STELE vs Nanonets: The Template Training Tax Nobody Prices Into the Demo

There is a very specific moment, about three months into a Nanonets deployment, where the AP manager stops smiling on the vendor's monthly call. A supplier changed their invoice format. Extraction accuracy on that vendor dropped noticeably. The Nanonets rep — a good rep, a polite one — explains that the model needs another 40–60 sample invoices to retrain against the new layout.

This is not a bug in Nanonets. It's the design. Nanonets is a template-and-training IDP platform: you build a model per document type, you feed it labeled samples, and it gets accurate on the layouts it has seen. When layouts change, accuracy decays until you retrain. The math on that ongoing labor is what usually doesn't make it into the ROI slide.

This post is the honest version of that math — Nanonets' real 2026 pricing, where the training-tax hides, where they legitimately win, and where STELE's per-tenant few-shot memory changes the shape of the problem.

The math, up front

At 10,000 invoice pages per month — the typical mid-market AP volume this category was built for — here is the all-in monthly cost per vendor. Numbers are synthesized from published pricing pages, G2 and Capterra reviews, and buyer conversations at 2026 rates.

At the volume this category actually serves, STELE is roughly 5–10x cheaper than the Nanonets Pro plan, and well over an order of magnitude cheaper than the usage-based tier for a normal invoice mix. The gap widens at every step up in volume because our marginal cost-to-serve on Cloudflare's edge is about $0.006 per page — six- tenths of a cent — and Nanonets' isn't.

Our full cost-model math is available on request: email security@steledocs.com and we'll walk you through every assumption, or share the relevant part of the source under NDA.

What each platform actually does

Nanonets is a model-training IDP platform. You pick a document type (invoices, receipts, purchase orders, ID cards, forms), upload sample documents, label the fields you want, and Nanonets trains a customer-specific extraction model. It has a solid review UI, a respectable pre-trained invoice model that gets you started without labeling, and an ecosystem of workflow blocks — validators, formatters, approval routing — you chain together per document type. G2 reviewers give it 4.7/5 across 100+ verified reviews and consistently praise ease of use and accuracy on documents that fit the trained models.

STELE is a Cloudflare-native IDP platform that solves the same problem with a different architectural bet:

• Per-tenant few-shot memory instead of per-format model training. Every correction you make in the review inbox gets embedded into your tenant's own memory bank. On the next document, we retrieve your three most-similar previously-corrected documents and inline their final JSON as few-shot examples to the model. There are no models to train, no labeled sample sets to build, no "please give us 40 more examples" emails. Accuracy compounds with usage.
• Confidence-gated human review. Every field ships with a per-field confidence signal. Documents below threshold pause in a durable Cloudflare Workflow and land in a review inbox. Everything above goes straight through to your ERP. You are reviewing the documents the system is unsure about, not every extraction.
• Vision fallback for scans. If the PDF is a scan (near-empty text extraction), we auto-fall-back to a vision-capable model with the same schemas. Crumpled shipping manifests and phone-camera photos of restaurant bills go through the same pipeline.
• Arithmetic-validated model tiering. A 3B model detects document type, an 8B model does routine extraction. If the arithmetic doesn't add up (subtotal + tax ≠ total), we escalate to a 70B model automatically. A deterministic quality gate before we spend on the expensive model — this is how we cut LLM cost about 4x versus running everything on the big model.

Every one of those is running in production.

Published pricing, side by side

Nanonets currently publishes three tiers:

• Starter — free, $200 in credits (never expire); roughly 100 invoices before you spend a dollar.
• Pro — $999/mo per model, includes 10,000 pages/month, $0.10/page overage.
• Enterprise — custom, sales call required.

There's also a usage-based rack rate — $0.02/simple, $0.10/standard, $0.30/complex block — which sounds cheap until you look at a real invoice workflow. Nanonets' own docs say a typical invoice pipeline uses 4–6 blocks per document. At $0.30 for the complex-AI blocks you're routinely $1.20–1.80 per invoice on rack rate before any overage or annotation labor.

STELE's plans:

• Free — 100 pages/month, no credit card
• Pro — $49/mo, 1,000 pages
• Team — $199/mo, 10,000 pages
• Enterprise — custom arrangements built on top of Team (SSO, dedicated support, custom connectors). No fixed public price — talk to sales@steledocs.com.

No per-model fee. No page-complexity surcharge. No expiration on included volume. You can see the plans and start today.

Where the cost hides

Three line items don't show up on the Nanonets pricing page and land on the AP team's real budget:

1. Model count. Nanonets' Pro plan is priced per model. If you process invoices, purchase orders, and receipts, that's three models at $999 each — $3,000/mo before overage. If your invoice mix is heterogeneous enough that vendors sit in visibly different layout buckets, teams sometimes build separate models per major supplier group to keep accuracy up. STELE has one plan, one price, all document types — one system that learns per-tenant regardless of document class.

2. Training and labeling labor. Nanonets' documented "quick" model setup requires 20+ labeled sample documents; a robust production model typically requires more. Reviewers on Capterra and G2 flag this repeatedly — "takes quite a long time initially for the AI model to be trained" is a direct verified-review quote. That's calendar time you don't have and internal labor that's not on any invoice. STELE starts producing extractions on document one with confidence-gated review, and the memory bank fills itself from your corrections without a labeling project.

3. Retraining when formats change. This is the tax that surprises mid-cycle. A supplier's ERP changes, the invoice template moves, accuracy on that vendor drops, and you're back on a call about labeling another batch. STELE's per-tenant few-shot memory absorbs format shifts as they happen — the moment a correction is logged, the next document with a similar layout retrieves it as an example. No retraining project, no model versioning.

4. Rejected pages. Most vendors, Nanonets included, charge for every page attempted. STELE doesn't charge for extractions that fail the parse gate — the _parseError flag rolls back the page count.

Accuracy claims, and how to actually verify them

Nanonets markets high accuracy and reviewers do report it on trained document types. The honest caveats:

• The accuracy is conditional on training. A green-field vendor with no labeled samples for their layout starts closer to base-model performance and improves as samples accumulate.
• Accuracy on documents the model has never seen — a brand-new supplier's first invoice — is not the marketing number.
• Model drift is real. Reviewers describe accuracy degrading when layouts change and requiring active retraining maintenance.

What we recommend asking for in any IDP pilot, Nanonets included:

1. Per-tenant accuracy trend over the pilot window. Any vendor can cherry-pick a good week — you want the trend line.
2. Straight-through vs. review-queue ratio. The number that predicts your AP clerk's actual workload.
3. Correction feedback loop. When you correct a field on Tuesday, does the same field on Friday's document get better? Or does the correction die in a log file?
4. Cold-start accuracy on a never-before-seen supplier. This is the real production case that marketing decks avoid.

STELE surfaces exactly this in-product: the quality dashboard reads per-document-type confidence and correction rates from the same telemetry the model calls emit, so you can watch it change as you use it rather than take a one-time demo number. Nobody else in this category exposes that number to the customer directly. We're comfortable losing deals on numbers because that's the game we chose to play.

When Nanonets is the right pick

Not sarcastic — real cases where Nanonets is the honest recommendation:

• You have a small, stable set of document types with heavy layout repetition. If 90% of your monthly volume comes from the same 12 suppliers on templates that change once a year, the training-tax amortizes across a lot of documents and Nanonets' trained-model accuracy is genuinely excellent.
• You already run Nanonets and your team knows the model-management workflow. Institutional knowledge is worth real money. If you're a year into a paid contract and your controller is fluent in the Nanonets review UI, the switching cost may outweigh 12 months of margin. Renew and re-evaluate next cycle.
• You process non-financial documents (ID cards, medical forms, shipping manifests with fixed layouts) where Nanonets has purpose- built pre-trained models we haven't shipped equivalents for yet.
• Your data must live in a specific AWS region for regulatory reasons and moving to a Cloudflare-native vendor isn't a fight you can win internally. Rare, but real in some healthcare and defense contexts.

For anyone outside those cases, at mid-market volume, you are paying a training-tax that doesn't buy you accuracy your competitors can't match a different way.

When STELE is the right pick

If any of these describe you, we should talk:

• Mid-market controller, 50–500 FTE, running QuickBooks or NetSuite, processing 5k–20k invoices/month, tired of paying per model or per training project.
• Long-tail supplier base. If your AP mix is 200+ vendors with no dominant layout cluster — the case Nanonets' template-plus- training model struggles with — per-tenant few-shot memory is structurally better suited to your volume shape.
• Freight or logistics ops team processing bills of lading and proof-of-delivery documents — often scanned, sometimes crumpled. The vision fallback matters here.
• You want a fixed monthly bill. $199/mo for 10k pages. No per-model fee, no page-complexity surcharge, no training-project SOW.
• Anyone building on Cloudflare who wants an IDP pipeline that actually runs on the same edge you do.

What to do next

1. Try Free tier — 100 pages/month, no credit card. Enough to run last month's ten weirdest invoices and see the confidence + accuracy numbers for yourself, with no training project.
2. Or apply to our design partner program — an extended Team-plan trial and hands-on onboarding, no labeling homework beforehand.
3. Have a supplier mix or document type you'd like benchmarked against Nanonets or another IDP vendor? Email sales@steledocs.com and we'll process a batch and publish the side-by-side numbers.

We are not the IDP for every buyer. We are the IDP for the buyer who is done paying a training-tax on layouts that change every quarter, wants to see the numbers instead of the marketing decks, and believes accuracy should compound with usage instead of decaying with model drift.

If that's you, come look at what we built.

Have a supplier mix or document type you'd like benchmarked against Nanonets or another IDP vendor? Reply to sales@steledocs.com and we'll process a batch and publish the side-by-side numbers.