Reducto alternatives for document extraction

Why teams look for a Reducto alternative

Reducto is a strong product. It parses PDFs, scans, spreadsheets and slides into structured JSON, extracts typed fields with a citation and bounding box on every value, and offers hosted, VPC, on-premises and air-gapped deployment. Teams that look elsewhere usually have one of four reasons: they want an open-source tool they can run themselves, they are standardised on one cloud provider, they want a different price shape, or the job is a back-office process rather than feeding an AI application.

Price shape matters most in the last case. Reducto charges per page, per operation: $10 per 1,000 pages for Parse, $20 for Extract and $40 for Deep Extract at list rates, with a 20% discount for batch jobs. That is simple to forecast.

Extend

Extend sells APIs for parsing, extraction, classification, splitting and editing, with workflows, human review and an evaluation suite to test accuracy and catch regressions. It publishes its own benchmarks with public datasets. Pay As You Go includes 10,000 credits, worth $100, and extra credits cost $0.01 each. On that plan a Performance parse is $20 per 1,000 pages and a Performance extraction $30, with parsing billed separately. Enterprise adds bring-your-own-cloud and hybrid deployment, SSO and custom models.

Choose Extend when you have engineers who will own schemas and evaluation sets. See docRefinery vs Extend for the head-to-head.

LlamaParse

LlamaParse, from LlamaIndex, parses documents into Markdown and JSON for AI applications, and its Extract product returns fields you define in a schema. Its pricing page lists a free plan with 10,000 credits, pay-as-you-go credits at $1.50 per 1,000, and a Pro plan at $500 a month. Enterprise adds private VPC and hybrid deployment. The pricing page lists SOC 2 Type II, GDPR and HIPAA.

Choose LlamaParse when the output feeds retrieval or an agent and you need wide file-format coverage. See docRefinery vs LlamaParse.

Unstructured

Unstructured turns documents into clean, chunked data for vector stores and language-model pipelines. New accounts get 10,000 free pages, then pay $0.015 a page. Its business plan runs in a dedicated instance or inside your VPC on Azure, AWS or Google Cloud. It is built for ingestion at scale, not for returning a governed set of business fields. See docRefinery vs Unstructured.

Unstract and Docling: the open-source routes

Unstract is available as open source, as a managed cloud, and as a self-hosted Enterprise edition that runs in your own VPC. Its text extraction API costs $1 to $15 per 1,000 pages depending on mode, with 100 free pages a day. Its Document Studio plans start at $499 a month for 5,000 pages. Docling is an MIT-licensed open-source converter that you install and run locally. It costs nothing to license, and you take on hosting, upgrades and any extraction logic yourself. See docRefinery vs Unstract.

The cloud providers

Amazon Textract, Azure Document Intelligence and Google Document AI are the usual choice for teams that want everything inside one cloud account. They read text, tables and forms well and offer prebuilt models for invoices, receipts and identity documents. What they return is raw material: classification, review and business rules are yours to build. Textract's text detection starts at $0.0015 a page in US West (Oregon). Our Textract alternatives page covers all three.

docRefinery

docRefinery is a next-generation extraction system built for business processes rather than retrieval. Reducto's breadth of file types follows from its focus on parsing; docRefinery turns PDFs, scans and photographs into checked field sets for invoices, bank statements, purchase orders and claims, and also handles long documents, at scale, on premises.

What docRefinery does differently from Reducto

• You define the output. Each field has a plain-language instruction, a "Return as" name you choose as its output key, a type and a required flag, so results arrive under your names.
• One output format for every document. Every field returns text, alias, field_confidence and location, and every table returns rows, so one parser handles every document type.
• Layout memory. Each sender's layout is learned once and remembered, and re-trained when a form changes.
• Long files, segmented. Long, multi-page files are handled at scale and separated into the documents inside them, each classified into its type.
• Governed fields. An administrator decides which fields may be extracted, from a catalogue of 20 document types and 785 field definitions.
• Null, never a guess. A field that is not on the page comes back null, and by default every value carries a confidence score checked against a threshold you set. Low scores go to a person.
• Learns from corrections. A reviewer's correction becomes a suggested fix, tested on that layout's own documents before anyone applies it.
• Plug and play. API keys, a workflow from one sample document, export to JSON, CSV or XLSX, a Chrome extension in early access that fills web forms, and custom types trained and hosted at no charge.

It runs in our cloud, in your private VPC, or fully air-gapped on your own servers on the Enterprise plan. Starter is free up to 100 pages a month. Paid plans are custom and quoted by sales.

Where docRefinery falls short today

• Compliance: our SOC 2 Type II audit is in progress and there is no report yet. SSO is planned for Enterprise and not available yet. We make no data-residency commitments yet. Reducto, Extend and LlamaParse are further along here.
• Track record: we are in Early Access with design partners, publish no independent accuracy benchmark, and have no public case studies.
• Scope: we do not chunk or embed documents for retrieval yet, and we do not read presentations yet. A retrieval pipeline, Parse, is planned, and presentations and wider office formats are in development. For RAG ingestion today, Reducto, LlamaParse or Unstructured fit better.
• Self-serve: paid plans are quoted by sales rather than bought by card.

Where Reducto remains the better choice

• Breadth of input: Reducto reads spreadsheets and slides as well as PDFs and scans.
• AI and retrieval workloads: its parsing output is designed for language-model and agent pipelines.
• Security paperwork: Reducto states it is SOC 2 and HIPAA compliant, with BAAs and zero data retention on Growth and above.
• A full menu of operations: parse, extract, split, classify and edit, each priced openly per 1,000 pages.

How to choose

1. Name the output. Chunks for an AI system point to Reducto, LlamaParse or Unstructured. Checked fields for a business process point to Extend, docRefinery or the cloud providers' prebuilt models.
2. Decide where documents may go. If they cannot leave your network, shortlist the options that run on-premises or air-gapped and confirm that in writing.
3. Count how often layouts repeat. If a few hundred senders produce most of your volume, test how each option handles the hundredth document from the same sender, not only the first.
4. Run 50 to 100 of your own documents through two or three options and compare field by field. Vendor benchmarks, ours included, are no substitute.