Best Landing AI Alternatives for Document Extraction in 2026

TL;DR

• Landing AI sells Agentic Document Extraction (ADE), an API that parses documents into Markdown with visual grounding and then extracts fields against a JSON Schema using LLM reasoning. It prices by the page and by the character, and it is a strong parser for messy, visual documents.
• Teams look elsewhere when they need extracted values checked before they reach a system of record, when they want to decide for themselves when extraction behaviour changes, when page-and-character pricing makes the bill hard to forecast, or when they need a hosting region Landing AI does not offer.
• Sensible leads this list for teams that want to own their extraction logic: layout methods and LLM methods chosen field by field in one versioned configuration, validation rules that flag bad values for review, per-document pricing, and managed services for teams that want Sensible to build the configurations.
• The five tools fall into four groups: hybrid extraction with validation, agentic parsing APIs, a parsing toolkit with extraction on top, and a hyperscaler service.

Where Landing AI Fits, and Why Teams Compare It

Andrew Ng founded Landing AI in 2017 to sell computer vision to manufacturers, and since 2024 the company has moved its focus to documents. ADE launched in March 2025, and its second API generation, built on the DPT-3 parsing models, went generally available in July 2026. Landing AI's own comparison guides describe the best fit as "AI and engineering teams building LLM workflows over variable corpora," and for those teams it is a reasonable choice.

The reasons to compare it with other tools are specific, and most of them are written down in Landing AI's own documentation: how its schemas handle missing values, who decides when model behaviour changes, how the price moves with page density, and where the data can be hosted. This guide covers each one, then compares five alternatives and says which kind of team each suits.

What Is Agentic Document Extraction, and Where Does Landing AI Fall Short?

Agentic document extraction is Landing AI's product name for a pipeline in which a vision model parses the page and an LLM extracts the fields. Other vendors now use the phrase for their own products, with different meanings: Reducto uses it for a loop that checks and re-extracts its own output, and LlamaIndex brands its higher parsing tiers "Agentic."

In ADE the pipeline is three API calls. Parse converts a PDF, image, Office file or spreadsheet into reading-order Markdown, with HTML tables and a bounding box for each block. Extract takes that Markdown plus a JSON Schema and returns the fields, each one pointing back to a character range in the Markdown. Ground, which is free, maps those ranges onto page coordinates. Async jobs accept documents up to 6,000 pages. SOC 2 Type II, EU hosting and a HIPAA BAA with zero data retention are available from the $250-a-month Team plan.

Pricing is credit-based at $0.01 a credit. Parsing with DPT-3 Pro costs 1 credit a page plus 0.5 credits per 1,000 output characters, and Extract charges by input and output characters. Asynchronous requests on the standard tier cost half; synchronous calls and the Playground always run at the full priority rate. Landing AI calibrates a typical business page at about 1.5 credits to parse and 1.5 to extract on the standard tier, so roughly 3 cents a page async and 6 cents synchronous, with dense pages costing more.

The gaps teams run into are documented on Landing AI's own pages.

The first is validation. ADE's schema converter removes the keywords that would constrain a value, including pattern, minimum, maximum, minLength and oneOf, and it ignores the required keyword. For a missing number or enum field the docs say "the model returns an invented value of the field's type instead, such as -1 or one of the enum options" unless the schema allows null, and a missing boolean comes back false. The docs describe no business-rule or cross-field checks after extraction.

The second is who controls change. Extract is "powered by LLM reasoning," and the only determinism claim on Landing AI's site covers DPT-3 Verity, a preview transcription model. Snapshots can be pinned, but fixes still reach a pinned snapshot, and a superseded pin is resolved to the current snapshot without an error. The changelog entry for October 8, 2026 reads: "Every plan tier moved," from older DPT-2 and Extract versions to new ones.

The third is review. The Extract response has no per-field confidence score, and DPT-3 Pro, the default parse model, returns none. There is no hosted review queue: Landing AI's own guide says "the confidence-based routing logic lives in the calling pipeline." Classify and Split exist only in the first-generation API, marked "Do not use this feature in production environments."

The fourth is cost and hosting. Price per page moves with the amount of text on the page and doubles on synchronous calls, which makes a monthly bill harder to forecast from document counts alone. Hosting is AWS US East (Ohio) or AWS EU (Ireland), with no UK or Australian region, and invoices come from a US entity without VAT. Without zero data retention, which the pay-as-you-go Explore plan cannot turn on, Landing AI's security page says customer "data may be used to provide and improve the services." Turning zero data retention on disables the Ground API.

Different Alternatives Solve Different Parts of the Problem

The five tools below do different jobs. A team that needs validated values in a loan file and a team building retrieval over research PDFs can both leave Landing AI and reasonably land on different tools.

Several tools sit outside the list on purpose. Amazon Textract and Google Document AI follow the same pattern as Azure inside their own clouds. Mistral OCR and Unstructured are low-cost parsers that return text, Markdown or partitioned elements and leave field extraction and checks to the team. Enterprise IDP suites such as ABBYY and Hyperscience sell to IT and operations buyers on annual contracts, which is a different purchase.

The 5 Best Landing AI Alternatives for Document Extraction

1. Sensible: Best for Teams That Need Validated, Versioned Extraction

Best for: Product, operations and engineering teams putting extracted data into a system of record, such as a loan decision, an underwriting file or a claims queue, who want to choose the method behind each field and see failed checks before the data lands.

Sensible is a document extraction platform built on hybrid extraction. Extraction logic is written in SenseML, a JSON configuration in which each field uses either a layout method (rows, regions, tables, checkboxes, repeating sections) or an LLM method, and a field can fall back from one to the other. Where a document holds its structure, a layout method reads the value from the same anchor on each run. A field typed as a number returns null when the value on the page is not a number, so a missing amount shows up as missing rather than as -1.

Configurations are versioned and published to development or production, so a fix for one issuer's layout ships when the team publishes it. Through managed services, Sensible's solutions engineers write and maintain the configurations, onboard new formats and manage model and LLM updates, working with the customer in a shared Slack channel.

Key Functionalities

• Method per field. Layout and LLM methods in one configuration, with fallbacks between them, and the LLM provider (OpenAI, Anthropic or Google) chosen per method.
• Validation rules. JsonLogic checks against the extracted values, such as required fields, formats, sums and cross-field math, with error and warning severities. A failed check flags the extraction for the human review tab, where a reviewer sees the failed rule and the source, fixes the value, and approves or rejects it.
• Confidence signals on LLM fields. Query Group fields return a categorical signal such as answer_not_found, and strict mode returns null for an answer the model judges incorrect.
• Traceability and output. The editor highlights the source text behind a field, and the API can return each field's page and bounding polygon. Output is typed JSON, Excel or CSV, through a REST API, Python and Node SDKs, webhooks, email intake, Zapier or an MCP server. Portfolios split a multi-document file and extract each document with its own configuration.

Pros:

• Extraction logic lives in a configuration the team can read, diff and version, and behaviour changes when the team publishes a new version
• Validation and a review queue are part of the platform on every plan
• Per-document pricing, where one extraction result counts as one document whatever its page count
• Sensible's own solutions engineers can build and maintain the configurations

Cons:

• Writing and tuning SenseML is engineering-shaped work, which is what managed services is for
• On short, simple documents at high volume, per-page parsers cost less per document; Sensible offers per-page pricing for high-volume single-page documents on request
• Routing to downstream systems happens through webhooks, your own code or Zapier rather than a catalog of native ERP connectors

Pricing: Per document. Growth is $499 a month, or $449 billed annually, with 750 documents and $0.57 per document in overage. Scale is $1,499 a month, or $1,349 billed annually, with 3,200 documents and $0.53 per document in overage. Enterprise is custom from 10,000 documents a month. Every paid plan starts with a 14-day free trial, no credit card required. SOC 2 Type II on all plans, HIPAA with a signed BAA and custom data retention on Enterprise, and custom region and data-residency deployments on Enterprise.

Verdict: The strongest fit for a team leaving Landing AI because extracted values need checking before they reach a decision, or because it wants to control when extraction behaviour changes. Teams that mainly need Markdown for a retrieval pipeline will find Reducto or LlamaExtract closer to that job.

2. Reducto: Best for AI Teams That Need Parsing Plus Form Write-Back

Best for: AI and engineering teams building retrieval or agent workflows over complex documents who also need to split packets, classify them, or write values back into PDFs and Word files.

Reducto describes itself as "the agentic document platform for parsing, extraction, splitting, classification, and editing." Extract returns typed JSON against a schema with citations and bounding boxes. Deep Extract, launched in April 2026, runs a loop that extracts, checks the result against the source and extracts again; Reducto's launch post reports 99 to 100% field accuracy on key documents. Studio is a browser workspace for building and evaluating pipelines. Reducto raised a $75M Series B led by a16z in October 2025 and names Harvey, Mercor and Rogo as customers.

Key Functionalities

• Parse, Extract, Deep Extract, Split, Classify and Edit (form filling) as separate endpoints.
• Citations with bounding boxes on extracted fields, checked visually in Studio.
• Python, Node.js and Go SDKs, a CLI and an MCP server.
• SOC 2 Type II, HIPAA with a BAA, EU data residency, and VPC, on-premises or air-gapped deployment.

Pros:

• Edit endpoint for filling forms, which Landing AI's own comparison page says ADE does not offer
• Flat per-page extraction price with parsing included
• Wide range of deployment options, including air-gapped

Cons:

• Confidence scores and a hosted review queue are not described in the docs we read
• Dense schemas over 100 fields a page can add a per-field surcharge that is not published
• No business-rule validation layer is described; Deep Extract checks values against the source, not against your rules

Pricing: Pay-as-you-go per 1,000 pages: Parse $10, Extract $20 with parsing included, Deep Extract $40, Classify $7.50. New accounts start with free credits. Growth and Enterprise are custom.

Verdict: The closest like-for-like replacement for a team that used ADE mainly as a parser, with the added option of writing results back into documents. Teams that need values checked against business rules will still be writing that layer themselves.

3. Extend: Best for Engineering Teams That Want a Review Step Inside the Pipeline

Best for: Engineering and AI teams that want parse, extract, classify and route steps as an API, with a human review step and validation step they can add to the same workflow.

Extend positions itself as "Document Processing Infrastructure for AI Agents." It covers parsing, schema-based extraction with per-field citations, splitting, classification, form filling and tamper detection. Workflows chain those with validation and router steps, a human review step that pauses a run in a dashboard queue, per-field confidence scores, and workflow versioning. Customers listed on its site include Brex, Checkr, Square, Mercury and Upstart.

Key Functionalities

• Workflow steps for parse, extract, split, classify, validate, route and human review, with versioning.
• Per-field citations and confidence scores in the extraction output.
• Multifile extraction across 2 to 50 files in one run, and 35-plus file types.
• SOC 2, HIPAA and GDPR, with US or EU hosting or deployment in your own VPC.

Pros:

• Review queue, per-field confidence and validation steps are published features, which covers the three gaps teams most often hit with ADE
• Pay-as-you-go credits with a free allowance and no contract
• Can run inside a customer's own cloud

Cons:

• Credits stack per step, since parse and extract are billed separately
• Built for engineering teams; an operations team without engineers will need setup help
• Extraction is model-driven, so field behaviour follows Extend's model updates rather than a configuration the team writes

Pricing: Credits at $0.01 each, with 10,000 credits free. In Performance mode, parsing costs 2 credits a page and extraction 3, so 5 credits a page. Light modes cost less. Growth and Enterprise are custom.

Verdict: A good landing spot for developers who liked ADE's API shape but need a review queue and confidence scores out of the box. Teams that want to pin field behaviour to logic they wrote should test whether model-driven extraction holds up on their own samples across updates.

4. LlamaExtract: Best for Teams Already Building on LlamaIndex

Best for: Developers who already use LlamaIndex or LlamaParse for retrieval and want schema extraction on the same account and credits.

LlamaExtract is the extraction product in LlamaCloud, next to LlamaParse, which promises to transform "messy documents into AI-ready data at scale." Extraction returns JSON shaped by a schema, with citations to the source page, and the documented examples include confidence scores. LlamaParse is the core of the product and ranks well in community comparisons for tables and figures; extraction is the newer layer on top.

Key Functionalities

• Schema extraction with citations, in Cost-effective, Agentic and Agentic Plus tiers.
• LlamaParse output in Markdown, text or JSON for RAG pipelines.
• Classify and Split endpoints.
• Python, TypeScript, Go and Java SDKs, a CLI and REST.

Pros:

• One account and one credit pool for parsing, retrieval and extraction
• Low entry price, with a free tier and a $50 Starter plan
• Familiar to teams already building with the LlamaIndex framework

Cons:

• A hosted human review queue is not described on the pages we read
• Credit cost per page changes with the tier chosen for both parsing and extraction
• Compliance details (SOC 2, HIPAA) were not stated on the pricing or docs pages we read; ask for the trust documentation

Pricing: Credits at $1.25 per 1,000. Extraction including parsing costs 8 credits a page in Cost-effective mode (about 1 cent), 25 in Agentic (about 3 cents) and 60 in Agentic Plus. Free with 10,000 credits; Starter $50 a month with 40,000 credits; Pro $500 a month with 400,000 credits; Enterprise custom, with SaaS or hybrid deployment.

Verdict: The natural choice for a team whose stack is already LlamaIndex and whose main job is retrieval, with extraction as a second need. Teams whose main job is validated field extraction will be building checks and review themselves.

5. Azure AI Document Intelligence: Best for Teams Committed to Azure

Best for: Engineering teams already running on Azure that want prebuilt models for common financial and tax documents, or custom models trained on a few labeled samples, inside their own cloud tenancy.

Azure AI Document Intelligence offers prebuilt models for invoices, receipts, bank statements, pay stubs, US tax forms and US mortgage documents, a Layout model for tables and structure, and custom extraction models. Microsoft's docs say a custom model needs "only five examples of the same form or document type to get started." Fields come back with a confidence score between 0 and 1, though Microsoft notes "not all document fields return a confidence score." Microsoft now places it alongside Content Understanding, a separate service with LLM-powered analyzers.

Key Functionalities

• Prebuilt models for invoices, receipts, bank statements, pay stubs, W-2s, 1099s and mortgage forms.
• Custom template and neural models trained from labeled samples in Document Intelligence Studio.
• Per-field confidence and bounding polygons.
• Available in many of Azure's 60-plus regions, with disconnected containers for restricted environments.

Pros:

• The broadest region choice in this list, which matters to buyers whose residency needs fall outside the US and EU
• Low per-page prices on prebuilt models, with commitment tiers at volume
• Stays inside an existing Azure security and billing setup

Cons:

• No review UI and no validation layer; Microsoft's docs suggest adding "a human review stage" yourself
• Custom models are tied to the API version lifecycle and need retraining and retesting on migration
• Prebuilt models return Microsoft's fixed schema, which a team then maps to its own

Pricing: Per 1,000 pages in East US: Read $1.50, prebuilt and Layout $10, custom extraction $30. 500 pages a month free on the free tier. Commitment tiers lower the rate at volume.

Verdict: A sensible default for an Azure shop with standard financial documents and engineers to build the review and validation around it. Teams without a cloud commitment will find the API tools above quicker to start with.

How Do These Five Landing AI Alternatives Compare Side by Side?

Star ratings reflect strength within each dimension, not an overall rank.

Which Landing AI Alternative Fits Your Team?

The table above compares capabilities. This one answers a narrower question: for a given team and job, which tool goes first on the shortlist.

Conclusion: Match the Tool to the Reason You Are Leaving

Landing AI remains a good choice for parsing visually complex documents into Markdown for retrieval and agents, with fine-grained grounding and compliance options at a low entry price. It fits less well when the extracted values feed a decision and need checking, when a model update moves results a team had already approved, or when hosting has to sit somewhere other than Ohio or Ireland.

Sensible fits teams that need validated extraction they can version, and a managed services team to build it when they would rather not. Reducto fits AI teams that also write back into forms, Extend fits engineers who want review inside the pipeline, LlamaExtract fits teams already on LlamaIndex, and Azure fits teams committed to Azure. The quickest way to decide is to run the same twenty documents through the two closest candidates, including the awkward ones, and compare the output with what the downstream system needs. Sensible's head-to-head pages cover Reducto, Extend and LlamaParse in more detail.

FAQs

What is the best alternative to Landing AI?

Sensible is the strongest fit for teams that need extracted values checked before they reach a decision, with layout and LLM methods in one versioned configuration, validation rules, a review queue and per-document pricing. Reducto is the closest like-for-like option for teams that mainly used Landing AI as a parser.

What is agentic document extraction?

Agentic Document Extraction (ADE) is Landing AI's product: a vision model parses the document into Markdown with bounding boxes, and an LLM extracts fields against a JSON Schema. Other vendors use "agentic" for different things, such as Reducto's loop that checks and re-extracts its own output.

How does Landing AI pricing work?

Landing AI charges credits at $0.01 each, by the page and by the character, separately for parsing and extraction. A typical business page costs about 3 credits on the asynchronous standard tier and about twice that on synchronous calls. Team plans start at $250 a month.

Does Landing AI validate extracted data?

Landing AI checks that a schema is well formed, but its schema converter removes constraint keywords such as pattern, minimum and maximum and ignores the required keyword. Its docs describe no business-rule or cross-field validation, and a missing number or enum field returns an invented value unless the schema allows null.

Which Landing AI alternatives offer data residency outside the US and EU?

Landing AI hosts in AWS US East (Ohio) and AWS EU (Ireland). Azure AI Document Intelligence runs in many of Azure's 60-plus regions, and Sensible offers custom region and data-residency deployments on Enterprise. Reducto and Extend list EU hosting and deployment in a customer's own cloud.

Can I move my Landing AI schema to another tool?

Field names and descriptions carry over, since a Landing AI schema is standard JSON Schema and most API tools in this list accept one. Extraction behaviour does not carry over, because in ADE it lives in Landing AI's model rather than in the schema.

Does Sensible require training a model?

Sensible extracts with SenseML configurations rather than trained models. A configuration mixes layout methods for fixed structure with LLM methods for fields that vary, and a layout change means editing the configuration and publishing a new version.

Can Sensible build and maintain the extraction for us?

Sensible's managed services team writes and maintains configurations for each document type, onboards new formats and manages model and LLM updates. On the hybrid tier your engineers handle integrations; on the fully managed tier Sensible owns the pipeline from ingestion through integration.