Nanonets works well enough when your documents are clean, digital, and consistent. But if you're processing invoices from 200+ vendors, scanned documents, or anything with handwriting, you've probably already discovered the limits. Model retraining takes weeks. Accuracy drops on layouts the system hasn't seen. And you're still paying for every extraction attempt, including the ones that come back wrong.
"Five times a week I'm talking to someone who's processing hundreds of thousands of documents through Nanonets and not happy with their solution," as one sales rep in the document processing space put it. The dissatisfaction follows a pattern: Nanonets performs well during a controlled demo, then struggles when exposed to the actual variety of documents a real business receives.
If you're in that position, here's what to consider before choosing your next tool, and what the alternatives actually look like.
Lido is the strongest Nanonets alternative for teams processing complex, variable-format documents at high volume. It extracts data from any document layout — including scanned, handwritten, and multi-table formats — without model training, retraining cycles, or per-format configuration. Teams switching from Nanonets to Lido eliminate the accuracy degradation and constant model maintenance that make Nanonets unworkable beyond clean digital PDFs.
The core issue isn't that Nanonets is bad software. It's that the model-training approach has a structural limitation: it needs to learn each document type before it can read it. When your document types are predictable and limited, this works fine. When they aren't, you end up on what one operations lead at a gas distribution company called the retraining treadmill.
His company processes 27,000 documents a month, including invoices, supplier statements, and customer POs. They migrated to Nanonets from Docparser specifically to escape template maintenance. Instead, they got a different version of the same problem. "We spend a ton of time retraining the models," he told us. Nested tables, the hardest part of their workflow, still don't extract correctly.
A government agency had a more expensive version of the same experience. They signed a $30,000 Nanonets contract expecting plug-and-play document processing.
"We paid that 30 grand and it was supposed to be plug and play, but... it's great for a quick and easy but it is absolutely one of the worst."
The agency also flagged a billing issue that appears in 7 of Nanonets' 99 G2 reviews: there's no free reprocessing. When the extraction gets it wrong on scanned or complex documents, you're charged again to try again. "That reprocessing feature allows us to take a second chance without being charged a credit, that's what really bit Nanonets."
The complaints from G2 reviews echo this. 6 reviewers cite model training and setup time: "It does take quite a long time initially for the AI model to be trained." 5 reviewers flag OCR and accuracy issues:
"OCR issues, such as incorrect mappings and trouble with blurred documents."
Others note inconsistent performance:
"A lot of variation in speed of the product, sometimes it will fly through 200 documents, and sometimes it seems to take 10 minutes for fewer than 50."
These aren't edge cases. They're the documented experience for teams with real document variety.
Before evaluating specific tools, it helps to know what actually matters for teams dealing with 50+ document formats from dozens of vendors. Not every Nanonets alternative solves the same problems.
Layout-agnostic extraction. If the replacement tool still requires you to train a model or define a template for each new document type, you're buying the same problem with a different interface. The tool should handle formats it's never seen before, on the first upload.
Scanned and handwritten document handling. This is where most tools fall apart. If your vendors send faxed copies, scanned receipts, or documents with handwritten annotations, test with those documents first, not with clean digital PDFs. The government agency mentioned above said Nanonets "bombed the demo" on their scanned documents.
No penalty for iteration. Extraction on complex documents often takes 2-3 passes to get right. You should be able to adjust your instructions and re-run without burning credits. Any tool that charges per attempt, including failed attempts, is penalizing you for their limitations, not yours.
Speed to first result. 6-12 week model training periods mean you won't know if the tool actually works on your documents until you're already committed. Look for something you can test in minutes with your actual files.
Best for: Teams processing 1,000+ documents monthly from 50+ vendors with mixed formats, including scanned and handwritten documents. No templates or model training required.
Lido takes a fundamentally different approach. Instead of training models on sample documents, you upload a document and describe what you want extracted in plain English. It works on layouts it has never seen before, including scanned, handwritten, and degraded documents, with 99.9% accuracy on scanned inputs.
The key differentiators for teams leaving Nanonets: no model training or retraining, 24-hour free reprocessing on every extraction (you only pay when the output is right), and native handling of scanned and handwritten documents with support for files over 500 pages. One CPA firm uses Lido to process 3,500 compliance audits a year across thousands of payroll formats with a single setup. A trucking company processing 360,000 pages a year replaced 6 data entry employees.
Where it's limited: Lido has fewer native out-of-the-box downstream integrations than platforms like Nanonets or ABBYY. You can connect via API, but if you need pre-built connectors to specific ERPs or accounting systems on day one, check whether your systems are supported.
Best for: Enterprises with 500+ employees and dedicated IT teams who need on-premises IDP deployment with compliance certifications and RPA integration.
ABBYY is the enterprise incumbent. Vantage offers 150+ pre-trained models in a marketplace, on-premises deployment options, and integrations with RPA tools like UiPath and Blue Prism. If you have a dedicated IT team and need compliance-heavy deployment options, ABBYY is a credible choice.
The tradeoffs: The interface requires technical knowledge to configure. G2 reviewers note "handwritten recognition could be improved" and cite lengthy support response times. Pricing starts at $29.99 for 500 pages on the starter tier, but enterprise plans are custom-quoted and typically run into five figures annually. The configuration depth that makes ABBYY useful for large organizations makes it more tool than most mid-market teams need.
Best for: Financial services teams processing fewer than 5,000 pages/month of invoices, bank statements, and insurance documents with 10-20 consistent vendor formats.
Docsumo has pre-built models for financial document types and claims 95% satisfaction on G2. Their pricing is straightforward: free up to 100 pages, then roughly $0.30 per page on the Growth plan.
The tradeoffs: "Docsumo's accuracy can waver when dealing with more complex document layouts," according to one G2 reviewer. Another noted that "because of the vast amount of variety in our invoices, Docsumo's systems can get mixed up occasionally." If your document formats are relatively consistent, say 10-20 vendor templates that rarely change, Docsumo handles that well. If you're dealing with the kind of format variety that drives teams away from Nanonets, the underlying challenge is similar.
Best for: Engineering teams of 3+ developers who already run on Google Cloud and want to build custom extraction pipelines using Google's pre-trained and custom-trainable models.
Google Document AI provides pre-trained models for common document types and lets you train custom models via Google Cloud. If your team already runs on Google Cloud and has engineers comfortable with API-based development, it offers a flexible foundation.
The tradeoffs: This is a developer tool, not a business user tool. There's no point-and-click interface for non-technical team members. You need to build the extraction pipeline yourself, manage the infrastructure, and handle errors programmatically. For teams that left Nanonets because of setup complexity, Google Document AI requires more engineering investment, not less.
Best for: AP teams processing 5,000+ invoices/month who need a dedicated invoice capture, validation, and approval routing workflow.
Rossum focuses on accounts payable: invoice capture, validation, and approval routing. Their AI is trained specifically on invoices and purchase orders, which gives them high reported accuracy on those document types.
The tradeoffs: The narrow focus means Rossum doesn't cover teams processing a wide variety of document types beyond AP. If you're extracting data from payroll documents, claims forms, BOLs, or other non-AP formats, you'll need a second tool. Pricing is enterprise-focused and not publicly listed, which typically means $20,000+ annually.
Best for: Small teams (1-5 people) with fewer than 10 consistent document formats who need simple Zapier-based automation at low volume.
Docparser is template-based extraction with integration hooks: Zapier, webhooks, and API exports. If you have 5-10 document formats that rarely change, Docparser is straightforward and affordable.
The tradeoffs: Every new document layout needs its own template. Layout changes break existing templates. There's a 30-page default cap per file (50 max) and a 20 MB file limit. One G2 reviewer said it has "fallen behind alternatives" as of 2022. For teams leaving Nanonets specifically because of model maintenance, Docparser moves backward into more rigid template maintenance. This is worth noting because the migration path from Docparser to Nanonets to still-not-satisfied is a pattern that shows up repeatedly in sales conversations. The Esprigas operations team processing 27,000 documents/month took exactly this path.
Dive deeper into specific competitors: See our detailed Nanonets vs. Lido comparison, or read our industry-specific comparisons for energy companies and government agencies.
The right alternative depends on why you're leaving Nanonets. If it's pricing, tools like Docsumo (free up to 100 pages) and ABBYY ($29.99 for 500 pages) offer more transparent entry points. If it's setup complexity, simpler tools like Docparser trade capability for ease of use. If it's the fundamental issue, model retraining, poor accuracy on scanned documents, and failure on formats the system hasn't seen, then the replacement needs to work differently, not just be a different version of the same approach.
Test any alternative with your worst documents, not your best ones. The clean digital invoice isn't the problem. The scanned, handwritten, rotated, 50-page document from the vendor you onboarded last week, that's the test that matters.
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