Cudo Compute vs Runcrate

Dedicated access to NVIDIA GPUs including GB300, B300, VR200, GB200 NVL72, HGX B200, H200, and H100 in SXM and PCIe form factors

Multi-node GPU clusters for training and inference, commissioned to acceptance criteria before entering production

Infrastructure across the UK, EU, North America, APAC, and the Middle East with in-region options for latency, residency, and regulatory needs

Foundational SRE included as standard with 24/7 monitoring, incident response, firmware management, and NVIDIA escalation paths

REST API and documented workflows for provisioning, scaling, and lifecycle automation

Controlled environments for enterprise and regulated workloads with jurisdictional control and sovereign data residency enforcement

Token-billed API calls and GPU hours draw on one credit balance with a single invoice, auto-recharge, and credits that do not expire

Chat, image, video, text-to-speech, speech-to-text and embedding models served behind OpenAI-compatible REST endpoints, so existing clients switch by changing the base URL

Bare-metal instances with SSH, Docker and custom images, provisioned in about 60 seconds

An unauthenticated endpoint publishes the live GPU rate card, refreshed every few minutes

Reserved bare-metal from 16 to 128+ nodes with NVLink fabric and managed Slurm job submission

Python and TypeScript SDKs, a Vercel AI SDK provider, a CLI for instance and volume management, and an MCP server for controlling the platform from AI assistants