Parse vs. Extract: Which ParseAI API Is Right for Your Use Case

ParseAI has two document APIs: Parse and Extract. Both accept a document and return structured output. The difference is in what "structured" means for each, and that distinction determines which one belongs in your stack.

What Parse Does

The Parse API takes any document — PDF, scan, image, or handwritten form — and converts it to clean, well-structured markdown. Tables become markdown tables. Headings are preserved. Lists are formatted correctly. The output is the full document, represented as text that an LLM or search index can actually work with.

Parse does not try to identify or extract specific fields. It makes no assumptions about what the document is or what you need from it. It just gives you the document content in a reliable, clean format.

What Extract Does

The Extract API takes a document and a schema you define. The schema is a list of field names and descriptions in plain English — no code, no regex, no templates. Extract returns the values for those specific fields as structured JSON, with a confidence score for each field.

Extract is opinionated. You tell it what you want, it finds it in the document and returns it. If a field is not present or the value is ambiguous, the confidence score reflects that.

When to Use Parse

Choose Parse when you do not know in advance exactly which data you need, or when you need the full document content rather than specific fields. The most common use cases:

• RAG pipelines — Parse turns any document into clean markdown, which chunks reliably and indexes well. The structure (headers, tables) is preserved, which improves retrieval quality.
• LLM context injection — When you want to feed a document to a language model as context, you need clean text. Parse gives you that without the noise of raw PDF extraction.
• Document chat applications — Users ask questions about specific documents. Parse gives you the full content to search and surface relevant sections.
• Content ingestion pipelines — When documents are the source of truth and you need to make them searchable or processable downstream.

When to Use Extract

Choose Extract when you know exactly which fields you need and you are processing the same document type repeatedly. The most common use cases:

• Data pipelines — You receive invoices, statements, or forms and need to push specific fields into a database or ERP. Extract gives you a predictable JSON schema on every call.
• Agent tool calls — AI agents that need to read a document and act on specific values. Extract is a clean function call: submit document plus schema, receive structured JSON.
• Application integrations — When a field from a document needs to populate a form, trigger a workflow, or feed into a calculation, Extract gives you the value directly.
• Operations teams — Via the Workflow platform, which is built on Extract. Ops staff describe the fields they need in plain English; the platform returns extracted values for review.

The Key Distinction

Parse gives you everything in a readable format. Extract gives you specific things in a machine-ready format.

If you are building for humans to read and search — RAG, document chat, knowledge bases — Parse is the right layer. If you are building for systems to process — databases, APIs, automations — Extract is the right layer.

Using Both Together

Some workflows benefit from both. A common pattern: use Parse to ingest a new document type and understand its structure, then define an Extract schema once you know what fields matter, then use Extract for all subsequent processing of that document type.

Another pattern: Parse for indexing (search and retrieval), Extract for actioning (triggering downstream steps based on specific field values).

Pricing

Parse and Extract are billed at the same per-page rate. One page processed through Parse costs the same as one page processed through Extract. You are not penalised for which API you use or for using both in the same workflow.