Extend Alternatives: Comparing Document Processing APIs

Comparing Extend alternatives means looking beyond whether a vendor has an API. Extend combines document processing APIs with workflow tooling. The useful questions are how well each platform handles your documents, what evidence it returns for extracted values, how it fits your deployment requirements, and what it costs at your expected volume.

This guide compares five alternatives, with particular attention to complex financial, legal, and healthcare documents.

TLDR:

• Extend offers document processing APIs, SDKs, workflow tooling, and Enterprise self-hosted or bring-your-own-cloud deployment.
• Several products provide citations, bounding boxes, or confidence scores. Compare how well that evidence survives complex tables and nested fields.
• Unsiloed AI is a strong fit when document structure, schema-driven extraction, inspectable output, and air-gapped deployment matter.
• The most reliable comparison uses the same representative documents and target schema across every shortlisted product.

What Extend Offers

Extend provides parsing, classification, extraction, splitting, and workflow tooling for PDFs, images, emails, and other unstructured documents. Engineering teams can use its REST API or its Python, TypeScript, Java, and Go SDKs, while operations teams can configure workflows and review results in the platform.

Extend also supports spatial citations and per-field confidence scores, so it shouldn't be treated as a no-code tool with limited traceability. Enterprise customers can choose self-hosted or bring-your-own-cloud deployment. Its published compliance posture includes SOC 2 Type II, HIPAA with a Business Associate Agreement (BAA), and the General Data Protection Regulation (GDPR), as documented in the Extend Trust Center.

An alternative becomes worth evaluating when your documents, integration model, review process, deployment constraints, or expected volume call for a different tradeoff. The relevant difference is fit, not whether Extend has an API.

How to Evaluate Document Processing Alternatives

Vendor feature lists don't show how a parser behaves on your hardest documents. Use the same test set and output schema for each product on your shortlist.

• Include representative files, scans, and difficult tables rather than clean sample documents alone.
• Measure missing and incorrect values separately. A valid JSON response can still omit rows or sections.
• Inspect source references and test whether confidence thresholds identify known errors.
• Compare end-to-end latency and cost, including parsing, extraction, retries, and human review.
• Confirm where models run, which network dependencies remain, how long data is retained, and which requirements are contractual for your plan.

This process turns a broad feature comparison into evidence your engineering, security, and operations teams can evaluate together.

Five Extend Alternatives to Evaluate

The products below cover different parts of the document processing stack. Some focus on parsing for retrieval, while others combine extraction, review, and workflow capabilities.

1. Unsiloed AI: Best Fit for Complex Documents and Controlled Deployment

Unsiloed AI provides vision-first, layout-aware APIs for converting complex, multimodal documents into structured outputs. Its dual-stream approach captures semantic content and structural layout, which is useful when a pipeline must preserve relationships across nested tables, figures, and multi-section documents.

• Schema-driven extraction with source references, bounding boxes, and confidence scores per field
• Hierarchical indexing with parent-child chunk mapping for retrieval-augmented generation (RAG) pipelines
• Cloud, on-premises, hybrid, and fully air-gapped deployment options
• REST API plus Python and JavaScript SDKs

Unsiloed is a strong candidate for teams processing financial statements, contracts, clinical records, and other documents where reviewers need to inspect the source behind extracted values. Its API-first model also suits teams that want to build parsing and extraction into their own application rather than adopt a broader workflow layer.

What to evaluate: table completeness, references for nested fields, and the integration work required for your review process.

2. Reducto: API-First Parsing and Extraction

Reducto provides APIs for parsing documents into retrieval-ready output and extracting fields into a schema. Its parsing pipeline combines computer vision and vision-language models, with processing options designed for difficult layouts and scanned documents.

• Parsing for more than 30 document formats
• Schema-based extraction with field citations, bounding boxes, source text, and confidence scores
• REST API plus Python and Node.js SDKs
• Cloud and private deployment options

Reducto is a strong candidate for engineering teams that want document parsing and extraction through an API without adopting a workflow-first platform.

What to evaluate: output quality on your most complex layouts, processing latency, and the cost of the modes required to reach your accuracy target.

3. LlamaParse and LlamaExtract: Best Fit for the LlamaIndex Ecosystem

LlamaParse reconstructs sections, hierarchy, tables, figures, and other document structure for RAG and agentic applications. Teams that also need schema-based field extraction can evaluate LlamaExtract, which adds structured extraction, citations, and confidence scores.

• JSON and Markdown parsing outputs
• Text and image extraction for multimodal documents
• Custom parsing instructions and automatic processing modes
• Direct integration with LlamaIndex and LlamaCloud

This combination is a natural shortlist choice for teams already building retrieval and agent workflows with LlamaIndex. Treat parsing and extraction as related but separate products when estimating cost and integration effort.

What to evaluate: which product or combination covers your pipeline, how citations map to nested output, and whether the available deployment option satisfies your security requirements.

4. Amazon Textract: Best Fit for AWS-Native Workloads

Amazon Textract is a managed AWS service for optical character recognition (OCR), forms, tables, signatures, identity documents, invoices, and targeted document queries. It returns hierarchical JSON with geometry and confidence data for detected elements.

• Text detection and layout-aware document analysis
• Form key-value pairs, tables, and expense-specific extraction
• Query answers with confidence scores and source locations
• Native integration with Amazon S3, AWS Lambda, and other AWS services

Textract is a practical option for teams already operating on AWS that need managed OCR and common document extraction capabilities at scale.

What to evaluate: the post-processing required for your target schema, the combined price of every feature used per page, and performance on document types outside its specialized APIs.

5. Unstructured: Best Fit for Open-Source Ingestion Pipelines

Unstructured offers an open-source Python library and a managed platform for ingesting and preparing content from many file types and data sources. The library partitions documents into elements such as titles, tables, list items, and narrative text while retaining metadata for downstream processing. The managed platform adds connectors, enrichment, workflow tooling, and schema-based extraction.

• Open-source document partitioning and chunking
• Connectors for cloud storage, databases, and software-as-a-service sources
• Managed parsing, enrichment, and extraction workflows
• SDK and API access for production pipelines

Unstructured is a strong candidate when connector breadth, open-source components, and configurable ingestion pipelines matter as much as field extraction.

What to evaluate: which capabilities require the managed platform, the configuration needed for production accuracy, and how much validation logic your team must add.

Extend Alternatives Feature Comparison

This table is a shortlist guide rather than a substitute for testing. Product capabilities and packaging change, and similar labels don't guarantee equivalent output on the same document.

Product	Primary fit	API and extraction	Source evidence	Deployment
Extend	APIs plus configurable workflows and review	REST; Python, TypeScript, Java, and Go SDKs; schema extraction	Spatial citations and per-field confidence	Cloud; self-hosted or bring your own cloud on Enterprise
Unsiloed AI	Complex documents and controlled environments	REST; Python and JavaScript SDKs; schema-driven extraction	Source references, bounding boxes, and per-field confidence	Cloud, on-premises, hybrid, or air-gapped
Reducto	API-first parsing and extraction	REST; Python and Node.js SDKs; schema extraction	Citations, bounding boxes, source text, and confidence	Cloud and private deployment options
LlamaParse and LlamaExtract	RAG and agents in the LlamaIndex ecosystem	Parsing plus a related schema-extraction product	Citations and confidence through LlamaExtract	Cloud and private deployment options; confirm plan requirements
Amazon Textract	Managed extraction on AWS	AWS APIs and SDKs; forms, tables, queries, and specialized document APIs	Geometry and confidence for supported output types	AWS managed service
Unstructured	Open-source and connector-led ingestion	Python library, API, SDKs, and managed extraction	Element metadata; verify field-level evidence for your workflow	Open-source self-hosting or managed platform

Several tools in this comparison provide citations or confidence scores. The useful distinction is how those signals fit your workflow: whether a reviewer can locate the source of an extracted value, whether nested fields and table rows retain their references, and whether confidence scores help identify errors on your documents.

When to Choose Unsiloed AI

Put Unsiloed on your shortlist when your pipeline depends on preserving complex document structure, extracting fields into a defined schema, and letting reviewers inspect the source behind the output. Its vision-first approach and on-premises or air-gapped deployment options are particularly relevant to teams processing financial statements, contracts, and clinical records.

Extend remains worth considering when you want document APIs together with workflow and review tooling. The deciding evidence should come from your own documents: table completeness, field accuracy, source-reference quality, and the work required to integrate the output.

Book an Unsiloed demo with representative documents and your target schema. Use the session to inspect the extracted values and source references, and discuss the deployment your team requires.

FAQ

When Is It Worth Evaluating an Extend Alternative?

Evaluate alternatives when you need a different balance of document accuracy, workflow tooling, integration control, deployment, latency, or cost. Extend already provides APIs and private deployment options, so compare products against a concrete workload rather than choosing based on category labels.

What Features Matter Most When Comparing Document Extraction Tools?

Start with output quality on your own documents. Then compare source references, confidence metadata, schema control, failure handling, latency, deployment, and the full cost of processing and review. Give extra weight to the requirements that create operational risk in your application.

How Do Vision-Language Models Differ From OCR?

OCR detects text, and modern OCR services can also return layout, geometry, and relationships. Vision-language approaches use visual and textual context together to interpret document structure. Neither approach guarantees better results for every document type, so test representative files and measure field and table accuracy.

Can I Get Field-Level Traceability From Document Extraction APIs?

Yes. Several products offer citations, bounding boxes, or confidence scores, but the returned metadata and supported configurations differ. Test whether the API preserves references for the fields and table rows your reviewers need to verify.

Which Deployment Options Matter for Regulated Workloads?

Cloud, bring-your-own-cloud, on-premises, and air-gapped deployments provide different levels of operational control. Confirm where the models run, whether the system has external network dependencies, how updates work, what data the vendor retains, and which compliance commitments appear in your contract.