Operations | Monitoring | ITSM | DevOps | Cloud

Industry analyst recognition means something different from an award. GigaOm does not hand out trophies. They evaluate products against a defined capability framework and tell the market where vendors actually stand. By that measure, Kubex has been named a Leader in two of GigaOm’s 2026 Radar Reports: Kubernetes Resource Management and Cloud Resource Optimization. In the Kubernetes report, we are positioned as an Outperformer. In Cloud Resource Optimization, a Fast Mover.

The early era for AI was defined by experimentation, standing up isolated environments, and finding the first practical use cases. Today, the conversation is different. Enterprises are no longer asking whether AI matters. They are asking how to scale it sustainably, securely, and economically. That shift is giving rise to the AI factory: a repeatable, governed, production-ready environment where data scientists, platform teams, and application teams can build, train, deploy, and operate AI at scale.

TL;DR: Most Kubernetes clusters waste GPU compute through over-provisioned pod requests and suboptimal node selection. This guide covers 10 tools that fix this across four layers: resource lifecycle (Kubex, ScaleOps, Cast.ai), hardware partitioning (GPU Operator, MIG, time-slicing), inference serving (Triton, KServe), and observability (DCGM Exporter, NFD). For most teams, the biggest gains are at the resource lifecycle layer: no model changes required.

In the modern cloud infrastructure landscape, we don’t have a data problem; we have an actionable interpretation gap. Engineering teams are often drowning in metrics that describe a crisis without providing a clear path to remediation. Traditional FinOps, SRE, and DevOps work has become a reactive loop of dashboard-watching and manual firefighting.

By 2026, the GPU shortage isn’t a supply-chain hiccup anymore. It’s baked into the system. Even after pouring billions into CapEx, most enterprises still want 40% more GPU capacity than they actually have. And it’s not because they’re chasing moonshots. Technology companies are training foundation models while serving inference for millions of users on the same clusters. AI labs are juggling fine-tuning, evaluation, and real-time experimentation side by side.

Over the past few years, Kubex has evolved from a cloud optimization product into a Kubernetes-centric solution, shifting its focus from cost and waste visibility to fully automated resource optimization. As that evolution happened, one of the earliest design decisions we had made began to show its limits: how the product was configured.

Kubex releases data from a survey of over 500 U.S. software developers, revealing a disconnect between cost sensitivity, scrutiny and resource efficiency.

Enterprises operating at cloud scale today face a growing reality: managing infrastructure performance and cost in silos no longer works. Kubernetes, multi cloud environments, and GPU accelerated workloads deliver immense agility and capability, but they also introduce complexity that outpaces traditional monitoring and cost governance approaches.