"How much will this cost?" is the first question every operations manager asks when evaluating document digitization. The answer depends entirely on your approach.
Here is an honest cost comparison across four approaches — with real numbers, not just feature lists.
Approach 1: Manual data entry (in-house)
The most common starting point. Someone on your team reads paper documents and types the data into a system.
Typical costs:
- Labor: $15-25/hour for data entry staff in the US (varies by region)
- Speed: 200-400 documents per day per person (depending on complexity)
- Error rate: 1-4% of keystrokes contain errors
- Hidden costs: Re-work, correction, downstream data quality issues
Example: Processing 1,000 invoices per month at 3 minutes per invoice = 50 hours of labor = $750-1,250/month in direct labor costs. Add 20-30% for error correction and QA.
Total: ~$900-1,600/month for 1,000 documents
Approach 2: Outsourced data entry (BPO)
Offshore business process outsourcing reduces labor costs but introduces latency and communication overhead.
Typical costs:
- Per-document: $0.50-3.00 per document (depending on complexity and provider)
- Turnaround: 24-72 hours typical
- Error rate: 1-2% (with double-entry verification)
- Hidden costs: Contract management, security review, communication overhead, re-work cycles
Example: 1,000 invoices/month at $1.50 per document = $1,500/month.
Total: ~$1,500-3,000/month for 1,000 documents
BPO works for steady, predictable volumes. It struggles with spikes, urgent documents, or documents requiring domain expertise.
Approach 3: Traditional OCR software
Desktop or server-based OCR engines like ABBYY FineReader, Adobe Acrobat, or open-source Tesseract.
Typical costs:
- Software license: $0-500/year (Tesseract is free; ABBYY ~$200-500)
- Per-page processing: Essentially free after license cost
- Template setup: 2-8 hours per document type (initial configuration)
- Template maintenance: Ongoing — templates break when document formats change
- QA labor: Someone still reviews OCR output for errors
Example: 1,000 invoices/month. Software cost amortized: ~$40/month. But you need 10-15 hours/month of QA labor to verify OCR output = $150-375/month. Template maintenance adds another 2-5 hours/month = $30-125/month.
Total: ~$220-540/month for 1,000 documents
Cheaper per document, but the QA labor and template maintenance often get underestimated. OCR accuracy drops significantly on handwritten text, poor scans, and documents with complex layouts.
Approach 4: AI-powered platforms
Platforms like PaperAI, AWS Textract, Google Document AI, or Azure AI Document Intelligence.
Cloud API services (Textract, Document AI, Azure)
Typical costs:
- Per-page: $1-15 per 1,000 pages (varies by feature)
- Development: 40-200+ engineering hours to build upload, processing, review, and export workflow
- Maintenance: Ongoing engineering for updates, scaling, error handling
Example: 1,000 invoices/month on AWS Textract with table extraction: ~$15/month in API costs. But building and maintaining the workflow application costs $5,000-20,000 in initial development + $500-2,000/month in maintenance.
Total: ~$515-2,015/month for 1,000 documents (amortized over 12 months)
This approach makes sense at very high volumes where the development cost is amortized across millions of documents.
PaperAI (complete platform)
PaperAI pricing is credit-based:
| Plan | Monthly cost | Credits included | Overage | |---|---|---|---| | Starter | Free | 100 credits | Top up anytime | | Pro | $19/month | 500 credits | Top up anytime | | Business | $39/month | 1,500 credits | Top up anytime | | Scale | $99/month | 5,000 credits | Top up anytime | | Enterprise | Custom | Custom | Negotiated |
Credits per document vary by the AI model used. Standard models cost 2-5 credits per page. Premium models cost 8-10 credits per page. Most teams use a mix — standard models for clean documents, premium models for complex ones.
Example: 1,000 invoices/month using a mix of models averaging 3 credits per document = 3,000 credits. The Scale plan ($99/month) includes 5,000 credits — enough for 1,600+ documents.
Total: ~$99/month for 1,000 documents
No development needed. No template configuration. Built-in review workflow, confidence scoring, auto-approve, structured extraction, and team collaboration.
Side-by-side comparison
| | Manual | BPO | OCR | Cloud API | PaperAI | |---|---|---|---|---|---| | Cost per 1,000 docs/month | $900-1,600 | $1,500-3,000 | $220-540 | $515-2,015 | $99 | | Setup time | None | 2-4 weeks | Hours-days | Weeks-months | Minutes | | Error rate | 1-4% | 1-2% | 3-10% | 1-3% | 1-3% (with review) | | Turnaround | Hours | 24-72 hrs | Minutes | Minutes | Minutes | | Structured extraction | Manual | Manual | Template-based | API-based | Built-in | | Human review | Built-in (it is the process) | Separate QA | Separate QA | Build your own | Built-in | | Scales with volume | Hire more people | Increase contract | QA bottleneck | Yes | Yes |
The real cost is not just per-document
When comparing approaches, include:
- Error correction costs — bad data entering your ERP or database creates downstream problems that are expensive to fix
- Setup and maintenance — templates break, APIs change, people leave
- Opportunity cost — time your team spends on data entry is time they are not spending on higher-value work
- Scaling cost — what happens when volume doubles?
Manual and BPO approaches scale linearly — double the volume, roughly double the cost. OCR and AI approaches scale sub-linearly because the software cost is relatively fixed.
What to evaluate
When choosing an approach:
- Under 100 documents/month: PaperAI's free Starter plan or manual entry
- 100-1,000 documents/month: PaperAI Pro or Business plan
- 1,000-10,000 documents/month: PaperAI Scale plan
- 10,000+ documents/month: PaperAI Enterprise or cloud API services (if you have engineering resources)
Try it with real documents
The best way to estimate your cost is to process your actual documents. PaperAI's free plan includes 100 credits per month — enough to test across multiple document types and see the output quality firsthand.