Building an AI Platform on Kubernetes: GPUs, IaC and Multi-Team Environments

Why one-off setups stop scaling

The first model usually runs wherever it was easiest to launch: a single virtual machine, a managed notebook, a script on a schedule. By the fifth model, every team has its own setup, GPUs sit idle in one place while jobs queue in another, and nobody can rebuild an environment from scratch.

An AI platform replaces those one-off setups with shared, secure foundations. Kubernetes has become the common base for that platform because it runs the same way on Google Cloud, Azure, AWS and in your own data centre.

1. Start from the workloads, not the tools

A platform is only as good as its fit with the work it carries. Before choosing node types or writing a line of infrastructure code, map what the teams actually run.

• List training jobs, batch scoring, real-time inference and LLM serving, with their hardware, data and latency needs.
• Note security and residency constraints early: private networking, customer-managed keys, regions that data must stay in.
• Record current spend and utilisation, so the platform can be judged on real numbers after launch.

2. Separate node pools for training and inference

Training and inference have opposite shapes. Training wants large GPUs for hours, then nothing; inference wants smaller, steady capacity close to users. Mixing them on the same nodes wastes money and makes performance unpredictable.

• Give training its own GPU pool that scales down to zero when no jobs are queued.
• Size inference pools for steady traffic, with autoscaling for peaks and a floor that keeps latency stable.
• Keep general CPU workloads — pipelines, APIs, dashboards — on cheaper nodes of their own.

3. Describe everything as code

If an environment can only be recreated by the person who built it, it is a risk. Infrastructure as code turns the platform into something you can review, rebuild and audit.

• Define clusters, networks, node pools and permissions in Terraform, CloudFormation or Ansible, stored in version control.
• Review infrastructure changes like application code, with a plan that shows exactly what will change before anything is applied.
• Use the same code to create development, staging and production, so they differ only where you decide they should.

4. Give each team a safe space

A shared platform must not mean shared blast radius. Isolation lets teams move quickly without stepping on each other.

• Create a namespace per team or product, with its own quotas, access controls and secrets.
• Provide templates for common jobs — training, batch, serving — so a new model starts from a working, secure baseline.
• Report usage and cost by namespace, so every team sees what it consumes.

5. Keep GPU spend visible and bounded

GPUs are usually the largest line on an AI infrastructure bill, and the easiest to waste. Cost control belongs in the platform design, not in a monthly surprise.

• Scale idle pools down automatically and schedule large training runs when capacity is cheaper, where your provider offers it.
• Set quotas per team and alert when spend approaches budget.
• Review utilisation regularly and right-size pools as workloads change.

Signs your platform is working

A good platform is judged by what teams can do with it, not by the number of components it contains. These are the signals to look for in the months after launch.

• A new model goes from template to production without a bespoke infrastructure project.
• Any environment can be rebuilt from code, and a reviewed plan shows every change before it is applied.
• GPU utilisation is visible by team, and idle capacity scales down on its own.
• Teams cannot see or affect each other's workloads, secrets or data.
• Your own engineers handle routine operations using the runbooks, without outside help.

Hand over a platform your team can run

A platform succeeds when your own engineers can operate it. That means runbooks, dashboards and templates, and time spent building alongside the people who will own it.

Start with the workloads you run today, describe the platform as code from the first day, and add teams one at a time. Each new model then lands on foundations that are already secure, repeatable and paid for.