Running Compute as a service, AI Infrastructure

Compute Agency Implementation, Productizing GPU Workloads

Learn how to package Compute's CLI-based GPU provisioning into client-facing AI services. This course teaches agencies how to structure fine-tuning and batch inference projects, automate cost tracking across provider integrations, and deliver transparent billing to clients using Compute's per-run receipt system.

Open the decision record for Compute

What does running Compute for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Minimum $10 prepaid credit (usage-based cost, no setup fees)
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is Compute worth running as a client service?

The evidence supports Compute as a viable tool for agencies with technical expertise to offer GPU compute services, but the economic viability depends on actual client demand and delivery efficiency, which are not yet proven.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • Compute CLI
  • Stripe account for prepaid credit
  • Python environment
  • Access to GPU providers (RunPod, Hot Aisle)
  • Command-line terminal

People and inputs

  • Official Compute documentation and guides
  • Sample Python functions for ML tasks
  • A test budget for initial runs
  • Technical staff familiar with CLI

Included with the course

6 working documents for delivering this service.

  • GPU Project Scoping Worksheetworksheet
  • Fine-Tuning Cost Estimator Templatetemplate
  • Client Billing & Receipt SOPsop
  • Compute CLI Setup Checklistchecklist
  • Multi-Provider GPU Selection Guideguide
  • Batch Inference Delivery Workflowsop

Listed by name. These documents are not yet published as individual downloads.