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3 items in AI Infrastructure
Most enterprises should run LLM workloads through a managed API until sustained volume or a hard data boundary forces a change. When it does, the decision splits three ways: which GPUs to rent or buy, which hosting tier to operate on, and whether any inference belongs at the edge. This comparison works through all three with current, primary-sourced numbers.
Most enterprises should exhaust managed APIs — including 50%-discounted batch endpoints — before running their own GPUs. When volume, data control, or open-weight models force self-hosting, the stack is mature: vLLM-class servers with continuous batching, queue-depth autoscaling instead of GPU-utilization triggers, speculative decoding for latency, and serverless GPUs for spiky workloads. This guide maps the whole decision, tier by tier.
AI compute has become a matter of national security. Export controls, CHIPS Act investments, and sovereign cloud requirements are reshaping enterprise AI strategy.