Fine-tuning-as-a-Service
Any model, tuned to know your business.
Fine-tune open-weight or proprietary 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.
General models lack business context
Benefits
Better models, without giving up control
Tune any model on your data, and stay in control of where it runs, which model you use and what it costs to operate in production.
Control
Your data, your boundary
Training data stays in the region you require, including sovereign setups or your own facility. Open-weight and owned models deploy in the same boundary, and every training run is logged.
Choice
Any model, your decision
Tune open-weight, proprietary or your own models, and decide where each one runs. We match the model to the task and keep evaluating new options, so your choices stay open as models change.
Economics
Fewer tokens, less compute
A tuned model applies your terminology and rules with less instruction per request, which can cut token use, response time and compute. A smaller tuned model can replace a larger one.
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 and proprietary 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
Tune
Evaluate
Deploy and refine
Enterprise AI Cloud
Tuning is one layer. We run the stack.
A tuned model only pays off when everything around it works: GPU capacity, data pipelines, inference endpoints and governance. Rackspace builds that stack with you, runs it in production and keeps improving it as models, data and requirements change.
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
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.
A tuned model only pays off when it’s built on the right data, run every day by people who own the outcome, and improved as models and requirements change. 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.
Enterprise AI
Extend fine-tuning across the Enterprise AI Cloud stack
Inference-as-a-Service
Serve tuned models through private, managed production endpoints.
GPU-as-a-Service
Provide dedicated or on-demand GPU capacity for training and inference.
Data Engineering
Prepare and govern data for model training and evaluation.
Palantir
Deploy tuned models within governed applications and workflows in Foundry and AIP.
Forward Deployed Engineering
Move fine-tuned use cases into production with engineers alongside your team.
Enterprise AI Cloud
Run fine-tuning within an integrated enterprise AI environment operated by one team.
FAQ
Common questions about Fine-Tuning-as-a-Service
What is fine-tuning an AI model?
Fine-tuning is the process of taking a pretrained AI model and training it further on a smaller, specific set of data so it performs better on particular tasks. The model keeps its general knowledge while learning an organization’s terminology, formats, rules and patterns, so it can apply them consistently with fewer instructions. Compared with relying on long prompts, a fine-tuned model is often more accurate, faster and less expensive to run for repeated tasks. Rackspace Fine-Tuning-as-a-Service fine-tunes open-weight and proprietary models on your data, inside your governed environment, and operates them in production.
What is Rackspace 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.
When should we fine-tune instead of using prompting or retrieval?
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.
Which AI models can be fine-tuned with Rackspace Fine-Tuning-as-a-Service?
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.
What data is needed to fine-tune an AI model?
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.
How does Rackspace Fine-Tuning-as-a-Service protect customer data?
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.
How does Rackspace measure and maintain AI model quality?
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.
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