AI Infrastructure Decoded: What is MaaS?

When deploying AI models in private environments, enterprises often encounter confusion around the new concepts introduced by AI technologies:

• What is AI infrastructure, and what does AI infrastructure exactly include beyond GPUs?
• What is an inference engine? Is it the same as an AI training framework?
• What are the differences between ModelOps, MLOps, and LLMOps?
• What is MaaS, and how does it work in AI deployment?
• What is an AI Agent?
• ……

To help enterprises accelerate AI model deployments and achieve sustainable operations, our blog series “AI Infrastructure Decoded” aims to clarify some of the most common concepts IT teams may encounter during AI adoption and deployment. In this article, we will take a closer look at Model as a Service (MaaS).

What is MaaS?

One-Sentence Definition:

MaaS (Model as a Service) is a cloud- or platform-based delivery model in which AI models are provided as on-demand services through standardized APIs, allowing users to consume model capabilities without worrying about how the models are built, deployed, or scaled.

Traditionally, during AI model deployments, tasks such as environment setup, model development or downloading, model deployment, training and fine-tuning, as well as resource monitoring and optimization are all performed manually by operations teams. This approach is time-consuming and labor-intensive, resulting in slow model delivery and increasingly complex management as the number of models grows.

As a result, many cloud service providers now offer MaaS (Model as a Service) — sometimes also referred to as AI platforms or inference platforms — to deliver “out-of-the-box” AI model capabilities for enterprises. The main goals of MaaS include simplifying model deployment, management, and fine-tuning, while improving inference efficiency and overall resource utilization.

What Capabilities Does MaaS Typically Provide?

• Model repository: A centralized library of callable pre-trained models, including Large Language Models (LLM), NLP, CV, and speech models.
• Compute resource management: Unified management of heterogeneous compute resources (CPU & GPU) across different locations.
• Inference services: Pre-integrated inference engines and frameworks for running models (such as vLLM, Llama.cpp, and SGLang).
• API / SDK interfaces: Support for HTTP, gRPC, and other invocation methods.
• Model management: Centralized operations and lifecycle management for multiple models.
• Observability: Monitoring of resource utilization and inference performance metrics (e.g., TTFT, TPOT, ITL).
• Metering and billing: Tracking invocation counts, token usage, and related consumption metrics.
• Security and access control: Access restriction mechanisms and data privacy protection.

Learn more about Arcfra’s AI infrastructure solutions from our website and blog post:

Powering AI Workloads with Arcfra: A Unified Full-Stack Platform for the AI Era