Enterprises making early AI commitments on AWS are doing so without benchmarks, without understanding the pricing architecture, and without the contractual protections that will matter when AI spend scales. This paper ensures you negotiate from an informed position.
How Bedrock, SageMaker, Textract, and AI infrastructure are priced — per-token, per-hour, per-page, and consumption-based models decoded with negotiation sensitivity for each.
On-demand vs. Provisioned Throughput vs. Batch Inference — break-even analysis, model version risk, and the economics of each consumption mode.
AI-specific gates for determining when to commit vs. stay on-demand — workload maturity, model stability, utilisation thresholds, and batch segmentation.
How to embed AI-specific discount tiers, consumption credits, and infrastructure tier protection within your Enterprise Discount Program.
Model deprecation migration, price escalation caps, cross-model flexibility, throughput SLAs, and commitment exit provisions — the AI-specific terms your agreement needs.
From AI consumption baselining and cross-provider benchmarking through workload segmentation, EDP integration, contractual protections, and AI FinOps implementation.
"The biggest AI cost mistake isn't paying on-demand rates — it's committing to Provisioned Throughput for a model you'll switch away from in four months. In AI, flexibility has more value than discount depth."Redress Compliance — Cloud & AI Practice