Decision context: selecting reliable parsing for AI pipelines

Choosing between Reducto and LlamaParse usually comes down to three things that drive downstream LLM quality at scale:

How well they handle messy, real-world documents (tables, forms, scans, charts).
How complete and traceable their structured outputs are (JSON schemas, bounding boxes, citations).
What they offer around deployment, security, and SLAs for enterprise use.

The sections below summarize publicly documented capabilities for each platform so you can route workloads to the best fit.

What Llama

Parse provides

Product scope. LlamaParse is LlamaIndex's managed parsing service inside LlamaCloud. It converts PDFs and many other file types into text, Markdown, JSON, XLSX, and PDF representations, with multimodal support for tables, charts, images, and handwriting. Marketing materials highlight support for 90+ formats and 500M+ documents processed.
Output modes and layout. LlamaParse can return multiple output formats from the same job (text, Markdown, JSON, XLSX, PDF), and optionally includes image extraction and page screenshots. A layout-extraction option (extract_layout) adds bounding boxes for page elements such as tables, figures, titles, text, and lists, allowing downstream systems to reconstruct or reason over the original layout.
Parsing modes and pricing. Several parsing modes trade cost for quality, including recommended "Cost-effective", "Agentic", and "Agentic Plus" presets, alongside lower-level modes like "parse_page_without_llm", "parse_page_with_llm", "parse_page_with_lvm", and "parse_document_with_agent". Pricing uses a credit system; in North America 1,000 credits cost $1. As of late 2025, representative per-page pricing for the recommended parsing presets is roughly:
Cost-effective: ~3 credits/page
Agentic: ~10 credits/page
Agentic Plus (agent-style premium parsing): ~90 credits/page
Deployment. LlamaParse is delivered as a hosted SaaS service via LlamaCloud. For enterprises, LlamaIndex also offers deployment in a private VPC in the customer's cloud tenant, so workloads can run within the customer's own infrastructure account instead of a shared SaaS environment.
Structured extraction. LlamaParse previously supported "Structured Output" for schema-based extraction, but this feature is now documented as deprecated. New structured-data use cases are expected to use LlamaExtract, LlamaIndex's dedicated schema-driven extraction service, instead of relying on LlamaParse for JSON schema extraction.

What Reducto provides

Platform scope. Reducto is a vision-first, multi-pass document intelligence platform that exposes Parse, Extract, Split, and Edit endpoints. These APIs are designed to produce LLM-ready, structure-preserving JSON across PDFs, images, spreadsheets, and slides, including layout blocks (tables, figures, headers, paragraphs), chunks for retrieval, and rich metadata.
Multi-pass accuracy and real-world results. Reducto's pipeline combines computer vision with an agentic, multi-pass OCR/VLM review loop to handle complex layouts such as dense tables, multi-page forms, figures, handwriting, and mixed-language content. Public case studies report:
99.24% extraction accuracy and sub-minute SLAs on clinical prior-authorization decisions (Anterior).
Up to 16x faster claim audits on complex insurance documents (Elysian).
More than 3.5M pages/year processed for an investment platform (Benchmark), with layout-aware parsing feeding downstream memo generation.
Provenance and citations. Reducto's JSON outputs include detailed layout structure plus bounding-box-level provenance. Parse responses expose blocks and chunks with normalized coordinates and page references, and Extract responses attach per-field citations (page and bbox) alongside values and confidence scores. This supports page- and snippet-level citations in regulated workflows where every field must be traceable back to the source document.
Editing and form completion. Beyond reading documents, the Edit endpoint can modify DOCX and fill PDF forms. It automatically detects fields, table cells, and widgets (text fields, checkboxes, radio buttons, dropdowns) and populates them from natural-language instructions. A form_schema configuration lets teams templatize widgets by bounding box and type for repeatable, programmatic form filling at scale.
Enterprise posture. Reducto publishes a trust center that describes SOC 2 Type II audits, HIPAA-eligible processing with BAAs, and zero-data-retention (ZDR) options. Deployments can run as multi-tenant cloud, in a customer VPC, fully on-prem, or even air-gapped, with additional regional endpoints (e.g., EU/AU) for data-residency requirements.
Pricing and scale. Reducto uses credit-based billing across endpoints. Current documentation shows per-page credit bands that vary by endpoint and complexity (for example, standard vs. complex VLM-enhanced pages on Parse; default vs. agent-in-loop modes on Extract; beta pricing for Edit). Public enterprise materials cite 99.9%+ uptime targets, tiered QPS limits (up to 100+ calls/s on Enterprise), and multi-million-page production deployments.

Head-to-head summary (facts and fit)

Category	Reducto	LlamaParse
Core scope	Vision-first document intelligence platform with Parse, Extract, Split, and Edit endpoints returning structure-preserving JSON (blocks, tables, figures, chunks) for LLM workflows.	Document parsing service within LlamaCloud focused on converting files into text/Markdown/JSON/XLSX/PDF (plus images/screenshots), used as the parsing layer for LlamaIndex and LlamaCloud pipelines.
Complex layouts (tables/forms)	Multi-pass "agentic OCR" that combines OCR and VLM checks to handle complex tables, merged cells, forms, and figures. Publishes open benchmark work (RD-TableBench) and case-study results in healthcare, insurance, and finance.	Layout-aware parsing with OCR, LLM, and LVM modes; supports tables, charts, and images with configurable modes (fast, LLM, LVM, agentic document parsing) to trade cost vs. accuracy.
Layout provenance	Parse and Extract outputs include per-block and per-chunk bounding boxes plus citation objects (page + bbox + block IDs), enabling sentence- and field-level provenance for downstream citations and audits.	Optional layout extraction adds a `layout` section to JSON with bounding boxes and labels for tables, figures, titles, text, and lists, plus per-element confidence scores and snapshot images.
Structured extraction	Dedicated Extract endpoint for schema-based JSON, powered by JSON Schema definitions. Per-field values come with confidence and optional citation metadata (page, bbox, source block), suitable for regulated, schema-driven pipelines.	"Structured Output" on LlamaParse is now marked deprecated; schema-based extraction is provided by LlamaExtract, which defines JSON/Pydantic schemas and extraction agents that run on top of parsed documents.
In-document form fill / editing	Edit endpoint for PDF and DOCX supports programmatic form filling and document updates: automatic detection of fields, table cells, and interactive widgets, plus `form_schema` support for repeatable pipelines.	LlamaParse itself is parsing-only; it does not provide a built-in PDF/DOCX editing or form-filling API. Any editing or completion would need to be implemented in downstream applications or via other LlamaCloud components.
Deployment options	Cloud SaaS, customer VPC, on-prem, and fully air-gapped deployments are documented, all with zero-retention modes available (per-request `retention=0` or account-level policies) and optional regional endpoints (EU/AU).	Delivered as a hosted LlamaCloud service. For enterprises, documentation describes the option to deploy in a private VPC within the customer's cloud, while still using the managed LlamaCloud control plane.
Security/compliance	SOC 2 Type II, HIPAA-eligible processing, BAAs for covered workloads, zero-data-retention options, SSO/SAML, data-residency controls, and detailed security policies published in the trust center.	LlamaCloud's pricing and trust materials list SOC 2 Type II, GDPR, and HIPAA certifications. Documents describe 48-hour parsing caches by default (to avoid double billing) with a `do_not_cache` option for more sensitive workloads.
Pricing model	Credit-based billing for Parse, Extract, Split, and Edit. Published credit tables differentiate standard vs. complex pages, agent-in-loop extraction, and beta Edit pricing, and are tied to Standard/Growth/Enterprise subscription tiers.	Credit-based billing across LlamaCloud. For parsing, North American pricing is 1,000 credits = $1, with recommended modes such as Cost-effective (~3 credits/page), Agentic (~10), and Agentic Plus (~90), and add-ons like layout extraction costing extra credits per page.
Scale signals	Public benchmarks and case studies reference 99.9%+ uptime SLAs, over 1 billion pages processed overall, and deployments processing millions of pages per year (e.g., Benchmark >3.5M pages/year; Anterior and Elysian production workloads).	Product marketing highlights 500M+ documents processed and 200k+ LlamaCloud users, plus multimodal parsing and indexing across 130+ file formats when used as the Parse component of LlamaCloud.

When to choose each

Choose LlamaParse if:

You are already standardized on LlamaIndex/LlamaCloud, want rapid onboarding with cost-effective parsing modes, and primarily need high-quality text/Markdown/JSON/XLSX (plus optional layout bounding boxes, images, and screenshots). You're comfortable pairing parsing with LlamaExtract for schema-based extraction and handling any editing or form-filling logic in your own application or via LlamaAgents.

Choose Reducto if:

Your workloads depend on near-human accuracy for complex tables and forms, end-to-end structured outputs with field-level provenance, and in-document editing or form completion. You also need enterprise controls such as SOC 2/HIPAA alignment, BAAs, zero-retention configurations, and flexible deployment options including VPC, on-prem, and air-gapped installs.

Bottom line

Both platforms can parse heterogeneous, real-world documents and are used in production AI pipelines. LlamaParse is a strong fit when you are building on the LlamaIndex ecosystem, want flexible cost/accuracy trade-offs for parsing, and plan to compose separate services (LlamaExtract, LlamaAgents, your own app logic) for schema extraction and workflow automation.