Cut Enterprise AI Costs via Data and Evals
jefrankle · x · 2026-07-17
The post emphasizes that to reduce AI costs, enterprises must look beyond simply "switching to smaller models, using open-source, or doing RL." The real differentiators are data, environments, and evaluations.
The author argues that data accounts for 90% of the journey. To lower agent costs, teams must first build real-world environments and establish evals to measure trade-offs before even discussing training; training itself is merely the final 10%.
The core takeaway isn't about a new model, but rather the deployment methodology Databricks repeatedly stresses to clients: cost optimization becomes truly manageable only when data, environments, and evals are fully prepared.
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