A practical guide to the best tools for both using AI agents to manage Kubernetes (AIOps) and running AI agent workloads on Kubernetes infrastructure in 2026.
Kubernetes was designed for stateless workloads. AI agents are highly stateful, requiring persistent memory, GPU scheduling (including fractional Multi-Instance GPU allocation), long-running processes, and unpredictable compute bursts.
Integrating AI agents with Kubernetes covers two distinct engineering use cases:
These tools treat your cluster as the input and use AI to reduce the operational burden on your team.
Qovery's built-in AI layer sits above the infrastructure and handles environment provisioning, deployment troubleshooting, and configuration suggestions through natural language. Unlike standalone AIOps tools, it acts on your infrastructure directly — not just alerts you. Particularly useful for platform engineers managing multiple environments across clusters. Qovery also exposes an AI agent skill so external agents (Claude, Cursor, etc.) can deploy and manage apps through a single prompt with guardrails to keep compliance and security on point.
The most widely adopted AI assistant for Kubernetes operations. Integrates directly into Slack and Teams, monitors cluster events in real time, and lets engineers trigger kubectl commands through chat. Best for teams who want to bring Kubernetes context into their existing communication workflow without a new dashboard.
Autonomous resource optimisation platform. Continuously adjusts pod and node allocation based on live workload behaviour without requiring manual intervention. Strong fit for cost-conscious teams running high-variance workloads.
Open-source CLI tool that runs diagnostics against your cluster and returns plain-English explanations of issues. No SaaS dependency, no data leaving your infra. Good entry point for teams exploring AI-assisted ops without committing to a platform.
These tools solve the infrastructure challenge of running agent workloads — which behave differently from standard web services.
Qovery abstracts Kubernetes complexity for teams deploying AI agent services. You define your app, Qovery handles the Kubernetes manifests, autoscaling, environment promotion (dev → staging → prod), and ephemeral environments for every PR. For AI workloads specifically, Qovery supports GPU node scheduling, long-running process configurations, and multi-service architectures common in agent pipelines (orchestrator + retrieval + inference). Try free →
Ray's Kubernetes operator for distributed AI and ML workloads. Best for teams running large-scale inference or training jobs that need fine-grained resource control. Requires meaningful Kubernetes expertise to operate.
The standard GitOps stack for teams who want version-controlled, declarative deployments of agent services. Mature, widely supported, but requires your team to own the full Kubernetes configuration layer. Qovery can sit on top of this stack and provide the developer-facing abstraction.
Qovery supports both use cases: it deploys and manages the infrastructure your AI agents run on, and exposes an agent-native API so your AI tools can interact with environments directly.
Melanie leads content at Qovery. She covers platform engineering trends, Kubernetes operations, FinOps, and the tools that help engineering teams ship faster.
Qovery ensures every agent action is scoped, audited, and policy-checked. Start deploying in under 10 minutes.