You want the benefits of AI, but your data is too valuable, too regulated, or too sensitive to hand to a public service. VMware Private AI Cloud lets you run AI inside your own environment, on the VCF platform you already trust.
What “metal to model” means
A Private AI Cloud covers the full stack, from physical servers to a model answering requests:
- GPU enablement. GPUs are brought online and organized into shared pools.
- Networking and storage. The network and storage are set up to meet AI demands.
- Model serving. A vLLM-based runtime serves the models you choose, with support for more than 150 OpenAI models in VCF 9.1.
- Multi-tenant sharing. Multiple teams or business units share GPU capacity safely and fairly.
- Observability. Tokenomics shows usage and cost at the token level, so you know what AI costs and who’s using it.
VMware Private AI Cloud and VMware AI Factory automate much of this path, so you’re not stitching together separate tools.
Why Private AI
- Control: Your data stays in your environment.
- Cost visibility: Token-level tracking makes AI spending measurable and accountable.
- Efficiency: Shared GPU pools make the most of expensive hardware.
- Familiar operations: AI runs on the same platform, tools, and processes your team already uses.
How ClearBridge helps
ClearBridge designs and deploys Private AI Cloud environments from metal to model. We fit the architecture to your workloads, enable your GPUs, set up model serving and multi-tenancy, and help your team run it with confidence in production.
Ready to bring AI into your private cloud? Talk to ClearBridge about a Private AI build.
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