There are three ways to approach intelligent document processing: SaaS platforms like Nanonets and Docsumo, cloud provider APIs like Azure Document Intelligence, AWS Textract, and Google Document AI, and custom-built pipelines designed around your specific documents.
Each is genuinely right for different situations. The mistake isn’t choosing the wrong technology — it’s choosing based on what’s easiest to procure rather than what fits the actual documents you need to process.
The three approaches#
SaaS platforms (Nanonets, Docsumo, and similar) are pre-built for common document types — invoices, receipts, purchase orders, identity documents. They offer no-code or low-code setup, a web interface for training and reviewing extractions, and per-page pricing. They’re fast to start and require minimal engineering. The trade-off is limited customisation: you’re working within the platform’s extraction model, and when your documents deviate from the expected, your options are constrained.
Cloud provider APIs (Azure Document Intelligence, AWS Textract, Google Document AI) give you managed ML models over an API. They handle standard document types well out of the box, and all three offer custom model training for domain-specific documents. The distinction from SaaS platforms is that you’re working at a lower level: the API returns extracted data, but the integration, validation logic, and failure handling are yours to build. They’re a good fit if you’re already inside one of the major cloud ecosystems.
Custom pipelines are built from the ground up for your specific documents. The extraction logic — whether rules-based, OCR-augmented, or LLM-assisted — is designed around your layouts, your fields, and your edge cases. You get full control over how failures are handled, what confidence thresholds trigger human review, and what the output schema looks like. The upside is accuracy and control. The cost is time and engineering investment upfront.
The questions that determine which fits#
Before comparing features, work through these five questions. The answers usually point clearly to one category.
1. Are your documents standard types or domain-specific?
Standard types are invoices, receipts, IDs, purchase orders, tax forms — the documents that every IDP vendor has trained on. Domain-specific documents are everything else: lab reports, customs forms, freight bills of lading, environmental survey templates, proprietary financial schedules. If your documents are standard, SaaS and cloud APIs have a head start. If they’re domain-specific, you’re likely going to hit the ceiling of both quickly.
2. How much layout variation is there within each document type?
Receiving invoices from one supplier in a consistent format is very different from receiving them from 30 suppliers in 30 different layouts. High layout variation is where pre-trained models struggle most. They perform well on the median document; the outliers are where extraction quality drops.
3. What’s the downstream consequence of an extraction error?
If extracted data feeds into compliance records, financial reporting, or operational decisions, a wrong value passed silently downstream is a real problem. If it feeds into a summary dashboard that someone reviews manually, the stakes are lower. High-consequence workflows need explicit failure handling — not just a confidence score returned by an API, but a defined process for uncertain values before they move downstream.
4. What’s your monthly document volume?
Per-page pricing from SaaS platforms and cloud APIs is low at low volumes and adds up at high ones. There’s a crossover point — roughly in the tens of thousands of pages per month, depending on the platform — where a custom pipeline’s fixed infrastructure cost becomes cheaper. If you’re well below that threshold, per-page pricing is fine.
5. Do you need full control over how failures are handled?
SaaS platforms route low-confidence extractions to a human review queue within their interface. Cloud APIs return confidence scores; the routing logic is yours to build. A custom pipeline can implement exactly the failure handling your workflow requires — thresholds per field, escalation paths, audit trails. If your compliance or operations team has specific requirements for how extraction errors surface and get resolved, that level of control matters.
When SaaS is the right choice#
SaaS platforms earn their place when your document types are standard, your layouts are relatively consistent, and you need to get something working without a significant engineering investment.
If you’re processing invoices from a small supplier base, or validating receipts for expense management, or extracting data from identity documents, the pre-built models in platforms like Nanonets or Docsumo cover the common cases well. Accuracy in the 85–95% range is often acceptable for lower-stakes workflows where a human is already reviewing outputs.
The setup time is days rather than weeks. There’s no infrastructure to manage. For teams without dedicated engineering resource, that tradeoff is real.
Where SaaS breaks down: unusual document types, high layout variation, accuracy requirements above 95%, complex edge cases, and any situation where you need the extraction process itself to be auditable and configurable.
When cloud APIs are the right choice#
Cloud APIs make sense when you’re already operating inside AWS, Azure, or GCP and want to stay there — and when your document types are standard enough that the pre-built models cover your main cases.
The practical difference from SaaS platforms is that you’re integrating at the API level. That means more engineering work, but also more control over how the extraction sits within your existing systems. Azure Document Intelligence or AWS Textract slot into a pipeline you build; SaaS platforms have their own interface and workflow.
Custom model training is available on all three platforms. It works well when you have a consistent document type with enough labelled examples — typically at least 50–100 per layout variant. It becomes harder when layout variation is high or when your document types change regularly, because retraining is a project, not a quick update.
Cloud APIs are a reasonable choice if your documents are close to standard types, you have engineering capacity to build the integration and validation layer, and your accuracy requirements are met by the pre-built models.
When a custom pipeline is the right choice#
A custom pipeline is worth the upfront cost when the standard approaches genuinely can’t meet your requirements.
The signals are fairly clear:
- Your documents are domain-specific and pre-trained models don’t perform reliably on them
- Layout variation is high enough that consistent extraction requires logic tailored to your formats
- Accuracy requirements are above 95% and extraction errors have real downstream consequences
- You need explicit confidence scoring and human-in-the-loop processing built into the pipeline — not bolted on top
- Monthly volumes make per-page pricing expensive relative to a fixed infrastructure cost
- You need full visibility into failure modes: not just a confidence score, but a defined path for every uncertain extraction
A custom pipeline built schema-first — where the output structure is defined before extraction logic is written — produces results that your downstream systems can consume directly, with validation baked in. The accuracy ceiling is higher because the extraction logic is built for your documents, not the median case.
The honest cost: weeks to build and test, not days. It requires engineering investment and, for production use, ongoing maintenance as document formats evolve. For the right use case, it pays for itself in accuracy and operational reliability.
The evaluation process#
Before committing to any approach, test it properly.
Use your real documents, not vendor demo examples. Every platform performs well on clean, representative examples. The question is how it performs on your documents — including the awkward supplier whose invoice format changed last year, and the document type that accounts for 15% of your volume but doesn’t look like anything in the pre-built models.
Include the difficult cases. Deliberately include your worst layouts, scanned documents with imperfect quality, forms with handwritten annotations, and the edge cases your team currently handles manually. Those are the documents that reveal whether a platform will work for you.
Measure accuracy on fields that matter. Overall character accuracy is not a useful metric. Measure extraction accuracy on the specific fields your downstream processes depend on — invoice total, supplier VAT number, shipment date, whatever drives your workflow.
Test failure modes. What happens when a required field is missing? What happens when a value is ambiguous — a date that could be read two ways, a total that doesn’t reconcile with line items? Does the system fail loudly, routing the document for review? Or does it pass a wrong value downstream with a confidence score you didn’t catch?
Decision table#
| SaaS Platform | Cloud API | Custom Pipeline | |
|---|---|---|---|
| Standard document types | Works well out of the box | Works well out of the box | Overkill for standard types |
| Domain-specific documents | Limited; hits ceiling quickly | Custom training available, with limitations | Built for this |
| Layout variation | Low to moderate | Low to moderate | High variation handled by design |
| Setup time | Days | Days to weeks (integration required) | Weeks |
| Accuracy ceiling | 85–95% typical | 85–95% typical; higher with custom training | 95%+ achievable on specific documents |
| Per-page cost at scale | Adds up at high volume | Adds up at high volume | Fixed infrastructure cost |
| Control over failures | Platform-defined review queue | Confidence scores returned; routing is yours | Full control; human-in-the-loop by design |
| Maintenance burden | Platform-managed | Platform-managed (your integration layer) | Your team or contractor |
No single row in this table determines the answer. The weighting depends on your documents, your volume, and the cost of errors in your specific workflow. For most operations teams, the accuracy ceiling and control over failures are the deciding factors.
Starting with a diagnostic#
If you’re not sure which category fits, the most useful step before evaluating any platform is to audit your documents: which types drive the most manual effort, how much layout variation exists within each type, and where extraction errors would actually cause problems.
That work takes a day and usually makes the decision obvious.
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