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AI infrastructure

AI infrastructure and MLOps in production

We build the infrastructure needed to run models continuously: deployment pipelines with versioning, automatic evaluation before every promotion, GPU autoscaling with cost control, and deployment to cloud, hybrid or your own servers.

From proof of concept to production

The gap between a notebook that works and a service that holds is where most AI projects die. What is missing is usually the same set of things: model and data versioning, a staging environment identical to production, automatic evaluation before promotion, and observability to know whether the model is degrading.

What we set up

The typical scope of an infrastructure phase.

  • Model-specific CI/CD pipelines with pre-promotion evaluation
  • A model registry with versioning and rollback
  • GPU autoscaling with spend limits and idle shutdown
  • Observability: latency, cost per request and model drift
  • Deployment on AWS, Azure, GCP or your own hardware
  • Encryption in transit and at rest, aligned with ISO 27001

Controlling GPU cost

GPU instances are the fastest-growing line item and the least watched. We configure scale-to-zero on idle, request batching so each cycle earns its keep, and a budget alert that fires before the spend hits the ceiling rather than after. On deployments we have inherited, those three measures typically cut the monthly bill substantially.

When hybrid makes sense

With a constant base load and occasional spikes, the cheapest combination is usually owned hardware for the base and cloud for the peaks. With irregular volume, pure cloud wins despite the hourly price. We run that calculation on your numbers before recommending anything.

FAQ

Can we run models without sending data outside?

Yes. Open models such as Llama, Mistral or Qwen deploy inside your own infrastructure and no data leaves your network. Cost shifts from per-request consumption to hardware and its operation, which pays off above a certain volume. We calculate that break-even with your real figures.

How do we know a model is degrading?

We instrument three signals: the distribution of inputs against the training distribution, results on a fixed evaluation set run on a schedule, and the final business metric. When inputs change shape, you see it before the outcome shows up in the business.