AI Inference Platforms provide managed infrastructure for deploying and serving trained machine learning models in production at scale. These platforms handle GPU orchestration, autoscaling, and optimization techniques such as dynamic batching, quantization, and caching to deliver low-latency API endpoints for generative AI and traditional ML models. They typically span serverless endpoints for variable workloads, dedicated GPU instances for sustained traffic, and edge inference for latency-sensitive applications. Most platforms now expose OpenAI-compatible APIs and support multiple model formats including ONNX, TensorRT, and native framework checkpoints. The category has evolved rapidly from serving primarily computer vision and NLP classifiers to hosting large language models and multimodal foundation models, with pricing shifting from per-request metering to granular per-token and per-GPU-hour billing that reflects the economics of transformer inference.