Sygnet vs LlamaParse

Sygnet vs LlamaParse covers two tools that both turn documents into structured text, but they sit at different points in a pipeline. LlamaParse is a hosted parsing and extraction service from LlamaIndex, built primarily to feed LLM and retrieval pipelines: it is an agentic document parser built for LLM pipelines, layout-aware OCR that turns PDFs, scans, tables, and charts into clean markdown, text, or JSON. Sygnet, the author of this page, is an intelligent document processing platform and API aimed at operational workflows where a specific field value has to be correct, checked, and traceable: zero-shot extraction with schema inference, per-field confidence scores, bounding-box provenance, validation rules, and human review routed only when confidence is low. We have tried to describe both accurately; if your problem is "chunk 400,000 PDFs well enough for a RAG index", LlamaParse is likely the better answer, and we say so below.

At a glance

Where LlamaParse is strong

If your destination is a vector index or an agent, LlamaParse is built for exactly that job and shows it. The output format is the giveaway: clean markdown that chunks sensibly, with tables preserved rather than flattened into word soup. The library describes broad file type support across PDF, PPTX, DOCX, XLSX and HTML with difficult layouts, accurate table recognition, multimodal parsing that extracts images and diagrams, and custom prompt instructions to shape the output. That last point matters more than it sounds: being able to tell the parser what kind of document it is looking at fixes a surprising number of layout problems without any code.

The commercial and engineering onramp is genuinely easy. New users get 10k free credits a month, four SDKs are published, and the v2 tier system means you pick a quality level instead of choosing models. The cost ladder is explicit, so you can start cheap on Fast and escalate only the documents that need it. There is also real breadth beyond parsing: Extract, Classify, Split and Index are all part of the same API surface, which means one vendor and one key for the whole retrieval pipeline. For teams already using LlamaIndex, that integration is hard to beat.

Where Sygnet is strong

Sygnet is built for the step after parsing, when a number has to be right and someone has to answer for it. Every extracted field comes back with a confidence score and the bounding box it came from, so a reviewer can see the source pixel region instead of re-reading the document. Validation rules run on the output, and cross-document checks compare values between related files: an amount on an invoice against the matching purchase order, for example, or a name across an ID and a proof of address. Only documents that fail a rule or fall below a confidence threshold reach a human; the rest pass straight through.

Extraction is zero-shot with schema inference, so a new document type does not mean a labelling project or a new template. Hosting is EU-only (Google Cloud europe-west1), customer documents are never used for training, and pricing is per document rather than per page or credit, which makes the cost of a 40-page lease predictable. Our bias is toward auditability: if you cannot show where a value came from, you cannot automate the decision on top of it. That is the trade we optimise for, and it is less useful if you only need good markdown.

Which one for which team

• Building RAG or an agent over a document corpus. Choose LlamaParse. Markdown output, table fidelity, chunking-friendly structure and a built-in Index product all point the same way. Sygnet is not a retrieval tool.
• Extracting specific fields that drive money or compliance decisions. Choose Sygnet. Per-field confidence, provenance and validation are the difference between an extraction and a decision you can defend. See insurance claims or banking and lending for concrete shapes.
• Already standardised on LlamaIndex. Stay with LlamaParse unless you have a review-and-audit requirement it does not cover. The direct integration removes a whole class of glue code.
• EU data residency and a strict no-training stance. Both are credible options. LlamaParse offers an EU instance hosted on AWS in Frankfurt, though its EU DPA covers EU-to-US transfers including access by US-based personnel. Sygnet runs only in europe-west1. Read both DPAs properly before deciding.
• Unpredictable mix of document types and page counts. Compare the two billing models against your real file mix. Credit-based pricing rewards cheap tiers on simple pages; per-document pricing rewards long files. Our ROI calculator helps with the second case, and the cost-per-correct-answer framing is covered in more detail in this post.

FAQ

Can LlamaParse replace an IDP platform?

For some workloads, yes. Extract returns JSON in your schema, and Classify and Split handle sorting and segmenting, which covers a lot of ground. What you build yourself is the operational layer: thresholds, validation, a review queue, and an audit trail. If that layer is small for you, the parsing service is enough. If it is large, buying it is usually cheaper than maintaining it.

Does either tool give per-field confidence scores?

Sygnet returns a confidence score and bounding box for every field. For LlamaParse, we found layout and bounding boxes documented for parsing output, but we did not find documented per-field confidence scoring for Extract, so treat that as unverified and ask the vendor directly rather than relying on this page.

How do the two pricing models actually compare?

They are not directly comparable without your file mix. LlamaParse bills credits per page, at 1, 3, 10 or 45 credits per page depending on tier, with credits at $1.25 per 1,000. Sygnet bills per document regardless of length. Short documents favour the page model; long ones favour the document model. Run both on a real sample.