An AI factory is not just GPU hardware. It is the self-service layer that lets data scientists, ML engineers, and AI teams provision what they need instantly — without raising a ticket, without waiting days, without wasting GPU-hours on idle infrastructure.
Data scientists request GPU clusters from a catalog. A100s, H100s, or whatever you have. Provisioned in minutes, metered per GPU-hour, decommissioned when done.
Teams deploy trained models to managed inference endpoints — isolated, auto-scaled, metered per request. No infrastructure work for the consuming team.
Jupyter environments with GPU backing, per user or per team, on demand. Auto-expiry prevents GPU waste.
Every GPU-hour consumed by every team tracked automatically. Chargeback to cost centres. No wasted spend.
The Problem
Buying GPUs is the easy part. Turning them into a platform that hundreds of AI practitioners can use productively — without wasting hardware or burning out the infrastructure team — is the hard part.
Data scientists wait days for GPU access. By the time the environment is ready, the experiment context is lost and the sprint has moved on.
Without metering, GPUs run idle between jobs. Nobody knows which team is consuming what. Finance has no data. Waste is invisible.
Training a model is one problem. Serving it as a managed inference endpoint — isolated, scalable, metered — is another that most teams solve differently every time.
Without self-service, every GPU request goes through the infrastructure team. At 10 data scientists it is manageable. At 100 it is impossible.
The Solution
Add the self-service, metering, and governance layer above your GPU infrastructure. AI teams get instant access. Infrastructure teams get control. Finance gets visibility.
Define GPU cluster sizes, notebook environments, and inference endpoints as catalog items. Teams order from the catalog — provisioning happens automatically.
Each team's workloads run in isolated environments. No cross-team access, no shared secrets, no accidental resource contention between projects.
GPU allocations have hard quotas and configurable auto-expiry. Idle resources are reclaimed automatically — no wasted GPU-hours sitting unclaimed.
Every GPU-hour, every notebook session, every inference request tracked per team. Chargeback data always available — no manual tracking.
Cloud Orchestrator sits above OpenShift AI. The GPU Operator, KServe, and ML tooling stay unchanged underneath — we add self-service and metering above.
Every AI catalog action available via API. MLOps pipelines can request GPU environments automatically as part of training workflows.
What's in the catalog
Self-service GPU clusters — A100, H100, or your hardware. Provisioned in minutes, metered per GPU-hour.
Deploy trained models to managed inference endpoints. Isolated per team, auto-scaled, metered per request.
Jupyter environments with GPU backing per user or team. Auto-expiry prevents idle GPU waste.
Teams publish models, others consume them as managed services. Versioned, access-controlled, tracked.
Isolated compute environments for MLOps pipelines — provisioned via API from your training workflow.
Shared, quota-limited environments for exploration and PoCs. Auto-expires. No approval required.
Related use cases
Start with a complimentary 2-hour design workshop. We design your service catalog, tenant model, and 90-day pilot scope — with your team, on your infrastructure.