Enterprise AI inference and compute platforms cover a range of specialized silicon and cloud accelerators used to run large models at scale — from wafer‑scale and on‑prem appliances (Cerebras) to cloud‑native chips and instances (AWS Trainium and Inferentia, NVIDIA GPUs and DPUs). This topic evaluates tradeoffs in raw throughput, latency, model compatibility, software ecosystem, and total cost of ownership for production inference and mixed inference/training workloads. Relevance (2026‑01‑16): model sizes, real‑time application needs, and energy constraints continue to push firms toward hardware that maximizes inference price‑performance and predictable operational costs. Specialized accelerators and optimized runtimes (e.g., vendor SDKs and inference servers) narrow gaps in throughput or latency, while cloud offerings simplify procurement and autoscaling. Cost and observability tools increasingly matter for choosing platforms and deployment patterns. Key tools and integrations: cloud platform MCP servers and cost telemetry are essential to compare real spend and utilization across vendors. Examples include an AWS Cost Explorer MCP server that surfaces Cost Explorer and Amazon Bedrock invocation data to agents, an AWS MCP server for programmatic AWS resource operations (S3/DynamoDB), an Azure MCP Hub for discovering MCP servers on Azure, and a Google Cloud Run MCP server for deploying test workloads. These integrations let benchmarking pipelines capture invocation counts, instance hours, and storage/network costs to compute end‑to‑end inference cost per request. Practical considerations: benchmark across realistic models and batch sizes; include model quantization and runtime optimizations; account for reservation/spot pricing, data transfer, and operational automation. The best choice depends on workload shape (latency‑sensitive vs batch), existing cloud commitments, and integration needs for cost and deployment telemetry.