This topic surveys the current landscape of inference infrastructure and managed inference platforms — the software, hardware and cloud services organizations use to run large language and multimodal models in production. As of January 2026, teams face tradeoffs among latency, cost, energy efficiency, regulatory controls and model compatibility, so the market has diversified into specialized chips, GPU clouds, serverless platforms and decentralized stacks. Key commercial and open offerings include: CoreWeave and Nvidia-backed clouds that provide GPU-heavy, low-latency capacity; AWS Trainium and Inferentia chips for cost‑efficient, accelerated training and inference on AWS; Baseten‑style managed inference platforms that simplify deployment, versioning and A/B testing; and Red Hat AI Inference Server for enterprise-oriented, standards‑based inference at scale. Complementary projects and vendors shape the periphery: Together AI offers serverless inference and fine‑tuning with a full-stack acceleration cloud; Rebellions.ai develops energy‑efficient inference accelerators and software for hyperscale data centers; Tensorplex Labs experiments with decentralized, blockchain‑enabled infrastructure and novel marketplace primitives; and LangChain provides the developer tooling to build, observe and deploy reliable LLM agents across these runtimes. Trends to watch include continued specialization of hardware for inference, wider adoption of serverless and managed inference to reduce operational burden, emphasis on energy and cost efficiency, and growth of marketplaces and decentralized options that trade centralized control for auditing and economic composability. For engineering and procurement teams, the choice increasingly depends on workload characteristics (throughput vs latency), governance needs, and integration with model development pipelines and observability tooling.