What is Models-as-a-Service?

Models-as-a-Service (MaaS) is an approach to delivering AI models as shared resources, allowing users within an organization to access them on demand. MaaS offers a ready-to-go AI foundation—in the form of application programming interface (API) endpoints—that encourages private and faster AI at scale. 

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Many want to use AI, but taking advantage of private models for the enterprise is where most organizations get stuck. Pretrained AI models from public sources like Hugging Face are becoming more accessible. And if you have the right hardware—like extensive graphical processing units (GPUs)—getting your models up and running can seem like a realistic goal. But here’s the issue: Once the model is up and running, who can use it? Furthermore, who can scale it? 

To scale a private AI model, you need to make a single model instance accessible to multiple users and multiple applications at once. Otherwise, the models are accessible only to the user who created it, which is really inefficient. That’s where MaaS comes in. 

Models-as-a-Service allows privately shared model access across 1 organization’s teams and applications, without sacrificing control over data. 

As organizations begin to adopt AI, it’s common to start with easy-to-use tools and interfaces. But as use grows, the focus shifts from experimenting with a couple of models to running AI at scale. You might start with a few specific models in production, but over time you’ll likely run many different types: language models, image models, audio models, and more—often with multiple versions and use cases.

This means moving from an “artisan” approach―doing everything manually―to more of a “factory” approach, where you manage models efficiently and consistently.

Managing all of this in a reliable, scalable way is what Models-as-a-Service is all about.

You don’t need public AI providers to explore AI patterns, such as retrieval-augmented generation (RAG), agentic, and coding assistants. Private AI models can power these same tools without impacting ease of use for the end user.

Models-as-a-Service is meant to support the use of openly available large language models (LLMs) like Mistral, Llama, DeepSeek, and more. It also isn’t limited to pretrained foundation models. MaaS can serve fine-tuned models or even built-from-scratch, predictive AI models, all on the same fully supported platform. 

In a typical MaaS implementation, an IT or AI platform engineering team makes AI models available through API endpoints for internal customers like developers and business users. Generally, MaaS environments are built on hybrid cloud AI platforms with API gateways to simplify integration across teams and operations. Key components of MaaS include models, a scalable AI platform, an AI orchestration system, and API management. All these moving parts equip your Models-as-a-Service to support a scalable AI strategy. 

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A well-rounded MaaS solution makes your AI integration easier. It saves time and money and helps you retain control over your AI strategy. MaaS is defined by these qualities: 

Accessible and scalable. Organizations often build private AI to maintain control over their AI strategies. But if it’s hard to use, no one will adopt it. For private AI to succeed, it needs to be as user friendly as public AI services (like OpenAI, OpenRouter, or Gemini). MaaS should be accessible to those who aren’t AI experts so it can scale properly. It should also integrate with your daily tasks as well as scale across organization-wide operations.

Trackable and adjustable. It’s important to know who is using Models-as-a-Service, how much they’re using it, and why. Then you can either report that usage (showback) or charge for it (chargeback). If you can’t track usage, it’s hard to manage cost, capacity, or fairness across teams. 

Transparent and secure. To take full advantage of your private AI model, your unique enterprise data is essential—as is following strict rules that govern where this data can be sent. MaaS lets you make this model your own and retain full control over your data. Be wary of “black box” models that don’t provide transparency. Explainability and traceability help you understand your AI model, improve efficiency, and maintain ethical AI best practices.

What is enterprise AI?

Large Language Models-as-a-Service (LLMaaS) is a type of MaaS that specializes in LLM capabilities, such as complex language processing.  

LLMs are deep learning models that are capable of computing large amounts of data in order to understand and communicate in various languages. LLMs are used for generative AI and commonly used for creating chatbots. But they’re also at the core of most modern AI use cases today, such as RAG, agentic AI, and coding assistants. 

Compared to LLMaaS, MaaS is more technology agnostic. As more types of models emerge, MaaS will be able to adapt. This flexibility helps model serving and access functions remain stable, even when models change and churn. 

Check out more generative AI use cases 

The benefits of adopting MaaS come down to being in control of your resources. It helps teams who lack the budget or AI skills to build, train, and serve their own models on their own terms.

Managing your infrastructure and GPUs can be costly. By becoming the private AI provider, you avoid the complexities of fragmented AI services and keep infrastructure costs under control.

Specific MaaS benefits include:

• Quicker time to value. MaaS frees up teams to build applications and solve business problems instead of managing the underlying infrastructure, leading to faster deployment and innovation.
• Efficiency and cost reduction. With a centralized AI infrastructure, your organization can work from a single source—as opposed to many different AI services. This helps avoid duplicated efforts, overspending, and disorganized resources.
• Better time management. GPU management requires skilled, trained professionals—and budget. With MaaS, your AI team can focus on responsibilities like managing and serving models, rather than time-intensive, repetitive tasks.
• Privacy and security. As your own private AI provider, you can self-host your AI models to avoid public-facing infrastructure. When your data isn’t exposed to third parties, it’s easier to protect it and maintain governance with existing security policies. 

You can use a prebuilt MaaS solution from a provider, or you can create your own. A team within your company can develop an internal MaaS solution to be distributed and operationalized. 

Creating a model service that meets your needs is important but only the 1st of many considerations when developing on your own. Other factors to think about before you get started include: 

• Data-collection processes: How will you make sure your training data is high quality? How will you protect your private data?
• Resource management: Who will be responsible for creating, building, and managing your MaaS and your GPUs?
• Reliable infrastructure: Is your infrastructure reliable enough to support a new AI model? Do you have the resources to benefit from the model once it’s created? 

Answering these questions before you get started will help build the foundation you need to succeed. 

When does MaaS make sense for your business?

Red Hat® AI is our platform of AI products built on solutions our customers already trust. Red Hat AI helps organizations:

• Adopt and innovate with AI quickly.
• Break down the complexities of delivering AI solutions.
• Deploy across the hybrid cloud.

Find out more about Red Hat AI 

Red Hat OpenShift® AI is included in Red Hat AI. It provides a flexible, cost-effective AI platform that supports MaaS in the cloud, at the edge, or on premise. 

Red Hat OpenShift AI helps organizations:

• Improve cross-team collaboration with streamlined workflows of data ingestion, model training, model serving, and observability.
• Heighten security with built-in authentication and role-based access control.
• Keep private data private when models are in air-gapped and disconnected environments.
• Reach every corner of the business—in the cloud or on premise—with flexible API gateways.
• Avoid model bias and drift with extensive model governance and AI guardrails.

Explore Red Hat OpenShift AI