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Fine-Tuning-as-a-Service

Models turned to your business.

Fine-tune open-weight models on your data, terminology and policies, privately and inside your boundary. We prepare the data, run the training, evaluate the results and deploy the tuned model to production, so it can apply your requirements consistently with less instruction at inference.

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General models lack business context

Out of the box, general models often require detailed prompts and examples to apply domain-specific terminology, formats and rules consistently. That adds tokens, latency and cost to each request. Fine-tuning adapts the model to those requirements, reducing the instructions needed at inference.

Custom AI Models

Domain models, built and operated for production.

Training inside your boundary

Training runs on your data within your environment, keeping that data under your control and separate from external model training.

Models sized for the task

Focused models can use fewer tokens and less compute than general models that rely on extensive prompting.

Base models selected for your requirements

Open-weight models are evaluated for quality, licensing requirements and compatibility with the infrastructure where they will run.

Evaluated before production

Benchmarking and validation measure model performance against your requirements before the model moves into production.

Integrated with managed inference

Tuned models deploy to managed inference endpoints that connect with your applications, agents and production workflows.

Retrained as requirements change

Models can be retrained as data, policies and products change, with each version tracked, evaluated and compared.

How it works

From your data to a production model

Prepare

Choose the base model, then select, clean and structure representative data for training.

Tune

Managed training adapts the model to your terminology, formats and requirements.

Evaluate

Test the tuned model against your requirements for accuracy, consistency and safety.

Deploy and refine

Deploy the model on managed inference, then monitor performance and retrain as requirements change.

Economics

Use fewer tokens and less compute at inference

A fine-tuned model can apply your terminology and rules with less instruction on each request. That can reduce token usage, response time and compute requirements at inference, lowering operating costs across high-volume production workloads.

Explore Enterprise AI Cloud

Control

Your data, your model, your boundary

Training data stays within the region your requirements dictate, including sovereign configurations or your own facility. The tuned model is deployed within the same boundary, and each training run is logged to provide a traceable record of the data used for each model version.

Use Cases

Where fine-tuned models add value

Adapt AI models to your business needs to improve accuracy, streamline workflows and support more reliable, context-aware outcomes.

Domain language

Apply clinical, legal and financial terminology consistently with your domain’s established usage.

Policy-aware decisions

Apply defined rules consistently across approvals, eligibility decisions and compliance checks.

Document extraction at volume

Extract information from contracts, invoices and forms according to your required fields.

Classification and routing

Classify requests, cases and messages using your criteria, then route them appropriately.

Customer communications

Generate responses aligned with your terminology, tone, disclosures and communication standards.

Specialist agents

Tune models for agent tasks requiring consistent terminology, rules and decision patterns.

Operating model

Build. Operate. Innovate.

GPU capacity only pays off when it is sized to the work, run every day by people who own the outcome, and refreshed as the hardware and software move. One team does all three.

Build

Data to model

Data preparation, base model selection and training are handled by the engineers responsible for the production model.

Operate

Monitored in production

Tuned models are monitored for quality and drift in production, with one team managing ongoing operations.

Innovate

Always evolving

New base models and training techniques are evaluated and introduced as they demonstrate value for your requirements.

CONTACT US

Build a model that knows your business.

Tell us the task and the data behind it. A Rackspace expert will show you what a tuned model could do and how quickly it could be in production.

FAQ

Common questions about Fine-tuning-as-a-Service

Rackspace Fine-Tuning-as-a-Service is a fully managed AI service that adapts open-weight AI models to an organization's specific data, terminology and business requirements. Rackspace manages the entire process, from data preparation and model training to evaluation and production deployment through managed AI inference, all within the customer's governed environment.

Prompting and retrieval work well when models need current information from your documents. Fine-tuning is useful when a model must consistently apply domain-specific terminology, formats or rules, or when extensive prompting adds token usage, latency and cost. Production systems can also combine fine-tuned models with retrieval.

Rackspace Fine-Tuning-as-a-Service supports open-weight AI models selected based on quality, licensing requirements and infrastructure compatibility. With a model-agnostic approach, Rackspace helps customers evaluate and select the most suitable base model for their specific use cases and business requirements.

Fine-tuning an AI model requires representative data that reflects the tasks the model will perform, such as documents, decisions and example responses. Rackspace Data Engineering helps customers select, clean and structure their data to prepare it for effective model training.

Rackspace Fine-Tuning-as-a-Service helps protect customer data by keeping model training within the customer's governed environment, whether in-region, in sovereign configurations or on-premises. Customer data is not used to train external models, and each training run is logged to support auditing and compliance.

Rackspace evaluates fine-tuned AI models against defined benchmarks for accuracy, consistency and safety before production deployment. Once deployed, models are continuously monitored for performance, quality and drift to help maintain reliability over time.

Typically with an AI readiness assessment or a cloud strategy assessment, followed by a working session that produces a prioritized plan with business cases. From there, Rackspace can prove a first solution, take it to production and operate it.