AI inference platforms and managed inference services provide the infrastructure and software to run trained models at scale—balancing latency, cost, reliability, and governance for production applications. This topic covers cloud and on‑prem inference, serverless APIs, specialized inference hardware, model marketplaces, and emerging decentralized deployment models. As of early 2026, demand for predictable, energy‑efficient inference has driven diversification: managed hosts and marketplaces (Replicate, Baseten) offer hosted model serving and simple APIs for rapid integration; enterprise and open‑source servers (Red Hat AI Inference Server, NVIDIA’s inference stacks including Triton‑style runtimes) provide production controls, hardware acceleration, and compliance features for on‑prem or hybrid deployments. Hardware vendors and accelerator startups (Rebellions.ai, Together AI’s end‑to‑end acceleration cloud) optimize throughput and power efficiency with purpose‑built SoCs and scalable GPU fleets. Developer frameworks (LangChain) continue to standardize model interfaces and orchestration patterns, while enterprise assistants and platform models (IBM watsonx Assistant, Google Gemini) increase the variety and scale of inference workloads. Key tradeoffs are operational complexity versus ease of use: managed services reduce DevOps burden but can limit visibility and cost control; on‑prem and accelerator solutions require more integration but improve latency, data locality, and energy efficiency. Marketplaces and decentralized projects (Tensorplex Labs) introduce new distribution and monetization paths—combining governance primitives, model discovery, and cross‑node execution. Observability, quantized/multi‑precision serving, model governance, and hybrid deployment patterns are central operational considerations. For teams choosing a path, the right mix depends on latency, compliance, energy targets, and the need for extensible orchestration and model governance.