We Build The Future Of The Cloud ⚡️
This video has been recorded during our retreat in the south of France 🇫🇷 in Feb 2022.
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The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards
Effective Kubernetes management in 2026 demands a shift from manual cluster building to intent-based fleet orchestration. By implementing agentic automation on standard EKS, GKE, or AKS clusters, enterprises eliminate operational weight, prevent configuration drift, and proactively control cloud spend without vendor lock-in, enabling effective scaling across massive fleets.
A Kubernetes single pane of glass is a centralized management layer that unifies visibility, access control, cost allocation, and policy enforcement across § cluster in an enterprise fleet for all cloud providers. It replaces the fragmented practice of switching between AWS, GCP, and Azure consoles to govern infrastructure, giving platform teams a single source of truth for multi-cloud Kubernetes operations.
Deploying a Docker container on Kubernetes requires building an image, authenticating with a registry, writing YAML deployment manifests, configuring services, and executing kubectl commands. While necessary to understand, executing this manual workflow across thousands of clusters causes severe configuration drift. Enterprise platform teams use agentic platforms to automate the entire deployment lifecycle.
At enterprise scale, managing provider-specific Kubernetes YAML across multiple clouds creates crippling configuration drift and operational toil. By adopting an agentic Kubernetes management platform, infrastructure teams abstract cloud-specific configurations (like ingress controllers and storage classes) into a single, declarative intent that automatically reconciles across 1,000+ clusters.
To stop GPU costs from destroying SaaS margins, teams must transition from static to consumption-based infrastructure by utilizing Karpenter for dynamic provisioning, maximizing hardware density with NVIDIA MIG, and leveraging Qovery to tie scaling directly to business metrics.
AI is helping developers write more code, faster than ever. But writing code is only half the story. What happens after? Building, deploying, debugging, scaling. That's where teams still lose hours.We're building Qovery for this era. Not just to deploy your code, but to make everything that comes after writing it just as fast.
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