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Service 06

AI Infrastructure & DevOps

The deployment, monitoring, and reliability layer that keeps your AI fast and available under real load.

The problem

A model that works in a notebook and a model that survives production traffic are different engineering problems. Without autoscaling, observability, and a rollback plan, an AI feature is one bad deploy away from an outage.

Our approach

We build the reliability layer beneath your AI system: deployment and autoscaling, monitoring and alerting, cost and latency observability on every model call, and an incident runbook so a rollback is a decision, not a scramble.

Use cases
  • Standing up deployment and autoscaling for an AI feature going into production for the first time
  • Adding cost and latency observability so model spend is a dashboard, not a monthly surprise
  • Building the incident runbook and rollback plan before, not after, a bad deploy
  • Auditing an existing AI system's reliability posture and closing the gaps
Representative stack
Containerised deploys and CI/CD pipelinesObservability tooling (logging, tracing, alerting)Serverless and edge runtimes where they fit the load profileCost and latency dashboards scoped to model calls
How we engage

Often paired with an AI System Architecture engagement — the architecture is what you build, this is what keeps it up.

Where you can see it

This is the layer that keeps Omos serving ExamSurf and Sydence — deployment, autoscaling, and per-call cost/latency observability so an inference spike is a dashboard entry, not a surprise bill.

In build / in useOmos
Have a problem shaped like this?

Tell us what you’re building and we’ll scope where AI Infrastructure & DevOps fits.

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