Cloud
Add compute as the work arrives. A fit for new projects, compute peaks, and distributed collaboration.
Research compute use cases
Compute that follows your research
Your data, software, and team are all different. Start with the workload, then choose cloud, on-premise, or both.
01 · Find your scenario
Life sciences research
More samples every month, yet analysis stalls on setup, queues, and lost files.
GenomicsSingle-cellBioinformatics
View use case AI research teams
The GPUs may be enough — if only people knew who was using them, and for how long.
Model experimentsGPU sharingTeam collaboration
View use case University labs
Machines scattered across personal accounts. Hardware is hard to share; environments are harder to hand off.
Hardware sharingMember accessEveryday ops
View use case 02 · Deployment options
Cloud, on-premise, or both?
There's no single right answer. Where your data lives, how long jobs run, and the hardware you already have all shape the call.
On-premise
Data and environments stay in-house. A fit for steady workloads and teams that already own hardware.
Hybrid
Keep everyday jobs local, and burst to the cloud when demand spikes.
03 · How it rolls out
Start with one real workload
From scoping to production: validate on a small scale first, then bring the rest online.
List the work
Software, data volume, run times, and how many people are involved.
Take stock
Review the servers, storage, and network you already have.
Pilot one job
Validate the environment and configuration with a representative task.
Scale up
Once it runs steady, onboard more jobs and team members.
Tell us about your workload
Not sure how to choose?
Tell us your team size, data volume, go-to software, and existing hardware. We'll find the blockers first, then talk specs.
No need to commit to cloud or on-prem up front
Existing hardware and software are all fair game