumer.
OpenTelemetry Kubernetes attributes processor reaches v1.0.0
On Wednesday, OpenTelemetry, the graduated CNCF project and open standard for telemetry, announced the v1.0.0 release and distribution of its Kubernetes attributes processor. It’s a helpful feature that uses the Kubernetes API to add Kubernetes metadata, such as stability, distributions, warnings, issues, and other metrics, to resource attributes.
According to the release notes, written by Elastic’s Christos Markou and Datadog’s Pablo Baeyens, the feature has been in progress in the OpenTelemetry Collector SIG since late 2025, based on a roadmap of users’ most-requested features. Existing attribute processors should review the breaking changes and migration guide.
DigitalOcean opens Spot GPU node pools
Technically, this occurred the week before last, but didn’t make the digest. As of September 9, DigitalOcean Kubernetes’ (DOKS) Spot GPU Node Pools entered public preview. According to the release notes, the feature runs worker nodes on interruptible GPU capacity at a “lower, variable rate than on-demand GPU nodes.” This could offer a cost-effective option for fault-tolerant workloads.
Other updates from the K8s universe
More updates from the infrastructure-heads, platform engineers, and multi-cloud operators working in the Kubernetes ecosystem:
- KubeCon registration applications are open until October 4, 11:59 p.m. MDT. Travel-scholarship applications have closed.
- Cilium 1.20.2 release includes several bug fixes and CI changes.
- The latest Talos Linux 1.14 update benefits multi-tenant services. “Welcome departure from LUKS-only,” comments software engineer Charles Deane on LinkedIn.
- The Apache Flink community announces Flink Kubernetes Operator 1.16.0, centered on “extensibility and documentation.”
- Arm introduces Neoverse CSS N4 alongside its AGI CPU to improve throughput efficiency and support the variable compute demands of agentic AI.
Follow the Road to KubeCon
Road to KubeCon is an eight-part series presented by HPE, which will be at KubeCon + CloudNativeCon North America in Salt Lake City. Before you go, explore how HPE Software helps IT teams do more with less complexity.
We’ll be here every Friday until KubeCon.
If you’d like to participate, Bill Doerrfeld, the writer of this series, is open to pitches — you can send release notes, quotes, reports, videos, case studies, or hot takes through his contact page.
If you didn’t catch the inaugural edition covering the Kubernetes v1.37 release, check it out here. You can also visit the Road to KubeCon page for the complete archive.
The post Kubernetes can run AI inference. But can it count the real cost? appeared first on The New Stack.
