From one GPU to an elastic ML platform

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

The challenge had three focus areas.

We ran the engagement as advisory and enablement, prototyping and validating the approach and then equipping the client's team with the architecture and step-by-step guidance to build and run it themselves.

The engagement delivered a validated architecture and the hands-on guidance for the client to stand up a multi-user SageMaker platform, meeting the success criteria of an operational domain, validated access controls, an end-to-end pipeline design, model versioning, per-user monitoring, and a completed knowledge transfer.

• Scale on demand — Ephemeral GPU training jobs replace a single fixed GPU, so training throughput is no longer capped by one machine and the client only pays for compute in use.
• Safe multi-tenancy — A single domain with VPC isolation, per-user execution roles, and attribute-based access control lets developers and customers share the platform without seeing each other's data or models.
• Cost under control — Managed spot training with checkpointing, per-user GPU quotas enforced automatically, and budget alerts keep expensive GPU training predictable.
• Enterprise-grade traceability — CloudWatch dashboards plus CloudTrail with SourceIdentity give the client per-user audit and failure visibility.
• Handover — Included the architecture, guidance documents, and recorded walkthroughs so the client's team can build, run, and extend the platform independently.

With a clear SageMaker blueprint and a cost model that works at scale, the client is positioned to onboard more customers onto self-service training and to grow its AI-driven manufacturing platform, no longer bound to a single GPU but ready to support whatever the business takes on next.