Vast.ai Clusters
Dedicated GPU and CPU clusters for frequent AI, ML and HPC training runs, scaled from tens to hundreds of GPUs, with custom networking and storage, utilisation and cost dashboards, enterprise SLAs and specialist support, arranged through sales.
Find alternatives to Vast.ai Clusters
What it does
- GPU cloud
- Model training
Sources
- Pricing
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Clusters page read: it says "transparent pricing" and "no hidden fees" but publishes no rate or plan; buyers are sent to sales.
- Description
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Official documentation · 18 Oct 2026
"Scale your AI, ML, and HPC projects with customizable, on-demand clusters designed for continuous performance."
- What it does
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Official documentation · 18 Oct 2026
"You're training large or complex models (NLP, computer vision, recommendation engines) on a frequent basis."
- Sold within
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Read the Clusters page: it is presented as a product beside GPU Cloud and Serverless in the Products menu, but states no price, plan or inclusion.
- Name
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Official documentation · 18 Oct 2026
"Vast.ai Clusters let you deploy scalable, high-performance compute for your AI, ML, and HPC projects."
- URL
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Official documentation · 18 Oct 2026
The product's own page, titled "GPU Clusters | Vast.ai" and linked as "Clusters" in the Products menu.
- Deployment
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Official documentation · 18 Oct 2026
"Enterprise-grade security, SLAs, and round-the-clock support."; "Rapid deployment, so you can spin up production-grade clusters in hours, not weeks."
- Intended for
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Official documentation · 18 Oct 2026
"Enterprise-grade security, SLAs, and round-the-clock support."; "You require stable capacity, enterprise SLAs, and robust support for mission-critical workloads."
Also from Vast.ai
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Vast.ai GPU Cloud Infrastructure service
Marketplace for renting GPUs (RTX 3060 to B200 across 68+ types) with on-demand, interruptible and reserved terms billed per second, driven from a web console, API, CLI or Python SDK.
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Vast.ai Serverless Infrastructure service
Autoscaling GPU endpoints for AI inference on the Vast.ai fleet: workers are provisioned from load predictions, with a reserve pool for fast cold starts, defined from the Python SDK, CLI or API and billed per second at the platform's GPU rates.
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