Architecture Analysis
Why LLMs Can't Replace Document Extraction APIs
ChatGPT can read a document. It can't build a production pipeline.
| Dimension | When to use LLMs directly | DocDigitizer |
|---|---|---|
| Scanned documents | Vision models struggle below 200 DPI. No OCR. | Dedicated OCR. Handles 75 DPI+, handwriting. |
| Multi-page documents | Context window limits. Truncation loses data. | No page limit. Cross-page context preservation. |
| Structured output | Fields hallucinated/renamed/omitted. | Schema-validated. No hallucinations. |
| Multi-document packets | Treats as one. Boundaries not detected. | Auto boundary detection, per-doc extraction. |
| Rate limits | Custom retry infrastructure needed. | Built-in retry, 99.9% SLA. |
| Cost predictability | 40-page contract = 80K tokens. Costs spike. | Fixed credit per document. Predictable. |
| Latency | 30–90 seconds for complex docs. | 2–8 seconds synchronous. |
| GDPR | US LLM provider. Requires legal review. | EU processing by default. No retention. |
| Confidence scores | No field-level confidence. | Per-field confidence scores. |
| Engineering effort | 190–320 hours (€19K–€32K) | 9–10 hours + API credits |
Honest Assessment
When to use LLMs directly
Need to summarise/analyse, not extract structured fields
Building one-off internal tool, not production pipeline
Volume low (<100/month) and accuracy flexible
Prototyping to validate extraction is worth building
When to choose DocDigitizer
Need structured JSON fields in production
Volume, reliability, cost predictability non-negotiable
Documents include scans, low-quality, multi-page
GDPR compliance requires EU processing
Need audit trails and per-field confidence
The developer experience
DocDigitizer
from docdigitizer import DocDigitizer
client = DocDigitizer("dd-YOUR_KEY")
result = client.extract("invoice.pdf")
# 3 lines. Includes OCR, validation, audit trail.Alternative
LLM DIY: PDF encoding + schema validators + retry decorators + rate limits + token counting + cost monitoring + audit logging + multi-page chunking. 200+ lines, 190–320 hours.
Can I use DocDigitizer with my LLM agent?
Yes, recommended. DocDigitizer extracts clean JSON, agent receives structured data. Reduces tokens 10x.
Aren't vision models good enough now?
Reliable on clean single-page docs. Failures on scans, rotated pages, multi-column layouts, tables = 15–40% of enterprise volumes.
Try it before you decide.
50 free credits. No signup required for the first extraction.
Questions? → Talk to Us