docRefinery vs LlamaParse

Developer platform or back-office tool

LlamaParse is LlamaIndex's document processing platform, with Parse, Extract and Index under one roof. Its natural buyer is a developer feeding documents to an LLM application. Extract, sometimes still called LlamaExtract, adds structured data to that stack. docRefinery starts at the other end: a document data extraction product for accounts payable, lending and onboarding teams, where the deliverable is a set of checked fields going into another system. The two meet at structured extraction, so that is what this page compares.

Where LlamaParse is the better choice

If your team already works in the LlamaIndex ecosystem, adding LlamaParse is the short path, and we would not argue you out of it. It is also the stronger pick in these cases:

• Mixed or unusual inputs. It lists 90+ formats and 100+ languages. We read 70+ languages and scripts, and presentations and wider office formats are in development, not yet available.
• Retrieval and agents. Parsed content can go straight into an index. Our retrieval pipeline, Parse, is planned and not available yet (see the roadmap), so a corpus bound for a vector store belongs elsewhere today.
• Choice of effort. Extract offers tiers from Cost Effective to Agentic Plus, so developers can trade cost against depth for each job.
• Procurement. LlamaIndex lists SOC 2 Type II, GDPR and HIPAA on its pricing page, with custom BAAs, enterprise SSO and availability on the AWS and Azure marketplaces. By comparison, docRefinery's SOC 2 Type II audit is in progress with no report yet, and SSO is planned for Enterprise but not available. Ask us for the latest status.
• A public price. Credit prices and the $500 Pro plan are on its website. Above our free tier, we quote.

What docRefinery does that LlamaParse does not, or does differently

Choose docRefinery when nobody on the team wants to own an extraction schema in code, and when every value must be checkable. These are the differences against LlamaParse's Extract.

• Evidence on by default. Unless you turn scoring off, every field and table cell comes back with a confidence score and its page and box. In Extract, confidence_scores and cite_sources are options that are off by default.
• You define the output in plain words. Each field has a plain-language instruction, a "Return as" name you choose as its output key, a type and a required flag, set by your organisation and held for every caller.
• One output format for every document. Every field returns text, alias, field_confidence and location, and every table returns rows, so invoices, statements and IDs all go through one parser in your code.
• Governed fields. Fields come from a catalogue of 20 document types and 785 field definitions, or a workflow designed from one sample document. Owner, admin and member roles decide who may change them.
• A review queue in the product. A value under its threshold goes to a person, the change stays on the run, and the correction becomes a suggested fix tested on that layout's own documents. Our guide to confidence scores explains how to set thresholds.
• Null, never a guess. A field that is not on the page comes back null rather than a plausible value.
• Layout memory. When a utility bill or invoice arrives from a sender we have seen, its layout is recalled, not relearned.
• Long files, segmented. Long, multi-page files are handled at scale and separated into the documents inside them, each classified into its type.
• Air-gapped on premises. Beyond private VPC, docRefinery runs fully air-gapped on your own servers on Enterprise.
• Plug and play for operations. Export to JSON, CSV or XLSX that always reflects corrections, a Chrome extension in early access that fills web forms from a document, and custom document types trained and hosted at no charge.

If your documents rarely repeat and the output feeds an agent rather than a ledger, that advantage narrows, and a general parser may serve you better.