Inference-as-a-Service
Production inference, on your terms.
Serve open-weight and fine-tuned models on private, managed endpoints running on governed infrastructure. Rackspace handles deployment, scaling, optimization and 24x7x365 operations, so applications and agents get fast, reliable answers at a cost you control.
Every AI answer has a price. At scale, it adds up fast.
Built for production
Everything it takes to run models in production.
Private, managed endpoints
Secure, production-ready endpoints that connect models to your applications and agents, operated by Rackspace.
Your choice of models
Open-weight models and the domain models you fine-tune, evaluated and selected for each use case.
Tuned for cost per result
Routing, placement and model optimization that balance response quality, latency, GPU utilization and cost.
Scales with demand
Capacity that grows and contracts with traffic, so peaks are served and quiet hours cost less.
Production reliability
Monitoring, incident response and continuous tuning as workloads and models change.
Governed and auditable
Access controls, logging and data boundaries built in, and your prompts and data never train external models.
Economics
Inference that pays its way
Cost per result matters more than cost per GPU hour. Smaller models fine-tuned on your data need far fewer tokens to do the same work. Steady, high-volume workloads often cost less on private endpoints than on metered usage. Right-sized accelerated compute and high utilization bring the cost of every answer down as usage grows, and consumption flexes so spend follows value.
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Deployment options
Serve models the way your workload needs
As-a-service
Managed inference endpoints on shared capacity, the fastest way to put a model into production.
On a dedicated stack
Inference running on bare metal or managed Kubernetes reserved for your organization, for isolation and predictable performance.
Inside your boundary
Inference in sovereign configurations or your own facility when data has to stay in its jurisdiction.
Workloads
What enterprises serve with Inference-as-a-Service
Agents and assistants
Agents that reason, call tools and act across enterprise systems at a pace users expect.
Enterprise search and retrieval
Answers grounded in your documents and data, with access controls intact.
Document intelligence
Contracts, invoices and records read, classified and summarized at volume.
Customer and employee service
Conversational AI for contact centers, help desks and internal support.
Real-time decisions
Fraud screening, triage and risk scoring that respond in the moment.
Clinical and regulated AI
Summarization and decision support on data that stays governed.
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
Deployed for production
Models packaged, deployed and connected to your applications by the team that will run them.
Operate
Run like it's our own
Endpoints monitored, scaled, secured and tuned every day, with one team to call.
Innovate
Better over time
New models, serving techniques and accelerators brought in as they prove out, lowering cost per result as you grow.
Enterprise AI
Inference works best with the rest of the stack
Fine-Tuning-as-a-Service
Domain models trained on data that stays private.
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GPU-as-a-Service
GPU capacity as bare metal, Kubernetes or on demand.
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Data Engineering
The data foundation retrieval and agents depend on.
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Palantir
Foundry and AIP from proof of value to production.
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Forward Deployed Engineering
Engineers beside your teams, from use case to production.
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Enterprise AI Cloud
Full-stack, designed, built and operated to keep you in control.
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CONTACT US
Put your models into production, at a cost you control.
FAQ
Common questions about Inference-as-a-Service
What is Rackspace Inference-as-a-Service?
Rackspace Inference-as-a-Service is a managed service for running AI models in production on private endpoints. Rackspace deploys, scales, optimizes and operates the models 24x7x365 on governed infrastructure, available as a service on shared capacity or as part of a dedicated stack.
Which models can we run?
Open-weight models and domain models fine-tuned on your own data, evaluated and selected for each use case. Rackspace is model-agnostic and helps choose the model that best balances quality, speed and cost.
How does Rackspace keep inference costs under control?
By optimizing for cost per result: routing and placement that match each request to the right model and capacity, smaller fine-tuned models that need fewer tokens, right-sized accelerated compute run at high utilization, and private endpoints for steady, high-volume workloads where they cost less than metered usage.
Where does inference run?
In Rackspace data centers in the region your obligations require, in sovereign configurations operated by security-cleared local teams, or in your own facility. Prompts and data stay inside that boundary and are never used to train external models.
How does inference scale?
Capacity scales with traffic on shared infrastructure, and dedicated stacks are sized for predictable performance. Rackspace monitors usage and tunes capacity continuously as demand changes.
When should we use Inference-as-a-Service instead of a public AI API
Public AI APIs are a good fit for experimentation and variable, low-volume use. Inference-as-a-Service suits production workloads that are steady or high in volume, handle sensitive or regulated data, or need predictable cost and performance. Many organizations use both.
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.