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Rivac Labs
MLOps & LLMOps

AI Infrastructure Provisioning & Scaling

AI infrastructure tends to land on one of two expensive extremes — GPUs provisioned for peak load sitting idle most of the time, or capacity sized for the average case that throttles training runs and times out inference during real demand. This service sizes and provisions cloud, GPU, and edge infrastructure for training and inference workloads, then builds autoscaling that responds to actual load signals instead of static headcount guesses. It's for teams running ML or LLM workloads on infrastructure that's either burning cash sitting idle or falling over under real traffic. The result is a capacity plan and autoscaling setup that tracks demand instead of guessing at it.

How We’d Approach This

A clear, staged plan — not a black box

  1. 1

    Diagnose current infrastructure utilization across training and inference workloads to find idle spend and capacity gaps.

  2. 2

    Pilot right-sized provisioning and autoscaling rules on one workload to validate cost and performance before wider rollout.

  3. 3

    Review capacity plans and autoscaling thresholds with infrastructure and finance stakeholders.

  4. 4

    Roll out provisioning and autoscaling across remaining workloads, with utilization monitoring to catch drift from plan.

What You Get

Deliverables from this engagement

  • Infrastructure utilization audit for training and inference workloads
  • Right-sized provisioning plan across cloud, GPU, and edge resources
  • Configured autoscaling policies tied to real load signals
  • Utilization and cost-monitoring dashboard

Six Ways We Could Architect This

Different engagement, different build — pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.

Talk to us about AI Infrastructure Provisioning & Scaling
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