Trusted by teams building the world's AI infrastructure
Bring your own stack and rent the compute, or go straight to the models. Both run on the same platform, and nothing stops you using both.
No procurement, no setup. Log in and train, fine-tune, or call models.
Hardware to scheduler tuned end to end, so training and inference run fast and stable.
Rent GPUs as you need them and scale with demand, keeping cost at a minimum.
Model, dataset, and image libraries with versioning and permissions.
Per-member permissions, projects separated, every action logged.
Live dashboards for GPU usage and performance, so every workload stays visible.
GPU resources were available as soon as we requested them, with no queuing or allocation failures, and our long-running training jobs ran stably through to completion. Setting up a training environment is straightforward: pick an image, configure compute, and the job is ready. We could also reuse our existing environment images, which lowered our setup costs.
The Zettabyte platform provides flexible GPU resource management through its Kubernetes architecture. Our team can allocate compute to different AI tasks, which makes model training, data generation, and experiment validation easier and more efficient.
Get a 30-minute free trial.
Log in and start an H100
Enterprise GPU compute and model access on one platform. Rent GPUs, train and fine-tune, or call leading models through one API.
Environments deploy in seconds. Install the CLI, set a key, and launch an instance.
H100 and H200 today, B300 per deployment.
Yes, 30 minutes on an H100. Log in, pick a spec, and it's ready in seconds.
Pick the closest card above, or choose "Not sure yet" in the form and we'll figure it out with you.
Tell us who you are through the form. We scope a package together and your team gets access, allocation, and support from ours.
Tell us who you are and which track fits. We come back to you with a package for your team.
