How Qovery uses Qovery to speed up its AI project

At Qovery we use our own products every day. We believe it is the fastest way to build empathy for our users and push innovation further. Whether it’s a frontend engineer using Preview Environments to test a pull request in isolation or our backend team refining our API, Qovery is the heartbeat of our development lifecycle.

But today is another story, we’re taking you behind the scenes to show how we use Qovery to speed up our new AI initiative.

The Challenge: Speed vs. Complexity

To build this, we needed the right talent. We found an incredible AI expert to lead the initiative. He is a master of Large Language Models (LLMs) and data science, but like many specialized developers, his expertise isn't in infrastructure, Kubernetes clusters, DNS configuration, or deployment management.

In a typical setup, this engineer would have spent weeks waiting for a Senior Reliability Engineer (SRE) to:

• Set up CI/CD pipelines.
• Configure Kubernetes and Helm charts.
• Manage DNS and SSL certificates.
• Provision databases and monitoring.

Weeks of back-and-forth, copy/pasting of YAML configuration to put in place even before his code can reach its target, clients.

How He Plugged the AI Stack into Qovery

For our AI lead, the challenge wasn't just writing the code; it was ensuring that code could talk to the right data and scale on demand. Here is exactly how he "plugged" the AI stack into Qovery:

1. Connecting the Brain (LLM & QDrant):

The core part of the application containing the agent and MCP server is built using our automatic docker image builder pipeline and request on-demand GPU nodes during deployment. To function, it needs to connect to databases; while Qovery provides ready-to-deploy SQL databases, we initially lacked a solution for vector databases like Qdrant to store model embeddings. To solve this, we leveraged Qovery’s Helm deployment system to deploy the official chart of QDrant, without the need for our AI lead to touch any CI/CD pipeline.

1. Automated scaling with KEDA

Since some operations of the agent can require a lot of memory, we enabled our beta autoscaling based on KEDA. This ensures we have the correct number of replicas to handle the load and set a readiness probe to stop serving traffic once a threshold is reached.

1. Managing the "State" with Terraform

As the product grows, so do its dependencies; our AI lead soon needed to store data into object storage. Instead of opening a ticket to our SRE to handle the creation of the S3 bucket, he used Qovery’s new Terraform integration to manage the lifecycle of S3 bucket directly within him environment, making him completely autonomous.

Empowering the Individual

With Qovery, our AI lead pushed his application to production in a single day. He didn't just "deploy code"; he deployed a production-grade system with all the "safeties" an SRE requires, handled automatically by the platform:

• Automatic Rollbacks: Instant recovery if a new model version fails liveness probes.
• Deterministic Builds: Using Docker to ensure the AI environment is identical from local to prod.
• Full Observability: Real-time monitoring to observe the live state of the agent's performance.

Conclusion: Infrastructure as Leverage

We are really proud of our new AI DevOps Copilot initiative, It proves what we’ve always believed: Infrastructure should be leverage and AI is a big advantage for the future

Qovery has become the platform we envisioned: a place where developers are empowered to move at the speed of their ideas, and DevOps teams know the guardrails are baked into the system.

Check out our AI Copilot or try Qovery yourself!