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What you are probably seeing
Your monthly bill from a document intelligence API grows linearly with every page you process, making the total cost unpredictable and directly tied to workflow volume.
Alternatively, you see a flat hourly rate for a cloud instance with specific compute specs, but the math to convert that into a per-page cost requires running the machine continuously.
Why it happens
Paid APIs charge for every page processed, meaning the cost scales infinitely with volume, which becomes expensive for large static datasets.
Self-hosted infrastructure costs scale with time and resource allocation, so the per-page price drops dramatically as you process more documents on the same machine over a fixed period.
What changed recently
Step by step
1. Record the exact number of pages you typically process in a batch to establish your baseline volume.
2. Identify a paid API tier that supports your volume and note the cost per page or per operation.
3. Select a GCP instance type with sufficient CPU and memory to handle your OCR workload without timeouts.
4. Calculate the total hourly cost of that GCP instance and divide it by the maximum pages you can process per hour.
5. Compare the resulting self-hosted cost per page against the paid API rate to determine which option saves money for your specific batch size.
Ways to go further
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