Nanonets is one of the better‑known AI document processing platforms, and it shows up alongside PaperAI on most shortlists. Both use AI for extraction. Both have solid UIs. But they sit on different architectures and land in different sweet spots, and the fit for your team depends on which trade‑offs matter.
What both platforms have in common
- AI‑based extraction that does not depend on brittle templates.
- A workflow model with review, approve, and export.
- API access for programmatic use.
- Multi‑page, multi‑document PDF handling.
- Confidence scoring and a review queue.
If you are comparing PaperAI and Nanonets at a high level, you are picking between two modern products. The differences are in the details.
Where PaperAI differs
Multiple AI models, not a single proprietary model
Nanonets uses its own in‑house model stack. PaperAI lets you pick from 5 AI models via Azure OpenAI — Standard tier (GPT‑4o Mini, Mistral Document AI, GPT‑5.4 Mini) and Premium tier (GPT‑4o, GPT‑5 Chat). Match the model to the document: cheap and fast for clean PDFs, premium for handwriting or complex tables.
Why this matters: foundation‑model capability moves fast. A platform that lets you swap models lets you benefit from every upgrade without waiting for a vendor release.
Flows: reusable configurations, not model‑by‑model training
Nanonets often involves a model‑training step per document type where you label a set of samples. PaperAI's Flows are zero‑training: describe the fields you want, pick a model, save. Ready to process on document #1.
This is the difference between platform time‑to‑value measured in days vs. hours. See extraction flows and smart flows vs manual templates.
Review UX and auto‑approve
Both platforms have a review queue. PaperAI's is keyboard‑first with side‑by‑side field correction, and auto‑approve is configurable per field with a confidence threshold — not just per document. For teams processing thousands of documents, those small UX details add up.
Transparent credit pricing
PaperAI publishes credit pricing with a free tier (100 credits). Nanonets pricing has historically required a conversation with sales for anything beyond the starter tier.
Full Markdown output for knowledge systems
PaperAI outputs clean Markdown in addition to structured data. If your downstream is a knowledge base, RAG pipeline, or LLM workflow, Markdown is the right format — not a blob of text or a table dump. See PDF to Markdown for teams.
Where Nanonets might be the better fit
- If you want a vendor‑managed model tuned for your exact document type, and you have time to train it.
- If your workflow needs specific Nanonets integrations that are not yet on PaperAI's list.
- If your team has already invested in Nanonets training data.
Feature comparison
| Capability | Nanonets | PaperAI | |---|---|---| | AI model | Proprietary | 5 models via Azure OpenAI | | Multi‑model per document type | No | Yes | | Template‑free | Yes | Yes | | Per‑type training required | Often | No | | Markdown output | Limited | Yes | | Side‑by‑side review UX | Yes | Yes, keyboard‑first | | Auto‑approve per field | Limited | Yes | | Free plan | Limited | 100 credits | | Pricing | Quote‑based | Public credit pricing | | API | Yes | Yes (Scale+) |
When each one wins
Pick Nanonets when:
- You have an existing labeled dataset and want a tuned model.
- Your procurement team is comfortable with quote‑based pricing.
- You need specific integrations in the Nanonets catalog.
Pick PaperAI when:
- You want to avoid per‑type model training and ship faster.
- You want the flexibility to pick the AI model per use case.
- You want Markdown output for LLM pipelines.
- You want to evaluate on a free plan before any sales call.
Tip
The real test: take 25 of your hardest documents — the ones your current OCR gets wrong — and run them on both platforms. Whichever has fewer field‑level corrections is the better fit for your document mix.
The short version
Both products work. PaperAI wins on time‑to‑first‑result, multi‑model flexibility, and Markdown output for downstream LLM pipelines. Nanonets wins when you have the time and labeled data to train a tuned model per document type and want a single managed model stack.
Try PaperAI free with 100 credits. No sales call.