Enterprise AI is the use of artificial intelligence inside a business’s core operations, applied to its own data, systems and workflows to improve decisions, automate work and serve customers. Unlike consumer AI tools, it has to run in production with the security, governance, reliability and cost control a business requires, across infrastructure, data, models, agents and applications working together. Rackspace builds, operates and continuously improves enterprise AI environments, so organizations can move AI into production and keep control of their data, technology choices and where their AI runs.

Rackspace Enterprise AI Cloud is how Rackspace builds, operates and continuously innovates enterprise AI environments. It brings together GPU capacity, managed inference and fine-tuning, data engineering, platforms such as Palantir, Forward Deployed Engineers and 24x7x365 operations across private, sovereign, hybrid, public and on-premises environments, with Rackspace accountable for running it in production.

No. Customers start wherever their needs begin, whether that is Private Cloud, accelerated compute, inference, data foundations or a complete enterprise AI program, and keep the environments that already work. Rackspace determines the right place for each workload based on performance, security, control, economics and sovereignty.

Rackspace is model-agnostic and does not own a foundation model. Customers can use open-weight and commercial models, fine-tune smaller domain models on their own data and connect to public cloud AI services where they fit.

Enterprise AI Cloud capacity is built on AMD Instinct GPUs, with options sized from edge and small-model inference through large-model inference and fine-tuning. Rackspace is also a member of the NVIDIA Cloud Partner Program, with competencies in compute, networking, visualization and NVIDIA Enterprise Software, and is bringing NVIDIA Blackwell into its Institutional Sovereign Pod for regulated and sovereign workloads.

GPU cloud providers are strong in accelerated infrastructure. Rackspace combines that infrastructure depth with engineering, governance and continuous production operations, so GPU capacity arrives as part of an environment Rackspace builds, operates and stays accountable for.

By optimizing the whole environment rather than one line item. The full cost of a workflow includes model usage, retries, human review, compute, data movement, licensing, security and operations, so Rackspace balances cost, quality, performance, risk and reliability together and keeps spend visible.