A model deployment platform is software that puts a company's trained AI models, and now its AI agents, on servers, keeps them answering requests reliably and watches how they behave. It is part of what engineers call MLOps or LLMOps: the operations work of running machine-learning models and large language models in daily use. Many platforms of this kind run on the customer's own cloud account, so the company's data and models stay inside its own systems.
A trained model is a large file. To use it, someone must load it onto a server with the right GPU, give it a web address and send it users' requests. This is called deploying the model, and answering each request is called serving. The platform does these steps with a few clicks or one command. It packs the model into containers, packaged copies of the software, and has Kubernetes start them on servers.
The harder work comes after the model is live. Requests rise and fall during the day. So the platform adds GPU servers when requests start to queue, and removes servers that sit idle, because idle GPUs still cost money. It records how long each answer takes, how many requests fail and how much each team spends. When engineers release a new version, the platform can send it a small share of requests first. If something breaks, it sends everyone back to the old version.
Say a health insurer in Kochi uses a language model to read the hospital bills attached to claims. On a normal day it handles 3,000 bills on two GPU servers. After floods in Kerala, claims jump to 25,000 a day. The platform sees answers slowing down and starts ten more servers within minutes. A week later, it switches the extra servers off again. When the team tests an improved model, the platform sends it one bill in twenty, 5% of the traffic, and compares the results before switching fully.
Before a model goes live, the team usually checks it with LLM evaluation. While it runs, LLM guardrails check what goes in and what comes out, and data masking hides patients' personal details. Apps reach the model through an LLM gateway.
How is this different from a distributed computing framework? That framework splits one job across many machines, mostly for training. A deployment platform looks after a model once it is trained, during the months and years in which it answers requests.