Make the infrastructure answer to the workload.
Move from a bounded task through responsibility, economics, evaluation, reliability, data boundaries, and a credible exit.
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Workload first
Define the workload before you compare an AI stack
Write the task, user, input boundary, acceptable output, review step, volume, and failure response before naming a model or cloud.Responsibility path
Compare API, managed-platform, and self-hosted AI paths
Compare what the provider operates, what your team must own, and what evidence would justify taking on more infrastructure.Unit economics
Measure AI cost per useful task, not only cost per token
Connect model, orchestration, retrieval, storage, review, retry, and support costs to a completed task the venture values.Evaluation
Benchmark your workload, not a public leaderboard
Use representative authorized cases, explicit acceptance criteria, latency targets, and failure review for the work the system must perform.Reliability
Design for AI dependency failure before launch
Define timeouts, retries, degraded behavior, queues, human fallback, observability, and user communication for an unreliable dependency.Data boundary
Map data boundaries and shared responsibility
Trace what enters the workload, where it travels, what providers retain, who can access it, and what the team must delete or export.Reversibility