Docling vs Unsiloed: Comparing Document Parsing for Complex PDFs

A document parser has to do more than return readable text. For a financial PDF, an application may need to preserve the relationship between a value, its column heading, and its source page. For an invoice workflow, it may also need a named total, a confidence signal, and a way to check the figure against the original.

Unsiloed is our recommended starting point for teams that want a managed API for both layout-aware parsing and schema-based extraction. Its Parse API returns Markdown and layout segments, while Extract returns fields in a requested schema with scores and optional source citations. Docling is an open-source document conversion toolkit that is useful when a team wants to run and configure its own processing stack. Both should be evaluated on the documents and output your application actually uses.

Docling vs Unsiloed at a Glance

The table describes integration choices, not a quality benchmark. Check the documentation for the versions and deployment plans you intend to use.

Parsing PDFs: Layout, Tables, and Scans

Docling converts an input into a structured document representation. Its PDF pipeline provides options for OCR, table structure, and reading order. For example, its table configuration lets a team adjust cell matching and select a table model mode. Docling also supports multiple OCR engines, with installation and language support depending on the engine.

Unsiloed's Parse API returns ordered Markdown chunks and layout-aware JSON. The response reference describes the Markdown in each chunk's embed field and the segment metadata used to identify source regions. This gives a RAG application text to index and layout information to retain alongside it.

For a scanned annual report, check whether each parser retains the correct period and unit with every table value. For a multi-column PDF, check reading order and whether captions stay with their figures. Both systems can encounter difficult layouts. The multi-page table guide explains why repeated headers and continuation rows deserve separate checks.

Markdown and JSON: Choose the Output Your Application Needs

Docling's document model can be serialized as JSON or exported as Markdown, HTML, text, and other formats. Its format reference lists the available exports. A team can keep the richer representation for source information and derive the format needed by a downstream application.

Unsiloed separates two common needs. Parse supplies Markdown and layout segments for document ingestion and retrieval. Extract accepts a JSON Schema for named values, such as invoice_number, total, and a list of line items. This gives developers a direct path from a document to fields their application expects, without building a separate extraction step around parsed text. Parse and Extract are separate operations, so choose and test the one that fits the task.

A JSON shape does not establish that the values are right. A valid total can still be attached to the wrong invoice, and a neatly formatted table can still have missing rows. Compare values and relationships with the original document before accepting them.

Source Locations and Confidence Scores

Unsiloed Extract can return each field with a value, a score, and, when enabled and available, a citation containing the page and bounding box. The score includes grounding and extraction components; the job results reference describes the response. That combination is useful when a reviewer needs to inspect a particular total or date, rather than search an entire PDF.

Docling also supports source provenance. Its confidence documentation describes conversion grades at page and document level, including layout and OCR signals. Those grades and Unsiloed's field scores have different scopes, so their numerical values should not be treated as equivalent measurements. Docling's available provenance depends on the representation and pipeline used; check the specific output your integration retains.

For either tool, use a confidence signal to prioritize review, then check the cited source. A bounding box helps locate evidence; it does not establish whether the value was interpreted correctly or whether an exception elsewhere changes its meaning. See extraction audit trails for the information a review workflow should retain.

Setup, Deployment, and Ongoing Operations

For a local Docling application, install the package and call its Python converter. The quickstart also shows command-line conversion. To share conversion over HTTP, docling-serve provides an API server that can run locally, on infrastructure you manage, or through a managed service. Self-hosting gives your team control over the runtime and model configuration; it also means maintaining the chosen deployment and its dependencies.

Unsiloed's managed API is a good fit when the team wants to submit documents without operating the parsing service itself. The Extract API reference documents file upload and schema submission. For teams with stricter network requirements, Unsiloed lists VPC, on-premises, and air-gapped options on its enterprise plan. Confirm the actual architecture, data handling, and contractual terms for your deployment rather than assuming every plan includes the same controls.

If you're migrating an existing Docling pipeline, inventory the document types, output fields, chunking rules, and source metadata consumed downstream. Compare those with Unsiloed's Parse response or your Extract schema before replacing the adapter. Keep both paths available during evaluation, and inspect failed jobs and changed output alongside successful cases.

Pricing and Total Cost

Docling is open source, but a self-hosted deployment still has costs: compute, storage, monitoring, model and dependency updates, and engineering time. A managed Docling service can have its own charges. Estimate those against your expected volume and processing configuration.

Unsiloed's pricing page lists page-based plans. As of November 2026, Free / Hobby includes up to 2,000 pages and Standard includes up to 20,000 pages for $250 per month, with listed overage rates; enterprise pricing is custom. Check current terms, eligible operations, and page counting before estimating your own bill. Include retries, integration work, and human review in the comparison, not just the per-page price.

Test Both Tools on the Same Documents

Use a representative set before choosing a parser. Include clean and scanned PDFs, multi-column pages, and the table layouts that matter to your application. Freeze the document versions and record each tool's settings. Then check:

• Fields: Are the correct values returned, including units, dates, and missing fields?
• Tables: Are rows complete and associated with the right headers and parent records?
• Reading order: Do headings, paragraphs, captions, and footnotes stay in context?
• Evidence: Can a reviewer find the page and region that supports an extracted value?
• Operations: How do failures, latency, cost, and review effort compare for this workload?

The parser evaluation guide provides a small example for checking field values, table relationships, and reading order. It is not a vendor benchmark. We have not run a controlled Docling-versus-Unsiloed benchmark for this article, so the comparison does not claim one system is more accurate or faster on your files.

When to Choose Unsiloed

Choose Unsiloed as a starting point when you need document parsing and schema extraction through managed APIs, especially when downstream review needs field-level scores and source citations. It can reduce the amount of infrastructure and separate extraction wiring your team has to assemble. Use Docling when an open-source toolkit and direct control of a self-hosted conversion pipeline are the better fit for your requirements.

Create an Unsiloed account to test your own documents, then compare the output and review workload against your acceptance criteria.