Enterprise AI economics: cost the whole workflow, not just per-token pricing
kashifmanzoor · x · 2026-11-16
The author argues that inference economics must become a first-class part of enterprise AI architecture:
- Prototypes are cheap and fast, but costs spiral once real users arrive, with ungoverned context lengths and rising request volumes;
- Modern enterprise AI is no longer a single model call: RAG pipelines, multiple model calls, and agents embedded in workflows all stack costs;
- Instead of per-token pricing or infrastructure token allowances, assess the end-to-end cost of AI-completed business workflows or tasks;
- Conclusion: build AI economics into the architecture from day one to avoid repeating decades of cloud economics mistakes.
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