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Cost at scale
Real projected training cost, not just the list price for a single run.
AWS, Azure and Google Cloud give our faculty and students the elastic compute, managed services and global reach to train models and run research that outgrows a single lab machine.
Why this partnership exists
We hold direct technical relationships with the major cloud providers, so research computing decisions are backed by their engineering guidance, not just documentation.
The right cloud choice is a research decision, not a preference.
Early student research projects kept hitting the ceiling of what a shared lab machine could train in a semester. Picking a single provider by default meant some projects didn't fit the platform.
Now we hold working relationships across the major providers, so research grants and capstone teams get matched to the platform that fits their actual compute, data and cost profile.
Every research project gets matched against the same checklist before compute is provisioned.
Real projected training cost, not just the list price for a single run.
GPU clusters, storage and pipelines that match the workload instead of forcing a rebuild mid-semester.
Data sovereignty and student-data requirements mapped before a single region is chosen.
Architecture kept portable enough that a provider is a choice, not a lock-in.
Research computing reviewed the same way we review any lab equipment request.
Compute needs, data sensitivity and budget shape the provider recommendation for each project.
Infrastructure-as-code from day one, so research environments are reproducible for the next cohort.
Staged rollout with cost alerts configured before a training run consumes a semester's compute budget.
Ongoing right-sizing so departmental cloud spend tracks actual research usage.
Compute that scales quietly when a project needs it.
The cloud should disappear into the research experience, not show up as a blocked training run.
That means autoscaling that actually holds under a real training job, monitoring that catches runaway costs early, and infrastructure students can eventually read and manage themselves.
Whichever provider a project lands on, the standard is the same: it should feel like infrastructure our own faculty would have chosen.
For the rest of our partner network, see all partners or talk to us.