LlamaParse vs Docling

Short answer: Docling is free and MIT-licensed, so you self-host it and pay in compute and engineering time. LlamaParse is a hosted API that bills $1.25 per 1,000 credits, which works out to $1.25 to $56.25 per 1,000 pages depending on which of its four parse tiers you pick. Neither one returns invoice fields. Both hand you a structured document, and turning that into vendor names, dates and line items is still your job.

That last sentence is the part people discover late, so it is worth stating up front. These two tools are competing to be the ingestion step in an AI pipeline, not to be an invoice product. If your actual goal is a spreadsheet of accounts payable data by Friday, the choice between them matters less than you think.

What each tool actually is

Docling is an open-source document conversion toolkit maintained by IBM Research Zurich, hosted as a project in the LF AI and Data Foundation. Its codebase is under the MIT license, and at the time of writing the repository sits at roughly 64,700 GitHub stars. It converts PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, email formats and audio into a unified representation it calls a DoclingDocument, which you can then export as Markdown, HTML, DocTags or lossless JSON.

LlamaParse is the commercial parsing service from LlamaIndex. You send a document to a hosted API and get back clean Markdown with tables and reading order preserved. It is not open source, despite the LlamaIndex framework itself being open source, and it requires an API key. It is priced in credits rather than pages, which is the source of most confusion about what it costs.

LlamaParse vs Docling, side by side

What LlamaParse actually costs

LlamaParse sells credits at $1.25 per 1,000, in both its North America and Europe regions. A page consumes a different number of credits depending on the tier: Fast bills 1 credit, Cost-effective bills 3, Agentic bills 10 and Agentic Plus bills 45. Multiply those out and you get $1.25, $3.75, $12.50 and $56.25 per 1,000 pages.

The spread between cheapest and dearest is 45x, which is why a single quoted LlamaParse price is close to meaningless. It also means the tier you choose is the entire budget decision. LlamaIndex own documentation points table-heavy, scanned and multi-column documents at the Agentic tier, and invoices are exactly that shape of document, so $12.50 per 1,000 pages is the honest planning figure rather than the $1.25 headline. We work through every tier, the plan allowances and the extraction surcharge on our LlamaParse pricing breakdown.

One detail worth knowing before you sign up: the paid plans carry no volume discount. Starter costs $50 and includes 40,000 credits, which is exactly $50 of credits. Pro costs $500 and includes 400,000 credits, exactly $500 of credits. Higher tiers buy concurrency, support and deployment options, not cheaper pages.

What Docling actually costs

Nothing, in license fees. That is genuinely the answer, and it is the strongest argument for it. But free software is not free to operate, and the honest cost model has three parts: the machine you run it on, the engineer who sets it up and keeps it running, and the time spent tuning it for your document mix.

For a rough sense of where the crossover sits, take a team processing 100,000 pages a month. On LlamaParse at the Agentic tier that is 1,000,000 credits, or $1,250 a month. At that level a dedicated machine and a slice of an engineer start to look like the cheaper option. At 2,000 pages a month, which is a typical mid-size accounts payable volume, the same tier costs $25 and self-hosting anything is obviously not worth the trouble.

So the volume question answers itself more cleanly than the feature question does. Low volume favors the hosted API. High and sustained volume favors the open-source one. The awkward middle is where you should weigh how much your team enjoys operating infrastructure.

Which is more accurate?

Published head-to-head accuracy figures for these two vary a lot between sources, and several of the numbers circulating in blog posts do not trace back to a reproducible benchmark. We are not going to quote one as fact. What we can say from working with both output formats is structural: Docling produces a richer intermediate representation, capturing layout, reading order, table cell boundaries and formula positions in one object, while LlamaParse produces cleaner Markdown out of the box with less post-processing.

The practical way to settle it is to run twenty of your own worst documents through both. Not your tidiest digital PDF, your worst phone photo of a crumpled invoice, because those set your quality floor. Twenty documents will tell you more than any benchmark table, and LlamaParse free tier plus a Docling install costs you nothing but an afternoon.

The thing neither of them does

Both tools give you a well-structured document. Neither gives you an invoice.

That distinction sounds pedantic until you build on it. A parsed invoice is Markdown or JSON describing what is on the page: headings, paragraphs, a table with cells in it. What accounts payable needs is different: a vendor name matched to your supplier list, an invoice number checked for duplicates, a date, a tax total that reconciles, and line items mapped to general ledger codes. Getting from the first to the second is schema design, validation logic, an exception queue for the fields that come back uncertain, and ongoing maintenance every time a supplier redesigns their template.

LlamaParse at least offers a paid path: LlamaExtract returns typed fields against a schema you define, billing the extract tier plus the parse tier underneath it, so 25 credits a page at the default Agentic setting. That is $31.25 per 1,000 pages, and it is more than three times what AWS Textract AnalyzeExpense or the Azure prebuilt invoice model charge at $10.00 per 1,000 pages, both of which return invoice fields natively without you writing a schema. Docling has no equivalent, so with Docling that layer is entirely yours to build.

There is also a security dimension that document pipelines tend to miss. Invoices arrive from outside your organization, and text you did not author is being fed straight into a language model. If that model can call tools or touch internal systems downstream, it is worth putting something in front of it that can block instructions smuggled inside the documents themselves, because a parser will faithfully transcribe a malicious line just as accurately as it transcribes a subtotal.

How to choose

Choose Docling if

Documents cannot leave your infrastructure for compliance reasons, you are processing sustained high volume where per-page fees compound, you already have engineers comfortable operating Python services and GPUs, or you want a rich structural representation to build on rather than Markdown. The MIT license also means no vendor can change your pricing.

Choose LlamaParse if

You are building on LlamaIndex and want the integration to be trivial, your volume is low enough that credits cost less than operations, you want to ship this month rather than next quarter, or your document mix spans many formats and you would rather not maintain the parsing layer yourself.

Choose neither if

The job is invoices specifically and the output you need is a spreadsheet or an accounting import. A general document parser is the wrong shape of tool for that, and you will spend more on the extraction layer you build on top than you ever spend on parsing. A purpose-built invoice tool already handles field extraction, line items, validation and export, which is why our own invoice data extraction software is priced flat per month rather than per page.

Frequently asked questions

Is Docling better than LlamaParse?

Neither is universally better. Docling wins on cost at volume, data residency and licensing, since it is MIT and self-hosted with no per-page fee. LlamaParse wins on time to ship, format breadth and not having to operate anything. Test both on your own worst documents rather than trusting a benchmark.

Is LlamaParse free?

LlamaParse has a free plan with 10,000 credits a month, which covers 10,000 pages on the Fast tier or 1,000 pages on the Agentic tier. Beyond that it is paid. When credits run out, credit-consuming API requests return a 402 error until the next billing cycle resets the allowance.

Is Docling really free for commercial use?

Yes. The Docling codebase is released under the MIT license, which permits commercial use without a fee. Your costs are the hardware you run it on and the engineering time to integrate and maintain it, not licensing.

Can either one extract invoice line items into Excel?

Not on their own. Both return a parsed document rather than mapped invoice fields, so line items come back as table structure that you still have to interpret, validate and export. LlamaExtract adds typed fields for 25 credits a page. Purpose-built invoice tools do this step natively.

Does LlamaParse send my documents to a third party?

Yes. LlamaParse is a hosted API, so documents leave your network to be processed on LlamaIndex infrastructure. If supplier invoices carry data that cannot go to a third party under your compliance rules, self-hosted Docling is the option that keeps processing inside your boundary.

Which one should I use for a RAG pipeline?

Either works well, and the deciding factor is usually operational rather than technical. If you are already using LlamaIndex, LlamaParse is the path of least resistance. If you are running high volume or have data residency constraints, Docling removes both the per-page cost and the third-party transfer.