Build with AI,
Deploy with Qovery.
AI coding tools turned every engineer into a 10× developer - and your CI/CD into the bottleneck. Claude, Cursor, or any coding agent builds it; Qovery ships it, governed by construction. Two deployment paths coexist: prompt-to-deploy from any AI agent AND Git-push-to-deploy with Terraform and PR review. Both policy-gated, both audited.
$ curl -fsSL https://skill.qovery.com/install.sh | bash> Deploy my project with Qovery
Before: 1 week coding,
1 day deploying.
Now it's the opposite.
Claude Code, Cursor, and Codex turned your engineers into 10× developers. But your CI/CD pipeline - builds, tests, environment provisioning, deployments - was designed for human-speed development. OpenAI runs 1 million builds per day. Your pipeline was built for 1,000. The bottleneck has flipped.
Deploys per engineer have exploded
Instead of deploying once a day, your engineers deploy 10–20 times. Preview environments, experiments, prototypes - all hitting a pipeline that was sized for waterfall. Queue times grow. Frustration mounts.
AI agents bypass your pipeline entirely
Coding agents deploy via API calls, not pull requests. Your carefully built Git workflow becomes optional. Audit trails break. Compliance gaps open. Platform teams lose visibility.
Two worlds that don't talk to each other
Git-push-to-deploy is too slow for experimentation. Prompt-to-deploy is too fast for production. You need both to coexist - same guardrails, same audit trail, same platform.
Two deployment paths.
One platform.
Ready to see this in action?
AI velocity.
GitOps safety.
Six capabilities that make prompt-to-deploy and Git-push-to-deploy coexist on the same platform.
Deploy by conversation
Tell Claude or Cursor: "Deploy this to staging." Qovery's MCP Server handles the rest - build, provision, deploy, configure. No pipeline config needed.
Terraform-native GitOps
Define environments, services, and databases in HCL. Git-versioned, PR-reviewed, reproducible. The Qovery Terraform Provider covers the full resource surface.
AI agents open PRs, not deploy directly
Configure agents to propose Terraform changes via pull request. Humans review and merge. The agent is fast; the process is safe.
RBAC-enforced guardrails
Platform Engineering defines who can deploy what, where. AI agents operate within those boundaries. Prompt-deploy and Git-deploy follow the same rules.
Policy-gated promotion
Define rules: preview = auto-deploy, staging = team lead approval, production = Git merge only. Agents and humans follow the same gates.
Full audit trail
Every deployment - whether triggered by a prompt, a Git push, or a Terraform apply - is logged: who initiated it, what changed, when it happened.
From bottleneck
to velocity.
How teams remove the CI/CD bottleneck while keeping production safe.
Install the Skill
Install Qovery MCP Server. Claude and Cursor can now talk to your Qovery org. Scoped to preview by default.
Import infra as Terraform
Export your environments as HCL. Commit to Git. Production deployments are now Git-triggered and PR-reviewed.
Two paths live
Engineers deploy experiments from prompts. Production goes through Git. Both paths audited on the same platform.
10× throughput realized
Pipeline bottleneck eliminated. Deploys per engineer up 10×. Zero security incidents. Platform team has full visibility.
Your entire stack, in one Terraform file.
This is a real production Terraform file managing a Go backend, Next.js frontend, PostgreSQL database, Helm charts, and AWS infrastructure - all through Qovery's Terraform Provider. Every resource is declarative, version-controlled, and reviewable in a pull request.
terraform {
required_providers {
qovery = {
source = "qovery/qovery"
}
}
}
resource "qovery_environment" "doktolib" {
project_id = var.qovery_project_id
cluster_id = var.qovery_cluster_id
name = var.environment_name
mode = var.environment_mode
}
resource "qovery_application" "backend" {
environment_id = qovery_environment.doktolib.id
name = "backend"
build_mode = "DOCKER"
ports = [{
internal_port = 8080
external_port = 443
protocol = "HTTP"
publicly_accessible = true
}]
}
resource "qovery_application" "frontend" {
environment_id = qovery_environment.doktolib.id
name = "frontend"
build_mode = "DOCKER"
ports = [{
internal_port = 3000
external_port = 443
protocol = "HTTP"
publicly_accessible = true
}]
}
resource "qovery_database" "postgres" {
environment_id = qovery_environment.doktolib.id
name = "postgres"
type = "POSTGRESQL"
version = "17.6"
mode = "CONTAINER"
}
resource "qovery_helm" "windmill" {
environment_id = qovery_environment.doktolib.id
name = "background-processing"
source = {
helm_repository = {
chart_name = "windmill"
chart_version = "4.0.10"
}
}
}“We replaced ArgoCD, FluxCD, and three custom Helm charts with Qovery's Terraform Provider. Our GitOps workflow is simpler, faster, and now AI agents can participate through pull requests instead of bypassing our review process. Audit and compliance are actually easier than before.”
What is AI DevOps?
How does AI agent orchestration work in a CI/CD pipeline?
Why do AI coding tools break traditional CI/CD pipelines?
Can AI agents deploy to production safely?
Unblock your pipeline.
Ship at AI speed.
See how Qovery gives your 10× engineers a 10× pipeline. Prompt-to-deploy and Git-push-to-deploy on the same platform.