Comparison 2026

DocDigitizer vs Nanonets

Same results. Simpler pricing. Better multi-document handling.

Dimension	Nanonets	DocDigitizer
Architecture	Model training platform. Custom models per type.	API-first. Zero-shot extraction. No models to train.
Time to production	Days to weeks. Labelling, training, validation cycles.	Under 1 hour. API key + SDK + first call.
Model training	Yes. ~50–100 labelled samples per type.	No. Works zero-shot on 371+ types.
Multi-document packets	Limited. Single-document scope. Pre-splitting needed.	Native. Auto separation and classification.
Synchronous response	Async by default. Webhooks for completion.	Synchronous. 2–8 second response.
MCP Server	Not available	Available (Early Access)
Pricing model	Per-page + workflow seats. Expensive at scale.	Per-credit. Predictable. No seat fees.
EU data processing	US-headquartered. EU requires enterprise plan.	EU-only by default. Frankfurt.

Honest Assessment

When to choose Nanonets

•

Highly irregular documents without standard field structure

•

Large volume of labelled training samples already collected

•

No-code interface needed for non-technical teams

•

Single document type, high volume

When to choose DocDigitizer

✓

Want to extract today, not after training cycle

✓

Process multiple types without separate model per type

✓

Receive mixed-document packets

✓

Predictable per-credit pricing preferred

✓

EU data residency required without enterprise overhead

DocDigitizer

from docdigitizer import DocDigitizer
client = DocDigitizer("dd-YOUR_KEY")
result = client.extract("invoice.pdf")
# Works immediately. No training.

Alternative

Nanonets: Create workflow → Upload 50+ samples → Label fields → Train model → Validate → Iterate. 3–7 days.

Specialized extraction models + LLM-assisted understanding. Custom types via schema definition without labelling.

Yes. One credit = one document. No seat fees, no add-on charges.

50 free credits. No signup required for the first extraction.