Built for heterogeneous AI stacks spanning CUDA, ROCm/HIP, and SYCL/oneAPI, with optimized networking, storage, and runtime paths across public cloud, private, sovereign, and air-gapped environments.
# one spec — every environment kind: AIFactory spec: accelerator: amd-mi300 runtime: rocm-hip environment: air-gapped fabric: infiniband-rdma storage: checkpoint: parallel-fs vector: nvme-object gateway: agent-secure harness: sandboxed runtime: policy-enforced
Enterprises can buy powerful silicon. What's painful is everything around it — the tooling, deployment patterns, portability, tuning, and operations — which fragments into a bespoke project for every environment, and a developer experience that drifts from team to team.
A single specification that already encodes the accelerator, fabric, storage, and runtime tunables for each target — promoted unchanged from cloud pilot to sovereign or air-gapped production.
Preconfigured JupyterLab, runtimes, drivers, and kernels — the same CLI, notebooks, and API clients in every environment, with zero code changes between them.
Runs inside the customer's own VPC, datacenter, sovereign region, or air-gapped enclave. You hold the keys, the data, and the control plane.
First-class support and tuning paths for today's accelerators — and an extensible contract for what comes next.
Portability across CUDA · ROCm/HIP · SYCL/oneAPI — plus an extensible path to emerging and custom accelerators.
Deploy-anywhere only matters if it performs everywhere. The platform tunes the paths that decide whether a cluster actually delivers — not just the accelerator on paper.
The model reasons. The harness gives it skills, memory, and tools. The runtime governs what it can see, do, and reach. CloudsAI is that secure runtime — and the deploy-anywhere infrastructure beneath it — model-, harness-, and runtime-agnostic, with policy enforced out of the agent's reach, identical in public cloud, sovereign, or air-gapped.
Per-session system and network isolation — agents install packages and run code they can't use to touch the host. Created and torn down on demand.
Deny-by-default across filesystem, network, and process, checked by the runtime before any action — enforced outside the agent's reach, even if it's compromised.
The agent never sees raw tokens. Scoped, per-agent credentials are injected at the sandbox boundary — so a compromised agent still can't exfiltrate.
Skills, memory, and sessions persist across rebuilds — snapshot, tear down, restore, and the agent resumes exactly where it left off.
Keep sensitive context on local models; route to frontier models only when policy allows. Swap models without touching the harness.
Full traces of every tool call, handoff, and allow/deny decision — the record regulated and sovereign environments require.
Run popular agent harnesses and secure agent runtimes unmodified, on open models served via NIM, vLLM, or self-hosted — across NVIDIA, AMD, Intel, and beyond.
Preinstalled, tested, and fully supported — so the experience doesn't move when the infrastructure does.
Runs across five deployment models — explore deployment models ↗
Bring your accelerators, your environment, and your constraints. We'll walk through how the platform deploys, tunes, and operates against them.