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. 

View a MaaS demo