“We were scaling document volume by nearly 50 percent every month —
Nutrient handled it without us adding resources.”
Harvey
compare
Nutrient is a LlamaIndex alternative for document extraction and retrieval augmented generation (RAG). This feature-by-feature comparison shows where Nutrient’s Data Extraction API wins against LlamaParse and LlamaExtract — deterministic grounding, a self-hosted deployment option, and a full document platform — and where LlamaIndex has the edge.
| Nutrient | LlamaIndex | |
|---|---|---|
| Core approach | An owned, hybrid optical character recognition (OCR) + AI
pipeline (text, structure, understand, agentic) you tune per
document. Repeatable, rule-based output you control — not a single
model’s best guess. | Cloud parsing built on vision language model inference
(LlamaParse), plus a separate open source local parser
(LiteParse). Output quality depends heavily on foundation-model
behavior. |
| Deployment | ||
| Output formats | ||
| Scope |
Both tools can pull data from a clean invoice. The difference shows up when a number is wrong in a contract or a clinical form. An LLM hands you JSON from a probabilistic read of the page and asks you to trust it. Nutrient hands you JSON where every value traces back to an exact box on the page — with a confidence score, a grounding label, and table coordinates. “The model said so” doesn’t survive an audit. “Page 2, row 7” does.
| Grounding | Nutrient | LlamaIndex |
|---|---|---|
| Per-field bounding boxes | ||
| Confidence scores | ||
| Grounding match labels | ||
| Visual citation overlay | ||
| Grounding without data egress |
No parser is best at everything, and any benchmark depends on the documents it’s run against. LlamaParse is optimized for spatially dense, form-heavy documents — schedules, insurance grids, scanned forms — and its agentic tiers reconstruct charts into tables. Nutrient is built for content-heavy documents — contracts, research papers, technical documentation, and knowledge bases — where structured Markdown that preserves heading hierarchy, lists, and table semantics is what an LLM actually needs to reason over. That’s the majority of real-world enterprise RAG.
Parsing, structured extraction, OCR, deployment, grounding, and more — compared capability by capability.
| Nutrient | LlamaIndex | Winner | |
|---|---|---|---|
| Parsing to Markdown | Structured Markdown with heading hierarchy, lists, and table
semantics. Strong on content-heavy documents. | Layout-aware parsing, strongest on form-heavy and spatially dense
documents. | Different jobs |
| Schema-based extraction | The /extract endpoint maps a document to your JSON Schema
with per-field citations. | LlamaExtract maps to a schema with per-document, per-page, and
per-table-row targets. | Draw |
| OCR | Built into structure, understand, and agentic modes. 100+
languages with automatic handling. | OCR in the agentic tiers; LiteParse falls back to local
Tesseract. | Nutrient |
| Self-hosted/data residency | Self-host the extraction engine via the SDKs and Document Engine
for data-residency needs. | LlamaParse is cloud-only. Local LiteParse is lower accuracy. | Nutrient |
| Citations and confidence | Bounding boxes, confidence scores, and interpretable match labels
— plus a viewer to render them. | Bounding coordinates and confidence; rendering left to the
developer. | Nutrient |
| Chart and figure data extraction | Available via vision language model (VLM)-augmented agentic mode;
not a current strength. | LlamaParse Agentic Plus reconstructs charts and plots into
structured tables. | LlamaIndex |
| Form-heavy, dense spatial layouts | Handled by understand and agentic modes. | Purpose-built for spatially dense forms, schedules, and grids. | LlamaIndex |
| Multilingual extraction | 100+ languages with automatic language handling. | Language support varies by tier; LiteParse is English-centric. | Nutrient |
| Output formats | Spatial JSON, Markdown, and schema JSON from one API. | Markdown and schema JSON across two products. | Nutrient |
| Visual review in a viewer | Render, highlight, and verify citations on the original document
in the Nutrient viewer. | Not available. | Nutrient |
| Framework and ecosystem integrations | SDKs, a Model Context Protocol (MCP) server, and a growing set of
connectors. | Mature Python connectors and a large, established RAG ecosystem. | LlamaIndex |
| Full document platform | View, edit, redact, sign, compare, and convert — beyond
extraction. | Parse and orchestrate only. | Nutrient |
| Pricing model | Per-page credits by processing mode. | Per-page credits by parsing tier; parse and extract billed
separately. | Draw |
For regulated industries and data sovereignty requirements, where a document is processed matters as much as how well.
| Nutrient | LlamaIndex | |
|---|---|---|
| Deployment options | ||
| Encrypted transport (TLS) | | |
| SOC 2 Type 2 |
Both tools bill per page in credits — what differs is the dollar value of a credit. Nutrient’s drops as your volume grows, so you see a range; LlamaParse charges one flat rate, so it’s a single number. Here’s roughly what 1,000 pages costs, from simple text to the hardest documents.
| Nutrient | LlamaIndex | |
|---|---|---|
| Free every month | 5,000 credits | 10,000 credits |
| Simple text documents | ~$0.84–$2.00/1,000 pages | ~$1.25/1,000 pages |
| Complex layouts (tables, forms, scans) | ~$8–$18/1,000 pages | ~$13/1,000 pages |
| Hardest documents (charts, handwriting) | ~$15–$36/1,000 pages | ~$56/1,000 pages |
Approximate self-serve rates as of June 2026 (Nutrient and LlamaIndex.ai), shown per 1,000 pages — the figure rises with document complexity because more difficult pages use more credits. LlamaParse’s credit price is a flat $1.25 per 1,000 credits; Nutrient’s drops from entry to volume plans. Schema field extraction adds a flat 6 credits/page on Nutrient (LlamaExtract adds 5–15).
Every value traces back to a box on the page with a confidence score and a match label — built for outputs that have to survive an audit.
Self-host the extraction engine with Nutrient’s SDKs and Document Engine — for regulated, sovereign, and data residency-bound workloads.
Parse and extract. Then convert, redact, generate, sign, view, edit, and compare across one platform. No second vendor.
Spatial JSON, Markdown, or schema-shaped JSON from one API, with reading order and page context downstream systems can rely on.
5,000 Data Extraction API credits every month, no credit card required. Pick the cheapest mode that meets your accuracy bar.
LlamaParse is LlamaIndex’s cloud document parser, and LlamaExtract is its schema-based structured extraction service. Nutrient’s Data Extraction API covers both jobs: The /parse endpoint returns document structure as spatial JSON or Markdown, and the /extract endpoint maps a document to your JSON Schema with per-field citations.
Yes. Nutrient is the stronger fit when you need auditable, source-grounded output, a self-hosted deployment option, structured Markdown for content-heavy documents, or a single platform that also views, edits, redacts, and signs. LlamaParse has the edge on spatially dense, form-heavy documents and on reconstructing charts into tables. See the comparison table above for the capability-by-capability breakdown.
If you’re comparing LlamaIndex with other indexing and extraction tools, the main document-parsing alternatives are Nutrient, Unstructured, Reducto, Docling, and Mistral Document AI. Nutrient stands out for deterministic, source-grounded output you can audit, a self-hosted deployment option, and being a full document platform rather than just a parser. For RAG and agent orchestration specifically, LangChain and Haystack are the framework-level peers to LlamaIndex — Nutrient is the extraction layer that feeds any of them.
For the parsing and extraction layer of a RAG pipeline, yes. LlamaParse is optimized for form-heavy, spatially structured files, while Nutrient is built for content-heavy documents — contracts, research papers, technical documentation, and knowledge bases — where structured Markdown that preserves heading hierarchy, lists, and table semantics is what an LLM needs to reason over. For agentic RAG, Nutrient supplies deterministic, citable data your agents can trust, while LlamaIndex remains the orchestration framework around it. That covers the majority of real-world enterprise RAG pipelines.
Yes. Beyond the hosted API, Nutrient’s parsing and extraction can be self-hosted through its SDKs and Document Engine, so they run inside your own infrastructure for data-residency and regulated workloads. Some AI-augmented modes rely on hosted models, so check with us for air-gapped requirements. LlamaParse and LlamaExtract are cloud-only; LlamaIndex’s local LiteParse parser runs offline but is lower accuracy.
Nutrient charges per-page credits by processing mode (text, structure, understand, agentic), plus a fixed add-on for schema extraction. New accounts get 5,000 free credits per month. LlamaParse uses tiered per-page credits and bills parsing and extraction separately, so an all-in comparison depends on your document mix and the tier each page requires. Talk to our team for a comparison scoped to your workload.
Yes. The API is backed by Nutrient’s broader security practices, including SOC 2 Type 2-audited infrastructure and TLS-encrypted transport — built for use in business-critical and regulated workflows.
Yes. The Data Extraction API is the parsing layer of a full document platform. Connect its output to AI Document Processing for templates and validation; to DWS for conversion, redaction, generation, and signing; and to Nutrient SDKs when humans need to review, edit, annotate, or approve documents in your application. LlamaIndex is a parsing and orchestration layer only.
EXPLORE
Reducto is a strong agentic document extraction platform with state-of-the-art table parsing. Nutrient is the broader, deterministic document platform — extraction plus viewing, editing, signing, and conversion — at a fraction of the per-page cost.
Unstructured.io is a strong RAG-ingestion toolkit — open source partitioning, chunking, and a deep connector ecosystem. Nutrient adds what it doesn’t: grounded schema extraction and the full document lifecycle — viewing, editing, signing, and conversion.
Scanbot is a real-time mobile capture SDK — camera scanning, barcode decoding, on-device data capture. Nutrient is the platform for everything after capture: OCR, data extraction, viewing, conversion, redaction, and compliance.
5,000 free Data Extraction API credits per month — no credit card required. Parse and extract source-grounded data your AI workflows can trust.