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What if you could control AI like your own private cloud? 🤯 Cedric Clyburn explains how AI Models-as-a-Service simplifies deployment, supports agentic AI, and enables RAG workflows, all while ensuring privacy and governance. Learn how to scale secure, effective AI in hybrid and private environments!

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IBM Technology

Models as a Service (MaaS) is an emerging pattern for organizations that want to deploy private, sovereign AI infrastructure instead of relying on third-party public APIs. By combining an orchestration layer (Kubernetes/OpenShift), inference engines (vLLM, KServe), and an API gateway, teams can serve multiple LLMs through a single standardized endpoint with built-in rate limiting, authentication, usage tracking, and observability via tools like Prometheus, Grafana, and Jaeger. This approach gives organizations full control over model lifecycle management, cost, data privacy (critical for healthcare and financial services), and governance — enabling RAG pipelines and agentic AI in fully air-gapped environments without depending on public cloud providers.

AI Models as a Service: Powering Agentic AI, Privacy, & RAG