The Big Picture
Qovery is the Kubernetes control plane for humans and AI agents — running entirely on your own cloud infrastructure. Whether a developer deploys via the console, an AI agent deploys via the Agent Skill, or a platform engineer manages clusters via Terraform, they all drive the same control plane. One consistent model, every interface.
How AI Agents Interact with Qovery
AI coding agents (Claude Code, Cursor, OpenCode, and 30+ others) interact with Qovery through two complementary interfaces:Agent Skill — Forward Engineering
Takes your source code → deploys it on QoveryThe AI agent analyzes your project, generates a Dockerfile, provisions databases, configures environment variables, and deploys — all autonomously. No Kubernetes knowledge required from the developer.
MCP Server — Operations
Manages existing infrastructure via natural languageOnce deployed, use the Qovery MCP Server to query environments, troubleshoot deployments, scale services, and manage infrastructure — all through conversation.
Why Kubernetes?
Kubernetes was designed for infrastructure operators — not developers, and certainly not AI agents. Qovery bridges this gap by adding:- Developer Experience - Deploy without Kubernetes expertise
- AI Agent Interface - Agent Skill and MCP Server for autonomous operations
- Production Readiness - Security, monitoring, and compliance built-in
- Multi-Cloud - Works on AWS, GCP, Azure, Scaleway, on-premise
- Universal Orchestration - Manages containers, databases, Terraform, and more
The Five Products
Qovery’s architecture consists of five integrated products:
Provision
Manage Kubernetes clusters across any cloud. Deploy infrastructure as code with Terraform.
Deploy
GitOps-based deployment for applications, databases, and services. Automatic builds and rollbacks.
Observe
Unified observability with correlated metrics, logs, and events across your stack.
Optimize
Intelligent cost tracking and optimization recommendations for Kubernetes and apps.
Secure
Built-in security and compliance for SOC2, GDPR, HIPAA, HDS, and DORA.
Your Infrastructure, Your Control
Unlike traditional PaaS (Heroku, Platform.sh), Qovery runs on your own cloud accounts:- Full Ownership - Your infrastructure, your rules
- No Vendor Lock-in - Standard Kubernetes underneath
- Cost Transparency - Direct cloud billing, no markup
- Data Sovereignty - Data stays in your chosen region
Three Layers of Abstraction
1. Multiple Interfaces
Work with Qovery through your preferred method — all driving the same control plane:- AI Agent Skill - Deploy from Claude Code, Cursor, or any AI coding tool
- MCP Server - Manage infrastructure via natural language
- Web Console - Visual interface for teams
- CLI - Command-line for developers
- Terraform - Infrastructure as Code
- API - Programmatic control
2. Production-Ready Features
Qovery adds what Kubernetes lacks:- Automatic HTTPS - Free SSL via Let’s Encrypt
- GitOps Workflow - Auto-deploy on Git push
- Preview Environments - Per-pull-request environments
- Database Management - Provision PostgreSQL, MySQL, MongoDB, Redis
- Secrets Management - Encrypted storage with audit logs
- Cost Tracking - Real-time spend visibility
3. Universal Orchestration
Manage any cloud resource from Kubernetes:
This means one control plane for your entire cloud infrastructure.
Two Workflows
With an AI Agent (Recommended for New Users)
1
Install the Qovery Skill
Run
curl -fsSL https://skill.qovery.com/install.sh | bash in your terminal2
Open your AI coding tool
Launch Claude Code, Cursor, OpenCode, or any compatible tool
3
Ask the agent to deploy
Say “Deploy my application with Qovery” — the agent handles everything from Dockerfile to running deployment
4
Manage with the MCP Server
Use the Qovery MCP Server to monitor, troubleshoot, and operate your infrastructure via natural language
With the Platform (Console / CLI / Terraform)
1
Connect Git
Link GitHub, GitLab, or Bitbucket
2
Configure
Set app settings, variables, and resources via the Console or Terraform
3
Deploy
Push code → Qovery builds and deploys automatically
4
Scale & Monitor
Track metrics, scale, optimize — all from one interface
Next Steps
Deploy with AI Agent
From code to deployed in ~10 minutes
All Use Cases
Browse every deployment path
Core Concepts
Learn essential terminology
Platform Products
Deep dive into all five products