Nanonets Alternative — Simpler, Faster Invoice OCR

Why teams are switching from Nanonets

Nanonets pioneered the idea of training custom AI models on your own documents, which works well for highly specialized form types. But for invoice processing — a domain with well-established field conventions — custom model training introduces unnecessary complexity and delay. Most teams using Nanonets for invoices spend days labeling training data before they can process a single document, then repeat the process whenever a new vendor layout appears. Invoice OCR takes the opposite approach: our model is pre-trained on millions of invoices from hundreds of industries and adapts to new layouts automatically. There is no labeling step, no training time, and no model version management. You call the API, you get structured data. For teams that want a simple, reliable invoice extraction pipeline without a machine-learning ops overhead, Invoice OCR removes every barrier between your documents and your structured data.

Invoice OCR vs Nanonets: Feature Comparison

Pricing Comparison

How to Migrate from Nanonets

1. 1

   Identify your current Nanonets workflows

   List the document types you process with Nanonets — standard invoices, receipts, purchase orders. This helps you confirm Invoice OCR covers each type out of the box.

2. 2

   Create your Invoice OCR account

   Sign up at invoiceocr.io. No model training required — you will have API access within minutes.

3. 3

   Test with your existing invoice corpus

   Run 20–50 of your current invoice samples through the Invoice OCR API. Compare the output field structure against your Nanonets model output to confirm all required fields are captured.

4. 4

   Replace Nanonets API calls in your code

   Update your integration to call the Invoice OCR REST endpoint. Our response schema returns all standard invoice fields (vendor, amount, date, line items, tax, totals) in a consistent JSON format.

Real-World Migration Story