§ Comparison
Architecture beats a feature checklist.
Most IDP comparisons line up the same twelve checkboxes. We would rather compare what actually determines your cost and your risk: where the compute and data live, how a correction gets learned, and who has to read every document. Here is how STELE's architecture differs from the category of legacy, hyperscaler-hosted IDP platforms.
What “legacy hyperscaler-hosted IDP” means
When people evaluate document-extraction vendors, they usually end up looking at platforms like Docsumo, Nanonets, Rossum, or ABBYY. We name them only as recognizable examples of a category — we have not audited their code or their current pricing, and nothing on this page is a claim about any one of those companies’ specific internals or dollar figures. What we can describe is the shape most vendors in this category share, because it is a pattern of how the category is typically built: a managed extraction service commonly hosted on a hyperscaler, often billed in part on data volume or API calls, with a review queue in front of some or all documents, running a closed pipeline.
STELE was built to a different architecture. The rest of this page compares the two on structure, not adjectives — with links to real pricing and the full security detail so you can verify every row yourself.
Side by side
| Dimension | Legacy hyperscaler-hosted IDP | STELE |
|---|---|---|
| Hosting & egress | Commonly hosted on a hyperscaler with metered egress — moving your own extracted data or source documents out of the platform can carry a data-transfer charge. | Runs on Cloudflare R2, which is zero-egress by design. Reading your own data back out never carries a data-movement fee. |
| Correction learning | Corrections are typically logged and folded into a future batch retraining cycle — often weeks or a release away. | Corrections are written to the audit ledger immediately and become few-shot examples the very next extraction of that document type, for that tenant, can draw on. |
| Human-in-the-loop cost model | Many platforms route all documents, or a fixed statistical sample, through a review queue regardless of how confident the extraction was. | Extractions carry a confidence score and pass a validation gate; only the ones that fail or land below threshold are routed to review. High-confidence documents complete without a human touch. |
| Source availability | Closed-source. You are trusting the vendor’s description of the pipeline. | Apache-2.0 licensed. The codebase is not publicly published yet, but we’ll arrange source access for your security team on request — see /security. |
| Setup & integration overhead | Often professional-services-led onboarding, with per-layout template configuration before the platform will extract a new document type. | Self-serve signup; documents are classified from their content rather than matched against a pre-built template per layout. |
Why this is the comparison that matters
Feature lists converge over time — most vendors eventually ship a confidence score and a review screen. Architecture does not converge the same way: whether your documents cross a metered boundary to leave storage, whether a correction improves the very next extraction or waits for a retraining cycle, and whether you can read the code touching your data are structural properties of how a platform was built, not settings a vendor can toggle after the fact.
See the real numbers
This page intentionally does not quote a competitor’s dollar figures — we have not verified their current pricing and would rather point you at sourced numbers than invented ones. For STELE’s own pricing, see /pricing. For the architecture claims above in full detail — the audit ledger schema, the ownership checks, the rate limits — see /security.