Unsiloed AI vs LlamaParse
Unsiloed parses documents more accurately than LlamaParse. It tops olmOCR-Bench at 88.0 to LlamaParse’s 73.5, scores and cites every value, and runs on-premise or fully air-gapped.
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Where Unsiloed makes the difference
Unsiloed wins the layer that sets the ceiling for everything downstream: parsing accuracy and field-level auditability. Here is where that shows up against LlamaParse.
Unsiloed
Parsing accuracy
Ranked #1 at 88.0, the highest score published for the benchmark
Trails at 73.5 on the same scorer, in its agentic mode
93.2 on the olmOCR-Bench table category
85.3 on the same run
94.6: page headers, footers, and page numbers stay out of the body text
18.3 on the same run, so running headers leak into the chunks you retrieve
Extraction & auditability
A 0 to 1 confidence score and a page and bounding-box citation on every value, with no measurable slowdown
Turning on confidence scores and citations significantly slows extraction
Platform & deployment
Cloud, VPC, on-premise, or fully air-gapped
Self-hosting on Enterprise, running on your own LLM provider keys
One per-page rate on every plan, with no credits to convert
Credits from 1 to 60 per page depending on product, tier, and add-ons
Prove it on the document that broke your last pipeline
Bring a file your current tool mangles: a merged-cell table, a scanned filing, a mixed packet. Run it through Unsiloed and see typed, cited output in minutes.
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Structured output, not a toolkit to assemble
Accuracy at the source
Retrieval is only as good as the parser feeding it. Unsiloed leads olmOCR-Bench at 88.0 with a vision-first read that keeps tables, reading order, and layout intact, and keeps running headers and footers out of your chunks.
Auditable at the source
Every value carries a 0 to 1 confidence score and a bounding-box citation to its source page. Reviewers check only what scores low and jump straight to where it came from.
Deploys where data can't leave
Unsiloed runs on-premise and in fully air-gapped networks, so documents never touch a public cloud. Processing stays inside the boundary your compliance team sets.
Where the differences actually show up
- Accuracy
The parser sets the ceiling
Every framework and vector database downstream inherits whatever the parser hands it. Flatten a table or leak a running header into a chunk, and no query engine gets it back. Unsiloed leads olmOCR-Bench at 88.0 against LlamaParse's 73.5 on the same unmodified scorer, with the widest gap on headers and footers: 94.6 to 18.3. That gap is the ceiling on everything you build on top.
- Auditability
Confidence and citations at full speed
LlamaExtract can return confidence scores and citations, but turning them on significantly slows extraction, so they get switched on only when the metadata is essential. Unsiloed returns a 0 to 1 score and a bounding-box citation on every value with no measurable change in processing time, so you can keep verification on for every document. For compliance and audit trails, that is the difference between shippable and not.
- Integration
Works with your existing stack
Unsiloed replaces the parse and extract step, not your retrieval code. Its chunks and JSON drop into whichever RAG framework and vector store you already run, so the retrieval layer finally gets clean, structured input.
- Deployment
Deployment where data cannot leave
Self-hosted LlamaParse is an Enterprise option that runs on your Kubernetes cluster and calls out to your own LLM provider accounts, such as OpenAI, Anthropic, or Google. Unsiloed runs on-premise and fully air-gapped, for environments where data cannot touch a public cloud at all.
- Pricing
Published plans, no credit math
Unsiloed pricing is public: Standard is $250/mo for up to 20,000 pages ($0.012/page), Growth is $750/mo for up to 75,000 pages ($0.010/page), and Enterprise covers VPC, on-premise, and air-gapped. LlamaParse runs on credits, 1,000 at $1.25, with parsing from 1 to 45 credits per page and extraction up to 60. Confirm current rates on each pricing page.
88.0 vs 73.5
olmOCR-Bench overall accuracy. Unsiloed Parser ranks #1; LlamaParse scores 73.5 in its agentic mode. A 14.5-point gap on a public, rerunnable benchmark.
Who Unsiloed is built for
Teams choose Unsiloed when the document layer has to be right.
Choose Unsiloed if…
- Parsing accuracy on complex documents is mission-critical
- You need a confidence score and citation on every field
- You operate in finance, legal, or healthcare with audit requirements
- You require on-premise or air-gapped deployment
- You want accurate parsing and extraction from one API, without credit math
Unsiloed handled edge cases that consistently broke other systems, particularly the ones around nested tables and different formats.
- Retrieval is only as good as the parser feeding it. Unsiloed uses a vision-first read that keeps tables, columns, and reading order intact. On olmOCR-Bench, with the unmodified Allen AI scorer, Unsiloed ranks #1 at 88.0 against LlamaParse's 73.5, and leads on tables (93.2 to 85.3) and on keeping headers and footers out of the text (94.6 to 18.3).
- Yes. Every parsed segment and word comes back with its page and bounding box by default, and every extracted value carries a 0 to 1 confidence score. Turn on field-level citations for extraction and they come at full speed, so you can keep them on for every document.
- Yes. Unsiloed runs on-premise and in fully air-gapped networks, so documents never touch a public cloud. Self-hosted LlamaParse is an Enterprise option that runs on your own LLM provider keys.
- No. Unsiloed replaces the parsing and extraction step and feeds LlamaIndex, LangChain, or any framework you already run. Keep your retrieval code and vector store as they are, and point them at cleaner, structured input.
- Upload a document and call the API to get structured output in minutes. Parse, extract, classify, and split all run on one REST API, with no credit math to plan around before anything ships.
- Unsiloed pricing is public: Standard is $250/mo for up to 20,000 pages ($0.012/page), Growth is $750/mo for up to 75,000 pages ($0.010/page), and Enterprise covers VPC, on-premise, and air-gapped. LlamaParse runs on credits, 1,000 at $1.25, with parsing from 1 to 45 credits per page and extraction up to 60. Confirm current rates on each pricing page.
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extracts from your documents
Bring your hardest document set. We will show you structured, cited output on a live call, or run a sample in the playground in minutes.