AI infrastructure is no longer a subfield of MLOps — it is the convergence of six traditional disciplines. Here is how Terraform, Kubernetes, GPU scheduling, inference routing, SRE, and FinOps are collapsing into a single operating model.
Introduction Multimodal agents—systems that can understand and generate text, images, audio, and video—have moved from research labs to real‑world products at a breathtaking pace. Early prototypes often run on a single GPU workstation, but production workloads demand elastic scaling, high availability, and cost‑effective compute. Two technologies have emerged as the backbone of modern, cloud‑native multimodal pipelines: Serverless GPU orchestration – the ability to spin up GPU‑accelerated containers on demand without managing servers. Vector databases – persistent, low‑latency stores for high‑dimensional embeddings that power similarity search, retrieval‑augmented generation (RAG), and memory management. This article walks you through the end‑to‑end journey of taking a multimodal agent from a proof‑of‑concept notebook to a production‑grade service that can handle millions of requests per day. We’ll cover architectural patterns, concrete code snippets, cloud‑provider choices, cost‑optimization tricks, and operational best practices. ...