Inference

Inference is the runtime phase of an AI system — the stage where a trained model produces predictions, classifications, or generated content from new inputs. Where training is a one-time (or periodic) cost, inference happens every time a user interacts with the AI: every chat message, every document classified, every image scored. Inference dominates the total cost of operating most enterprise AI systems because it runs constantly while training runs occasionally. Common inference workloads include real-time chat with LLMs like GPT-4 or Claude, batch scoring of millions of records nightly, and edge inference on phones running Apple Intelligence or Google Pixel features. Frameworks supporting inference include PyTorch, TensorFlow, ONNX Runtime, vLLM, llama.cpp, and Triton Inference Server. AI governance, AI compliance, and AI risk management programs treat inference as the operational core of any responsible AI deployment, since every inference call generates an auditable event in the lifecycle of an enterprise AI system.

Centralpoint Governs Every Inference Call: Oxcyon's Centralpoint AI Governance Platform meters every inference call across OpenAI, Gemini, Llama, and on-premise embedded models. The platform is model-agnostic by design, keeps prompts and skills behind your firewall, and lets you deploy multiple chatbots to any website or portal with a single line of JavaScript.


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