Keep the loop moving
Agents can launch batches, read status and cost as JSON, retrieve results, and prepare outputs for the next decision using predictable commands.
For people and agents
Guardrails let agents safely run jobs, deploy services, and launch VMs on CPU and GPU infrastructure.
curl -fsSL https://get.anycloud.sh | shanycloud jobModel training, evaluations, batch inference, and data processing.anycloud serviceModel APIs, inference endpoints, and research dashboards.anycloud vmInteractive exploration, debugging, and custom research workflows.Agent guardrails
Your team sets the research direction. Claude Code, Codex, Cursor, and Aider operate between decisions, while analytics, throttles, budgets, and alerts keep each agent session visible and bounded.
Agents can launch batches, read status and cost as JSON, retrieve results, and prepare outputs for the next decision using predictable commands.
Scope budgets, burn-rate throttles, and alerts to each detected agent session. When a cap is reached, new jobs wait while running jobs finish. Explore spend controls.
Launch, monitor, repeat—with controls
$ anycloud budget set 4000 --per day --agent-session Budget day (resets in 19h) per agent-session cap $4,000 each train-llama-sft $3,848 / $4,000 $152 left lr-sweep $3,136 / $4,000 $864 left eval-harness $368 / $4,000 $3,632 left
Agents in production
At Ångstrom AI, researchers chose the experiments and interpreted the results. Agents launched batches of jobs, monitored status, downloaded outputs, and generated plots and summaries for the next decision—all under per-session spend controls. The work spanned 100,000+ GPU hours.
“We have been using anycloud at @Angstrom_ai to make the gpus go brrrrrr!”
Execution modes
Run AI research as finite jobs, turn results into long-lived services, or drop down to a VM for direct compute access—all behind the same cloud-account workflow.
anycloud jobRun finite work to completion.
anycloud serviceKeep an HTTP workload online.
anycloud vmWork directly with provisioned compute.
Prefer Python? Jobs and Services are also available through the Python SDK.
Your cloud accounts
Choose where work runs while infrastructure, credentials, data, credits, and billing stay in your accounts.
Pin a cloud account, region, or hardware profile—or let anycloud make a cost-aware choice from matching compute.
Use the same container image across workload modes, with the environment, secrets, and runtime settings each one needs.
Your infrastructure, credentials, data, credits, and provider billing stay in the cloud accounts you control.
anycloud job --gpu-type h100 --spotFor one job, the lowest price across every region in every account wins. Spot prices are illustrative.
How it works
Run it yourself or hand it to an agent; anycloud follows the same path from cloud-account selection through provisioning, operation, and cleanup.
Start from the workload abstraction that fits.
Choose credentials, region, and hardware.
anycloud starts matching compute in that cloud account.
Follow the workload through its mode-specific lifecycle.
Release compute at completion or when you terminate the workload.
Optional cost optimization
For checkpointed Jobs, spot can lower the cost of long runs. If capacity is reclaimed, anycloud cleans up the lost VM, provisions a replacement, restores /mnt/checkpoint, and restarts the container.
Your team sets the direction and limits. anycloud handles execution through the same CLI.