Diego Pérez SastreUpdated: Choose LandingAI ADE for vision-first parsing with bounding-box citations inside a code-first stack; choose anyformat for calibrated confidence, no-code workflows and EU jurisdiction. Both return visual grounding, but LandingAI documents its confidence as experimental and not a probability of correctness, where anyformat measures 99.1% calibration accuracy.
| Capability | anyformat | LandingAI |
|---|---|---|
| EU Data Sovereignty | Yes | No |
| ISO 27001 | Yes | No |
| GDPR Native | Yes | Partial |
| Zero Data Retention | Yes | Yes |
| On-Premise Deployment | Yes | Yes |
| No-Code Workflow Builder | Yes | No |
| Zero-Shot Extraction | Yes | Yes |
| Field-Level Confidence Scoring | Yes | Partial |
| Complex Table Extraction | Yes | Yes |
| Figure Detection & Explanation | Yes | Yes |
Agentic Document Extraction is LandingAI's document intelligence platform, launched around March 2025 and rebuilt in September 2025 on DPT-2, a document pre-trained transformer that treats a page as a visual system rather than a text stream. LandingAI was founded by Andrew Ng and is based in Palo Alto, California; it also sells LandingLens, a separate no-code computer vision platform for industrial inspection that shares nothing with ADE beyond the company. ADE exposes six REST APIs, Parse, Extract, Split, Classify, Section and Build Schema, with Python and TypeScript libraries, and accepts PDFs, images, Office files and spreadsheets. A single parse job is capped at 1 GB or 6,000 pages, and the hosted playground at 100 pages (LandingAI docs, 2026).
Both platforms extract zero-shot against a user-defined schema, with no labelled examples and no template training, so neither side wins this point. ADE's Extract API takes a JSON schema supporting flat, nested, array and multi-table shapes, a Build Schema API drafts one from a sample document, and parsing is layout-agnostic by design (LandingAI docs, 2026). anyformat extracts against a schema the same way across 100+ formats and pairs the model with deterministic validation rules, so a field can be constrained rather than only predicted. The operational difference is who owns the schema: ADE schemas live in code a developer deploys, while anyformat schemas are edited in the product by operations users without a release cycle.
LandingAI genuinely does visual grounding, and it is not an anyformat differentiator: every parsed chunk and every extracted field returns a page number and bounding-box coordinates, including table-cell grounding, and Extract returns a per-field confidence value from 0 to 1 in its extraction metadata (LandingAI docs, 2026). The difference is calibration. LandingAI's own guidance describes the score as a relative triage signal rather than a probability of correctness, recommends 0.95 as a starting threshold, and labels confidence an experimental feature in which tables and custom-formatted fields return null (LandingAI docs, 2026). anyformat's per-field confidence is calibrated, measured at 99.1% calibration accuracy with an Adaptive ECE of 0.009 (anyformat benchmark, 2026), and every value carries a visual citation linking it to its exact position on the page. Calibration is what makes a straight-through threshold defensible to an auditor: a relative signal cannot tell you what share of auto-approved fields will be wrong, and a calibrated one can.
ADE is an extraction API rather than an orchestration platform, and LandingAI states this plainly: its own comparison material describes ADE as developer-focused, needing Python code to build recurring extraction pipelines, and unsuited to teams that want fully no-code recurring automation (LandingAI, 2026). Confidence-based routing lives in the calling pipeline the customer writes, and the review interface that shows a value, its score and its source location is documented as guidance for what the customer should build. anyformat ships that layer as product surface: a visual workflow builder (Studio) with branching, conditions, classification, splitting and routing, plus a review queue where a corrected value flows back into the run and on to downstream systems. Setting up a production workflow with the Annie assistant takes around 5 minutes against roughly 4 hours of manual configuration (anyformat internal benchmark, 2026).
ADE bills credits at $0.01 each, with Parse at 3 credits per page, or 4 with zero data retention enabled, putting a parsed page at $0.03 and $0.04 respectively, or $30 and $40 per 1,000 pages (LandingAI docs, 2026). Extract, Split, Section and Build Schema are metered on characters rather than pages, at 1 credit per 5,000 input characters and 1 credit per 1,000 output characters, so extraction cost tracks document density instead of page count and is harder to forecast per document; Classify is 0.5 credits per page. Plans are Explore pay-as-you-go with 1,000 free credits that expire after 90 days, Team from $250 per month for 25,000 credits, and custom Enterprise (LandingAI pricing, 2026). anyformat bills credits per page per operator at 10 credits per €0.01: Parse is 25 credits per page (€0.025, published as $25 per 1,000 pages), Extract 35, Classify 10, Split 25, and Validate 25 credits per rule (anyformat pricing, 2026). At list rates a $250 Team subscription covers roughly 8,300 parsed pages a month, where the €499 Business plan covers 500,000 credits or about 20,000 parsed pages, and zero retention is an included Enterprise control rather than a per-page surcharge.
LandingAI's published figure is 99.16% on the DocVQA validation split, answering 5,286 of 5,331 questions from parsed text alone, with the methodology reproducible from a public repository (LandingAI, 2026). That is genuine benchmark transparency and better than most of the market, but it measures whether parsed output supports answering a question, not whether a multi-field schema extracts correctly across document types. anyformat scored 78.1% on a combined parse score across 1,000+ real documents spanning 30+ document types (anyformat benchmark, 2026), held roughly 99% row recovery on tables growing to 50 pages and about 2,400 rows, and extracted 94% of mixed-format complex invoices with every field and line item correct (anyformat benchmark, 2026). On figures the two are level: ADE detects figure, logo, card and barcode chunks and returns concise descriptions, and anyformat classifies visual elements in context and returns structured descriptions.
LandingAI is a US company under US jurisdiction that holds SOC 2 Type II, claims GDPR and HIPAA compliance, and does not publicly list ISO 27001 on any of its security pages (LandingAI security, 2026). Its deployment options are serious: a dedicated EU deployment of ADE on AWS Ireland with its own portal and API keys that are not interchangeable with US keys, a containerised deployment inside a customer-managed VPC on AWS, Azure or GCP, on-premise on Enterprise, and a Snowflake Native App. Zero data retention processes documents in memory with nothing stored at rest, on Team and Enterprise plans, at one additional credit per page. anyformat is EU-native and ISO 27001 certified across the whole document pipeline, with zero-retention processing and cloud, private cloud or air-gapped on-premise deployment. An EU region is data residency, not a change of jurisdiction, and for buyers under GDPR or DORA that distinction is what procurement actually asks about.
An evaluation suite and the production pipeline around extraction. Extraction quality is one variable; keeping it correct after go-live is where document projects spend most of their engineering budget.
Evaluations, available today. Every anyformat Extract and Classify workflow has a Health tab. You build a dataset with verified ground truth, either by promoting a document the workflow already processed and confirming its values in the review interface, or by uploading labelled documents with their expected JSON. Files are taggable into sub-datasets, so accuracy reads per supplier, per document type or per difficulty slice instead of as one average. An evaluation re-extracts every in-scope document against a chosen workflow version and scores it field by field, with a result-versus-expected view on each failure; runs are numbered and immutable, so the effect of a change is measured rather than assumed. Health Overview then places the dataset benchmark next to live production accuracy, confidence and through rate, and reports the gap between them (Evals). LandingAI publishes a research benchmark on a public dataset, which is a credibility signal rather than a customer-facing feature: no ground-truth dataset manager, no sub-dataset slicing, no immutable run history and no benchmark-versus-production comparison appears in ADE.
Optimizer, announced and not yet available. anyformat has announced Optimizer, a workflow that tunes itself against its own dataset using your evaluations as the target. It is on the roadmap, not in the product today (Evals).
The pipeline you do not have to build. ADE returns markdown, hierarchical JSON, chunk coordinates and schema-shaped fields, which is a strong extraction primitive. Production also needs classification and routing, schema validation, retry handling, a review interface, accuracy monitoring and re-tuning whenever a layout or an underlying model changes, and LandingAI's own guidance assigns that pipeline to the customer's Python code. anyformat ships it as product: no-code Studio workflows, per-field calibrated confidence with review routing, visual citations for audit, and the evaluation loop above.
Choose LandingAI when you have engineers who want a clean, well-documented extraction primitive and are happy to own the pipeline around it. ADE's grounding is thorough, its DocVQA methodology is published and reproducible, its DPT-2 model is purpose-built for dense visual layouts, and a 1 GB or 6,000-page parse job with a Snowflake Native App and customer-VPC deployment covers demanding technical requirements. If you are building RAG or search over documents rather than an operational back office, US jurisdiction and SOC 2 Type II satisfy your compliance framework, and a pay-as-you-go start with 1,000 free credits suits your evaluation, it is a strong fit.
Choose anyformat when confidence has to be calibrated rather than indicative, because a straight-through threshold is only defensible if the score maps to a real probability; when operations teams rather than developers should own schemas and workflows; when EU jurisdiction and ISO 27001 are procurement requirements that an EU region alone does not satisfy; or when you want workflow changes measured against a ground-truth dataset before they reach production.
Teams comparing the two usually land on the same three points: who writes the orchestration, whether the confidence number can carry an audit, and whose law governs the data.
LandingAI Agentic Document Extraction is a set of six REST APIs (Parse, Extract, Split, Classify, Section and Build Schema) that a developer calls from Python or TypeScript. anyformat is a platform: the same extraction primitives plus a no-code workflow builder, a human review queue, calibrated per-field confidence and an evaluation suite, all operable by non-developers. The practical question is whether your team wants to write and maintain the orchestration around extraction or consume it as product.
Yes. The Extract API returns a confidence value from 0 to 1 per field inside its extraction metadata, and Parse returns confidence on supported chunk types. LandingAI describes the score as a relative triage signal rather than a probability of correctness, recommends 0.95 as a starting threshold, and labels the feature experimental, with tables and custom-formatted fields returning null. anyformat's per-field confidence is calibrated, measured at 99.1% calibration accuracy with an Adaptive ECE of 0.009 (anyformat benchmark, 2026).
Yes, and this is genuine parity rather than a difference. ADE returns a page number and bounding-box coordinates for every parsed chunk and every extracted field, including grounding down to the table cell. anyformat returns the same kind of visual citation linking each value to its exact position on the page, so an evaluation should turn on what the confidence number attached to that value actually means, not on whether coordinates are returned.
ADE credits cost $0.01 each and Parse consumes 3 credits per page, or 4 with zero data retention enabled, so a parsed page is $0.03 or $0.04, which is $30 or $40 per 1,000 pages. Extract, Split and Section are metered per character rather than per page, so extraction cost follows document density. anyformat charges credits per page per operator at 10 credits per €0.01, making Parse 25 credits per page, published as $25 per 1,000 pages, with zero retention included in Enterprise rather than billed per page.
LandingAI holds SOC 2 Type II and claims GDPR and HIPAA compliance, but does not publicly list ISO 27001 on any of its security pages. It does offer a dedicated EU deployment of ADE on AWS Ireland, with a separate portal and API keys that are not interchangeable with the US ones. That is data residency rather than a change of jurisdiction: the company remains US-based and US-governed, while anyformat is EU-native and ISO 27001 certified across the document pipeline.
No. LandingAI's own comparison material describes ADE as developer-focused, needing Python code to build recurring extraction pipelines and unsuited to teams that want fully no-code recurring automation. Confidence-based routing lives in the pipeline the customer writes, and the review interface is documented as something the customer should build. anyformat ships a visual workflow builder with branching and routing, plus a review queue where a corrected value flows back into the run.
