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Rackspace
Rackspace provides cloud infrastructure and managed services for enterprise AI, bringing together compute, data, models, applications and agents.
Its portfolio includes GPU capacity, managed inference, model fine-tuning, private cloud and engineering services.
- GPU-as-a-Service offers bare metal, managed Kubernetes and on-demand capacity for AI workloads.
- Inference-as-a-Service runs open-weight and fine-tuned domain models on private, managed endpoints.
- Fine-Tuning-as-a-Service covers data preparation, model training, evaluation and deployment.
- Private cloud options include dedicated, multi-tenant, bare metal, sovereign, hybrid and on-premises environments.
- Forward Deployed Engineers work alongside organisational teams to build, deploy and operate AI solutions.
Introduction
Rackspace provides cloud infrastructure and managed services for enterprise artificial intelligence. It builds and operates environments that bring together infrastructure, data, models, applications and agents.
Its services include GPU capacity, managed inference, model fine-tuning and engineering work. Private cloud options range from dedicated and multi-tenant environments to bare metal, sovereign, hybrid and on-premises configurations.
What Rackspace offers
Rackspace offers services across the enterprise AI stack, from compute and data engineering to application deployment and ongoing operations. Organisations can start with individual capabilities or an enterprise AI programme.
Cloud services include managed operations, migration and modernisation, platform automation, security and resilience, and advisory work.
Product and service categories
Enterprise AI Cloud
Enterprise AI Cloud brings together infrastructure, compute, data, platforms, applications and agents. The service includes GPU capacity, managed inference, fine-tuning and data engineering. Rackspace also offers engineering and operations across private, sovereign, hybrid, public and on-premises environments.
GPU-as-a-Service
GPU-as-a-Service provides GPU infrastructure for AI workloads. The options are dedicated bare metal servers, managed Kubernetes and on-demand capacity. Rackspace manages the GPU environment, including monitoring, drivers, firmware and capacity planning. The site also describes GPU capacity for training, fine-tuning and inference.
Inference-as-a-Service
Inference-as-a-Service runs AI models on private, managed endpoints. Open-weight models and domain models fine-tuned on an organisation’s data can be served through the service. Options include shared capacity, a dedicated stack and deployment in a sovereign configuration or an organisation’s own facility. Rackspace deploys, monitors and operates the endpoints.
Fine-Tuning-as-a-Service
Fine-Tuning-as-a-Service adapts AI models using an organisation’s data, terminology and requirements. The process covers preparing training data, selecting a base model, training and evaluation. Tuned models can be deployed through managed inference endpoints and monitored in production. The site also describes retraining models as data, policies and products change.
Forward Deployed Engineering
Forward Deployed Engineers work alongside an organisation’s team on AI use cases. Their work includes building and deploying solutions and managing them in production. An engagement can include discovery, team design, iterative development and ongoing operation. Rackspace also offers Elastic Engineering for cloud work such as migration, modernisation and automation.
Dedicated and multi-tenant private cloud
Dedicated Private Cloud provides physically isolated compute, storage and networking for one organisation. Multi-tenant private cloud uses shared infrastructure with logically isolated resources for each tenant. Its self-service provisioning covers virtual machines, networks and storage within the organisation’s defined quotas, roles and policies. Rackspace operates both types of environment.
Bare metal and other cloud options
Bare Metal Private Cloud provides single-tenant physical servers without a virtualisation layer. Rackspace manages the hardware, network and data centre operations, and optional GPUs are available. Sovereign private cloud is offered alongside hybrid and on-premises options. Hybrid cloud connects private infrastructure with public cloud services.
Data engineering and advisory
Data engineering includes connecting, preparing and managing enterprise data for AI applications and agents. For model fine-tuning, Rackspace can select, clean and structure representative training data. AI advisory covers opportunities, technologies and operating requirements. The site also lists cloud strategy and AI readiness assessments.
Browse the full range on the Rackspace website.
Using the website
The site includes an interactive Enterprise AI Cloud stack and an insights section covering cloud, data and AI topics. The site also includes case studies.
Notable features
Rackspace offers managed operations around the clock across its cloud and AI environments. Depending on the service, operations include monitoring, patching, lifecycle management and incident response.
For AI solutions, engineers can work within an organisation’s tools, data and workflows. The described process includes sharing documentation and knowledge with the organisation’s team.
Frequently asked questions
Which AI models can be used?
Rackspace describes support for open-weight and commercial models. Organisations can also fine-tune smaller domain models on their own data and connect to public cloud AI services.
What data is needed for fine-tuning?
Fine-tuning requires representative data reflecting the tasks the model will perform. Examples on the site include documents, decisions and example responses.
How are fine-tuned models evaluated?
Rackspace evaluates models against defined benchmarks before production deployment. After deployment, it monitors models for performance, quality and drift.
What can teams provision in multi-tenant private cloud?
Teams can provision virtual machines, networks and storage through self-service. Provisioning operates within the quotas, roles and policies defined by their organisation.
Can Dedicated Private Cloud connect to public cloud services?
Yes. Rackspace connects dedicated environments to public cloud services through its private network as part of a hybrid environment.
Who owns work developed during a Forward Deployed Engineering engagement?
Organisations retain ownership of their data and customer-specific intellectual property developed during the engagement. Rackspace may reuse general technical patterns, workflow structures, pipeline designs and infrastructure templates.
Key takeaways
- Enterprise AI services cover infrastructure, data, models, applications and agents.
- GPU capacity is available through bare metal, managed Kubernetes and on-demand options.
- Managed inference and fine-tuning services cover model deployment and ongoing operation.
- Private cloud options include dedicated, multi-tenant, bare metal, sovereign, hybrid and on-premises environments.
- Forward Deployed Engineers work alongside organisational teams on AI solutions.
Summary
Rackspace offers cloud infrastructure and managed services for organisations working with enterprise AI. Its portfolio covers individual capabilities such as GPU capacity and data engineering, as well as private cloud, embedded engineering and ongoing operations.
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This guide was written by AI. It does not represent the views or opinions of the website owner.
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Updated November 2026
