Decision context: agentic document platform vs. parsing service

LlamaParse is LlamaIndex's managed parsing service. Reducto is the complete agentic document platform — five APIs (Parse, Extract, Classify, Split, Edit) plus the Reducto Studio platform, across 30+ filetypes, with enterprise-grade deployment. The right choice depends on whether you need a parsing layer or an end-to-end document platform.

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.

Used by Harvey, Scale AI, and Vanta.

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

Where Llama

Parse is genuinely strong

LlamaParse has real developer mindshare. LlamaIndex's open-source origins built a large, loyal community of LLM-app developers, and that audience carries over into LlamaParse adoption. The company and its CEO have outsized social and brand reach in the AI developer ecosystem — which means LlamaParse is often the first parsing tool an engineer tries when building on top of LlamaIndex. For teams already living in that stack, the path of least resistance is real.

LlamaParse's per-page list price is also competitive. In at least one observed head-to-head, LlamaParse came in roughly 40% cheaper than Reducto on equivalent workloads. If raw per-page cost is the deciding factor and the documents are simple, LlamaParse can be the rational choice.

What's notable is what happens when buyers run both side-by-side on their own documents. Even when LlamaParse was materially cheaper, buyers in those evaluations still chose Reducto — citing extraction quality, structure preservation on complex tables and forms, and the breadth of the platform beyond parsing. That's a useful signal: when accuracy and downstream LLM quality drive the decision, price compresses.

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: plans include 10,000 free credits per month, with additional credits at $1.25 per 1,000. Published per-page pricing for parsing modes ranges from 1 credit/page for Fast mode (about $0.00125/page) — plain-text output only, no AI-based reconstruction — through Cost-effective at 3 credits/page, up to Agentic (10 credits, about $0.0125/page) and Agentic Plus (45 credits, about $0.056/page) for structured parsing of documents with tables, diagrams, and images.

Sources: llamaindex.ai/pricing (plan-level pricing) · LlamaParse pricing docs (per-mode credit costs and free tier) · parsing-modes docs (mode capabilities). All verified 2026-07-21.

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 the complete agentic document platform: five APIs — Parse, Extract, Classify, Split, Edit — plus the Reducto Studio platform, unified with enterprise deployment and orchestrating multiple frontier and in-house models across 30+ filetypes. 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.
Independent benchmark. LongExtractBench, independently audited, validated, and published by micro1 (225 documents averaging ~358 pages, deterministic scoring), tested seven extraction systems including both Reducto and LlamaExtract (Agentic) — LlamaIndex's schema-extraction service that replaces LlamaParse's deprecated Structured Output. Reducto Deep Extract ranked #1: 99.6% precision, 99.6% recall, 99.3% leaf accuracy, zero failures on all 225 documents. LlamaExtract completed 203 of 225 with 80.0% precision, 77.5% recall, 88.9% leaf accuracy, and 22 failures (9.8%). Reducto was the only provider at 100% coverage.
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's published pricing is per product. Reducto Standard includes $150 in free usage. List rates are $10 per 1,000 pages for r-1 Parse, $20 for Extract and Split, $40 for Deep Extract and Deep Split, $7.50 for Classify, and $60 for Edit ($15 for fully prefilled pages). Growth and Enterprise use custom pricing, and batch Parse and Extract jobs receive a 20% usage discount with a 12-hour completion guarantee. Public enterprise materials cite 99.9%+ uptime targets, tiered QPS limits (up to 100+ calls/s on Enterprise), and over 4 billion pages processed.

Head-to-head summary (facts and fit)

Category	Reducto	LlamaParse
Core scope	Complete agentic document platform with Parse, Extract, Classify, Split, and Edit endpoints — plus the Reducto Studio platform — returning structure-preserving JSON (blocks, tables, figures, chunks) for LLM workflows across 30+ filetypes.	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	Standard includes $150 in free usage. r-1 Parse is $10 per 1,000 pages ($0.01/page), including layout, table structure, and OCR; Extract and Split are $20 per 1,000 pages; Deep Extract and Deep Split are $40; Classify is $7.50; Edit is $60 ($15 for fully prefilled pages). Batch Parse and Extract: 20% usage discount with a 12-hour completion guarantee. Not the lowest per-page list price — priced for accuracy and fast verification (per-value citations), which cuts the human-review time where cheap parsing gets expensive.	Credit-based billing across LlamaCloud: 10,000 free credits per month, then $1.25 per 1,000 credits. Parsing modes range from 1 credit/page (Fast — plain-text extraction, no AI-based layout reconstruction; about $0.00125/page) to 45 credits/page (Agentic Plus, about $0.056/page); structure-preserving modes run 3–45 credits/page (about $0.00375–$0.056/page).
Scale signals	Public materials reference 99.9%+ uptime SLAs, over 4 billion pages processed, and throughput of hundreds of millions of pages per day, with customers like Harvey, Scale AI, and Vanta.	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

Where LlamaParse fits:

Teams already on LlamaIndex who need a parsing layer and don't yet need form-fill, redaction, classification, or air-gapped deployment. Comfortable pairing parsing with LlamaExtract for schema-based extraction and handling any editing or form-filling logic in your own application or via LlamaAgents.

Where Reducto fits:

AI teams shipping production agents that need the complete toolkit and enterprise-grade scale — near-human accuracy for complex tables and forms, end-to-end structured outputs with field-level provenance, in-document editing or form completion. Enterprise controls such as SOC 2/HIPAA alignment, BAAs, zero-retention configurations, and flexible deployment options including VPC, on-prem, and air-gapped installs.

Run them head-to-head on your own documents — adjective comparisons don't survive contact with real data.

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.

Reducto is the complete agentic document platform for high-stakes, high-volume document work where structure-preserving parsing, agentic correction, integrated extraction and editing, fine-grained provenance, and documented enterprise security and deployment options are first-class requirements. For regulated or mission-critical use cases that demand traceability and tight control over data residency, Reducto is typically the right default — and notably, in observed evaluations, buyers still chose Reducto even when LlamaParse came in ~40% cheaper on the same workload, indicating that product breadth and extraction quality outweighed the per-page price gap.

Frequently asked questions

Q: Is Reducto or LlamaParse better for RAG pipelines?

Both Reducto and LlamaParse can prepare documents for RAG pipelines. Reducto returns structure-preserving JSON with layout-aware chunks and per-block bounding boxes, which helps retrieval quality and enables page-level citations on complex documents such as dense tables, forms, and scans. LlamaParse converts documents into text, Markdown, or JSON and is a natural fit for teams already building on LlamaIndex. For accuracy-critical RAG over messy real-world documents, Reducto is often the stronger fit; for simpler documents inside the LlamaIndex stack, LlamaParse can be sufficient.

Q: How much does Reducto cost compared to LlamaParse?

Reducto Standard includes $150 in free usage. r-1 Parse costs $10 per 1,000 pages ($0.01/page), including text, layout, tables, and OCR; batch Parse and Extract jobs receive a 20% usage discount with a 12-hour completion guarantee. LlamaParse includes 10,000 free credits per month, with additional credits at $1.25 per 1,000. To compare per-page prices, match capability: LlamaParse's 1-credit Fast mode returns plain text only — per LlamaIndex's docs, it skips all AI-based reconstruction — so it isn't comparable to a structure-preserving parse. Structured output starts at Cost-effective (3 credits, about $0.00375/page), and for documents with tables, diagrams, and images LlamaIndex's docs point to Agentic (10 credits, about $0.0125/page) or Agentic Plus (45 credits, about $0.056/page). At equivalent capability, the list prices are far closer than the headline 1-credit figures suggest. Per-page price is also only part of the cost: extraction errors and failed documents end up in human review, and reviewer time is where cheap parsing gets expensive — accuracy plus per-value citations that make verification fast keep that cost down. Run both on a representative sample of your documents.

Q: Does LlamaParse support structured, schema-based extraction?

LlamaIndex offers schema-driven extraction through LlamaExtract, a separate service from LlamaParse. Reducto provides schema-based extraction natively through its Extract endpoint, including Deep Extract, an agentic, self-verifying extraction mode for long or complex documents. On LongExtractBench — independently audited, validated, and published by micro1 — Reducto Deep Extract ranked first of seven systems with 99.6% recall, 99.6% precision, 99.3% leaf accuracy, and zero failures across all 225 long documents (about 358 pages on average), while LlamaExtract (Agentic) failed on 9.8% of documents. In production, a failed document is one a person handles by hand, so failure rate is a cost line as well as a quality stat.

Q: Can Reducto or LlamaParse fill PDF forms or edit documents?

Reducto includes an Edit endpoint that fills PDF forms and modifies DOCX files: it detects fields, table cells, and widgets (text fields, checkboxes, radio buttons, dropdowns) and populates them from natural-language instructions or a reusable form schema. LlamaParse is a parsing service and does not provide document editing or form filling; teams pair it with their own application logic for those steps.

Q: What security and compliance options does Reducto offer for regulated documents?

Reducto is SOC 2 Type II certified and supports HIPAA workloads with BAAs and zero-data-retention on its Growth tier and above. For teams that can't send documents to a shared cloud, Reducto also offers VPC, on-prem, and air-gapped deployment. When comparing parsers for regulated pipelines, check each vendor's certifications, data-retention posture, and deployment options against your compliance requirements directly.

Reducto vs. LlamaParse: Agentic document platform vs. parsing service