This topic covers enterprise AI inference and serving platforms—how organizations deploy, scale and secure model inference across clouds, Kubernetes clusters, and purpose-built accelerators. Demand for predictable latency, cost-efficient GPU/ASIC utilization, and operational controls has driven a split between managed providers, Kubernetes-native servers, and hardware-optimized stacks. CoreWeave and similar cloud providers offer elastic access to GPUs and accelerators for high-throughput workloads; Baseten and comparable model-serving platforms focus on developer-friendly deployment, versioning and API surfaces for production models; Red Hat’s AI inference tooling provides Kubernetes-native serving and lifecycle controls suited for enterprise OpenShift environments; and AWS Trainium/Inferentia represent accelerator families that reduce inference cost and increase throughput when integrated into cloud stacks. Practical deployments also rely on integrations and deployment tooling: MCP (Model Context Protocol) servers and connectors (e.g., Pinecone, Grafbase, Cloudflare, AWS MCP adapters, Google Cloud Run handlers) enable LLMs and orchestration systems to interact with vector stores, GraphQL APIs, cloud resources and deployment targets. Security and runtime control are equally important—sandboxed execution platforms such as Daytona illustrate the need to isolate AI-generated code and protect production systems during inference pipelines or agent-driven automation. Key architectural choices for enterprises center on latency vs. cost trade-offs, hybrid or multi-cloud footprint, Kubernetes tool integrations for CI/CD and autoscaling, and compatibility with accelerator hardware. This overview helps IT and ML engineering teams compare serving approaches—managed vs. self-hosted, accelerator-backed vs. general GPU, and tightly integrated MCP-enabled stacks—so they can match performance, security, and operational requirements for 2026 production deployments.