Researchers need scale when they need it — not a contract for it. Burst capacity, spot pricing with auto-checkpoint, and no annual commitment.
Cloud providers throttle GPU quota for research accounts. Getting a quota increase takes weeks. By the time it's approved, your experiment window has closed.
Spot instances are cheap — until they get interrupted 18 hours into a 24-hour training run. Without auto-checkpoint, that's a full run wasted and a deadline missed.
Research grants aren't elastic. An unexpected cost spike from reserved capacity you didn't use, or a run that didn't terminate cleanly, can wipe out months of budget.
You need a 32-GPU cluster for three days. Provisioning reserved capacity requires a lead time measured in weeks, not hours. Experiments don't wait for capacity planning cycles.
The research workflow doesn't fit a reserved capacity model. You need burst access, not a contract. Aircloud is designed for workloads that run hard for a week and go quiet for a month.
Run preliminary experiments at the lowest available prices. Community-tier GPUs are ideal for small-scale probes, hyperparameter sweeps, and anything that doesn't need enterprise isolation.
Move larger training runs to spot instances. Aircloud auto-checkpoint saves your model state on interruption so runs survive preemption. Resume from the last checkpoint automatically.
When your experiment demands it, burst to multi-node GPU clusters — no quota request, no lead time. Spin up, run the experiment, spin down. Pay for what you used.
Up to 60% below on-demand rates. Use spot for training runs that can tolerate interruption with checkpoint. Same H100 hardware, lower cost.
Aircloud detects imminent interruption and saves model state automatically. Your training resumes from the last checkpoint — no custom code required.
Research funding cycles don't map to annual cloud contracts. Use what you need, when you need it. No minimum spend, no penalty for going quiet.
Spin up for a single experiment. Reserve capacity for a conference deadline sprint. Match your infrastructure term to your project timeline.
A run that terminates early doesn't cost a full hour. Budget your grant accurately — your spend tracks actual compute time, not billing period rounding.
Idle infrastructure doesn't cost anything. Between experiments, you pay nothing. Resume when the next run is ready.
Not every run needs an H100. Use the right hardware tier for each stage of your research workflow and keep your grant budget where it counts.
The right GPU for foundation model training, large-scale fine-tuning, and multi-node distributed runs. NVLink fabric for multi-GPU workloads.
Proven workhorse for training runs of any scale. Strong price-to-performance for most research training workloads that don't require H100-class throughput.
Enterprise-grade hardware at community-tier prices. Best for smaller models, rapid prototyping, ablation studies, and experiments where cost matters more than throughput.
Start free. Apply for research credits if you're working on open research. No annual commitment, no quota paperwork.