Model Serving

Model Serving is the infrastructure layer that hosts trained AI models and exposes them to applications via APIs, streaming endpoints, or batch interfaces. Common serving frameworks include NVIDIA Triton Inference Server (multi-model, multi-framework production serving), TorchServe (PyTorch-native), TensorFlow Serving, Ray Serve (Python-native), KServe (Kubernetes-native), BentoML (developer-friendly), and LLM-specific options vLLM, TGI (Text Generation Inference from Hugging Face), and Modal. Serving systems handle request routing, batching, model loading, autoscaling, monitoring, and observability. Cloud managed services include AWS SageMaker, Azure ML Online Endpoints, Google Vertex AI Endpoints, and AI-specific platforms like Replicate, Together AI, and Fireworks. Choosing the right serving stack often determines production economics. AI governance, AI compliance, and AI risk management programs treat model serving as a control point where policy enforcement, audit logging, and AI risk monitoring concentrate — supporting responsible AI delivery across enterprise AI portfolios at scale.

Centralpoint Sits Above the Model Serving Layer: Oxcyon's Centralpoint AI Governance Platform routes through any serving backend — your Triton-hosted Llama, vLLM-served Mistral, OpenAI, Gemini, or embedded options. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via a single JavaScript line.


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