Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has become a standard foundation for modern containerized applications, but operating clusters still requires significant engineering work. In the CNCF’s 2025 annual cloud native survey, published in January 2026, 82% of container users reported running Kubernetes in production. As adoption matures, the question for many teams is no longer whether to use Kubernetes, but how much of the operational burden they want to own.

Table of Contents GPU sharing with NVIDIA hardware is becoming easier to adopt in Kubernetes but it hasn’t been easier to observe. Time-slicing lets multiple workloads share the same GPU. MPS allows CUDA workloads to execute concurrently. Schedulers like KAI make it easier to manage these shared environments. But sharing a GPU introduces a problem that is easy to underestimate.

Compare the best Kubernetes cost management tools for 2026, from Kubecost and OpenCost to CloudZero, on allocation depth, scale, and pricing model.

Provision a fresh Docker Registry with Terraform, build your container image into it, and deploy to Kubernetes in one One pipeline. One click. It provisions a fresh Docker Registry with Terraform, builds your container image into it, and deploys that image to Kubernetes. Every run creates a uniquely named registry, so you never hit naming conflicts. Creating Docker registries by hand every time you spin up a new service or environment gets tedious fast.

Human approval is easy when you are sitting in front of the agent. For an agent running by itself in a cluster, almost none of that holds. You’re in a meeting and your agent is running in a cluster. It has a service account, it has been asked to keep a service healthy, and it has just worked out that the right fix is to roll back a database migration. Nobody is watching it. That was rather the point of deploying it. You want to get notified to approve such an important action.

Distributed AI training workloads impose complex scheduling requirements that Kubernetes’s built-in scheduler can’t meet. Kubernetes schedules pods individually and independently, but distributed training introduces two requirements that break this model: Pods must land on hardware with the right inter-GPU bandwidth, and all pods must be scheduled simultaneously. If either requirement goes unmet, training stalls or runs far below the hardware’s potential.

Agent Tasks let you run one-time or scheduled AI agents on your own infrastructure, with network guardrails, centralized MCP access and custom agent environment. Alessandro leads product at Qovery. He drives the changelog, roadmap, and product strategy - turning customer feedback into platform capabilities.