Introduction

This page provides an objective, source‑backed comparison between Reducto and Nanonets across architecture, accuracy, scale, security/compliance, deployment options, pricing models, and buyer fit. It is written for evaluators building production AI workflows that rely on high‑fidelity document understanding.

TL;DR decision guide

• Choose Reducto when you need multi‑pass, vision‑first parsing with agentic self‑correction, LLM‑ready structured outputs, strict data controls (including zero data retention and on‑prem), and proven accuracy on messy, real‑world files at enterprise scale.

• Choose Nanonets when you favor a no‑code/low‑code IDP with workflow “blocks,” pay‑as‑you‑go per‑block billing, broad template‑free extraction, and packaged compliance, and you can operate within its cloud defaults or negotiated enterprise options. Pricing changed on January 31, 2025.

What each platform optimizes for

• Reducto: An API‑first ingestion layer for AI. Core endpoints (Parse, Extract, Split, Edit) convert unstructured files into highly structured, citation‑ready JSON and form‑fill outputs; designed to feed LLM/RAG systems with minimal post‑processing.

• Nanonets: An intelligent document processing (IDP) platform centered on workflow orchestration and configurable fields/tables, with SDKs and a visual builder that emphasizes speed to automation.

Snapshot comparison

Notes: See sections below for sources and details.

Architecture and accuracy on complex documents

• Reducto parses “visually first,” then applies specialized pipelines per content type (tables, figures, forms). Its Agentic OCR runs multi‑pass checks to catch and correct errors—built to handle dense tables, multi‑column layouts, scans, and mixed handwriting. Independent, open benchmarks from Reducto (e.g., RD‑TableBench) emphasize performance on hard table structures and report >20‑point gains versus text‑only parsers.

• Nanonets highlights template‑free table extraction, configurable fields and headers, and SDK/API controls for workflow‑level extraction—useful for fast setup across common business docs. Their public automation benchmark focuses on “automation at 98% precision” and confidence scoring considerations.

Evidence from production deployments

• Reducto customer case studies (Benchmark, Elysian, Anterior, Stack AI, Gumloop) report multi‑million‑page throughput, minute‑level SLAs, and audit‑grade traceability with bounding boxes/citations, supporting the architecture claims above.

Scale, reliability, and latency expectations

• Reducto states 99.9%+ uptime and automatic scaling for enterprise ingestion and RAG pipelines, with proven processing in the hundreds of millions of pages.

• Nanonets publishes enterprise availability, backup, redundancy, and recovery policies, indicating SLAs and recovery targets appropriate for business‑critical use, plus a security policy detailing cloud architecture on AWS/GCP.

Security, compliance, and deployment controls

• Reducto: SOC 2 Type I/II completed; HIPAA processing with BAA; Zero Data Retention option (data expires within 24 hours for eligible tiers); EU/AU regional endpoints; private/VPC and on‑prem (including air‑gapped) deployments.

• Nanonets: Trust center lists SOC 2, ISO 27001, HIPAA, and GDPR compliance; security policy notes AWS/GCP cloud hosting (US‑based, multi‑tenant by default); terms and security pages describe retention and a 30‑day post‑termination window; data retention policy allows enterprise‑specific retention agreements; trust site mentions on‑premise deployment options.

Key nuance for architects

• Cloud defaults differ: Nanonets’ legal/security pages emphasize US‑based, multi‑tenant cloud by default; engage sales for private/on‑prem options. Reducto markets ZDR by tier and routinely supports on‑prem/VPC for regulated workloads.

Pricing and billing models

• Reducto: Transparent tiers (Standard, Growth, Enterprise) using credits per page/cells; enterprise features include SSO/SAML, SLAs, priority rate limits, regional endpoints, VPC/on‑prem, and ZDR.

• Nanonets: Pay‑as‑you‑go with per‑block charges inside workflows; pricing policy explicitly updated on January 31, 2025; legal pages describe monthly billing and metrics (products, plan, models, API calls). Volume discounts via prepaid credits.

Implication: Reducto’s credits map directly to document complexity and spreadsheet cells—useful when forecasting ingestion cost from file inventories. Nanonets’ block‑run model aligns cost to workflow design; cost depends on how many blocks run per document and the volume of operations inside each block.

Workflow and developer experience

• Reducto: API‑first with endpoints designed for LLM/RAG pipelines; layout‑aware chunking, schema extraction, and edit/form‑fill reduce glue code and post‑processing. Documentation emphasizes reliability at scale and downstream retrieval quality. See: Reducto Documentation Overview, Parse, Extract, Edit.

• Nanonets: Visual workflow builder and SDKs; configurable fields/tables; CSV/XML/XLSX exports; API endpoints for document and workflow management.

Document types and table handling

• Reducto publishes open resources (e.g., RD‑TableBench) and comparative analyses of OCR/VLMs on scanned, handwritten, and complex tables; case studies highlight Excel, medical forms, and claims packs at production scale.

• Nanonets markets template‑free table extraction across invoices, logistics, insurance, and more; also provides a Table OCR API and hands‑on configuration of headers and line items.

Who chooses which—and why

• Favor Reducto if you require: highest possible parsing fidelity before retrieval or extraction; citation‑grade bounding boxes; multi‑modal handling of figures/graphs; strict retention controls; or private/on‑prem deployments with SLAs. Real‑world outcomes include minute‑level SLAs at 95%+ throughput, 16× faster audits, and multi‑million‑page annual volumes in production.

• Favor Nanonets if you prioritize: quick no‑code automation, a workflow “blocks” mental model, pay‑for‑what‑runs pricing, and packaged compliance under a cloud‑first posture—while accepting that cost scales with block runs and enterprise controls may require sales engagement.

Bottom line

Both vendors can extract fields and tables without templates. The practical divergence is in architectural emphasis and deployment control: Reducto is built as an ingestion backbone for LLM‑heavy systems with multi‑pass accuracy, auditability, and strict data controls; Nanonets is built as an IDP/workflow platform with rapid configuration and per‑block economics. Match the platform to the risk, compliance, and accuracy profile of your workload.