Key Takeaways
Cost optimi
AI document pipelines can get expensive fast if they are built like prototypes. This post covers practical levers to reduce cost without sacrificing reliability.
Cost drivers in document AI
OCR cost per page
Preprocessing compute
Storage and retention
Human review time
Engineering time maintaining brittle scripts
Lever 1: Reduce pages before you pay for OCR
Common wins:
Detect and drop blank pages
Split large PDFs and process only relevant sections
Use low cost heuristics to identify document type
Lever 2: Batch and parallelize intelligently
Use asynchronous Textract for large PDFs
Use Step Functions map states for safe parallelism
References:
Textract async: https://docs.aws.amazon.com/textract/latest/dg/api-async.html
Step Functions map state: https://docs.aws.amazon.com/step-functions/latest/dg/amazon-states-language-map-state.html
Lever 3: Confidence gating and selective review
Review time is often the largest hidden cost.
Implement:
Required fields and thresholds
Validation rules
Auto approve when rules pass
Lever 4: Cache and dedupe
Many workflows reprocess the same doc.
Techniques:
Hash raw content
Store OCR results keyed by hash
Avoid reruns unless the pipeline version changes
Lever 5: Use the right storage tier
Use S3 lifecycle rules
Store derived artifacts for a limited time
Reference:
Lever 6: Pick the right database model
MongoDB is useful for workflow state and extracted records, but avoid storing large binaries in the database. Keep binaries in S3 and store pointers.
References:
MongoDB GridFS: https://www.mongodb.com/docs/manual/core/gridfs/
zation is mostly about reducing waste before OCR and review.
Batch work and use Step Functions for safe parallelism.
Cache OCR outputs and dedupe reprocessing.
Store binaries in S3, store metadata and structured records in MongoDB.
zion services: https://lidvizion.ai/
FAQs
Q: Is managed OCR always more expensive? Not necessarily. Managed services often reduce engineering and ops cost. You need to compare total cost of ownership.
Q: How do we estimate cost before building? Run a pilot on representative documents, measure page counts, exception rates, and review time, then extrapolate.
Q: What is a reasonable first metric to track? Track cost per processed document and percent routed to review.
Internal reference:
Lid Vi
Q: What should we link to internally? A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links.