Cut LLM Document-Extraction Cost by 85% Without Losing Accuracy
Most production LLM extraction pipelines route every field through a frontier model, every time. It works, and for a while nobody questions the bill. Then volume grows, or someone runs the per-document math, and the real question shows up. How much of this spend is buying accuracy, and how much is just habit. In practice, most of it is habit. The majority of fields in a typical extraction job do…
The majority of expenditures in document extraction pipelines are due to unnecessary model calls. To optimize costs without sacrificing accuracy, several strategies can be implemented. These include establishing a trusted baseline using the expensive frontier model, then testing cheaper alternatives against it. Fields that do not change between runs can be skipped through field-level caching, and only the necessary fields are processed based on the document type.
Model selection varies depending on field complexity, with simple fields being extracted using lightweight models, moderate reasoning fields using mid-tier models, and complex fields requiring the frontier model. Model distillation can further reduce costs by training a smaller student model to mimic the output of the expensive frontier model for specific fields.
This iterative process allows for significant cost savings, with per-document costs dropping by 85 to 93 percent while maintaining high accuracy.
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