Stop Overpaying for Intelligence | DevDay 2026
This session outlines advanced strategies for optimizing AI operational costs, shifting the focus from merely minimizing input/output tokens to maximizing 'cost per task.' Key recommendations include adopting 'cost per task' as the primary metric, systematically right-sizing models using Pareto frontier analysis, and leveraging advanced API features like Prompt Caching, Programmatic Tool Calling, and Batch APIs to drastically reduce overhead while maintaining quality.
Key takeaways
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Shift Metric from Tokens to Tasks
2:00
The most critical metric for cost optimization is 'cost per task,' not 'cost per token.' A cheaper model may fail to complete the task, requiring human intervention, which drastically increases the true cost (time + tokens).
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Model Selection via Pareto Frontier
5:30
To right-size a model, define the task and the required accuracy threshold. Plotting the Pareto curve (Accuracy vs. Cost) helps identify the optimal model configuration and harness that meets the required accuracy at the lowest possible cost.
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Advanced Cost Levers Beyond the Model
9:10
Beyond model choice, developers can optimize costs using four levers: Prompt Caching (reusing pre-processing of shared instructions), Programmatic Tool Calling (offloading data processing to code rather than passing all results to the LLM), Reasoning Effort (starting with lower levels for routing tasks), and Batch/Flex API processing (for non-urgent, high-volume tasks).
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Optimizing Prompt Cache Hit Rate
13:40
To maximize prompt caching efficiency, keep the beginning of the prompt consistent. For greater control, utilize explicit breakpoints or 'explicit only mode' when using newer model families (e.g., GPT-4o/GPT-5.6 families) to define precise cache boundaries.