Unsiloed AI vs LlamaParse
LlamaIndex gives you a framework to assemble. Unsiloed gives you the parsing and extraction layer it depends on, done better. It tops olmOCR-Bench at 88.0, cites and scores every field by default, and runs on-premise or air-gapped, the three places LlamaParse falls short in production.
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What Unsiloed does that LlamaIndex can't
Unsiloed wins the layer that sets the ceiling for everything downstream: parsing accuracy and field-level auditability. LlamaIndex is the framework you keep for orchestration and retrieval, sitting behind Unsiloed, not in its place.
Unsiloed
Parsing accuracy
Ranked #1 at 88.0 — the highest score on the open benchmark, outright
Trails at 73.5 on the same scorer
Reads tables, columns, and reading order like a human and keeps them intact
Flattens split tables and dense layouts, and retrieval inherits the loss
Extraction & auditability
A 0 to 1 score on every value, on by default — no flag to set
Opt-in only, and still in beta
A bounding box to the exact page on every value, always on
Opt-in, and off by default
Full speed with confidence and citations enabled
Considerably slower once enabled, per their own docs
One ingestion layer
All four in one REST API, one call
Considerably slower once enabled, per their own docs
Upload and call the API — structured output in minutes
Assemble connectors, pipelines, and retrieval before anything ships
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. Nothing is lost before retrieval even starts.
Auditable by default
Every value returns a 0 to 1 confidence score and a bounding-box citation to its source page. No opt-in flag, no beta feature, no speed penalty. In LlamaIndex, that costs you a slower pipeline and a beta toggle.
Deploys where data can't leave
The same API runs on-premise and in air-gapped networks, so documents never touch a public cloud. LlamaCloud self-hosting keeps data in a cloud tenant. Unsiloed goes all the way to isolated.
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 drop a header before retrieval runs, and no query engine gets it back. Unsiloed leads olmOCR-Bench at 88.0 against LlamaParse's 73.5 on the same unmodified scorer. That gap is the ceiling on everything you build on top.
- Auditability
Confidence and citations, always on
LlamaExtract can return confidence and citations, but both are opt-in, the confidence signal is in beta, and LlamaIndex's own docs say turning them on considerably slows extraction. Unsiloed returns a 0 to 1 score and a bounding-box citation on every value by default, at full speed. For compliance and audit trails, that is the difference between shippable and not.
- Architecture
Unsiloed sits upstream, not against you
This is not a framework swap. LlamaIndex owns indexing, retrieval, and query logic; Unsiloed owns the parse and extract step that feeds them. Keep LlamaIndex, LangChain, or any framework downstream and put Unsiloed in front, so the retrieval layer finally gets clean, structured input.
- Deployment
Cloud tenant vs truly air-gapped
LlamaCloud offers bring-your-own-cloud self-hosting, but only on Enterprise, and it runs in your cloud tenant. Unsiloed adds on-premise and air-gapped deployment for environments where data cannot touch a public cloud at all, on the same API you use everywhere else.
- 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. LlamaCloud runs on credits, 1,000 at $1.25, with parsing from 1 to 45 credits per page. Confirm current rates on each pricing page.
88.0 vs 73.5
olmOCR-Bench overall accuracy. Unsiloed Parser ranks #1; LlamaParse scores 73.5. A 14.5-point gap on a public, rerunnable benchmark.
Who should pick which
Different tools fit different pipelines. Here is the honest split.
Choose Unsiloed if…
- Parsing accuracy on complex documents is mission-critical
- You need a confidence score and citation on every field, by default
- You operate in finance, legal, or healthcare with audit requirements
- You require on-premise or air-gapped deployment
- You want the ingestion layer solved, not assembled
LlamaIndex fits while you prototype
- You are prototyping and iterating on retrieval logic
- Parsing accuracy is not yet mission-critical
- You want to assemble and own the full open-source stack
- Audit trails and controlled deployment are not required yet
- You are ready to maintain the pipeline as you scale
Unsiloed handled edge cases that consistently broke other systems, particularly the ones around nested tables and different formats.
Frequently
Asked
- Retrieval is only as good as the parser feeding it. Unsiloed uses a vision-first read that keeps tables, columns, and reading order intact, where LlamaParse flattens split tables and dense layouts. On olmOCR-Bench, with the unmodified Allen AI scorer, Unsiloed ranks #1 at 88.0 against LlamaParse's 73.5.
- Yes. Every value returns a 0 to 1 confidence score and a bounding-box citation to its source page, on by default and at full speed. In LlamaExtract both are opt-in, the confidence signal is in beta, and LlamaIndex's own docs say turning them on considerably slows extraction.
- Yes. The same API runs on-premise and in air-gapped networks, so documents never touch a public cloud. LlamaCloud offers bring-your-own-cloud self-hosting only on Enterprise, and it runs in your cloud tenant.
- No, and it does not try to be. LlamaIndex is a full RAG framework covering indexing, retrieval, and query logic. Unsiloed replaces the parsing and extraction layer that feeds it. Most teams keep a framework downstream for retrieval and put Unsiloed upstream so that layer finally gets accurate, structured input.
- Upload a document and call the API to get structured output in minutes. Parse, extract, classify, and split all run in one REST API, so there are no connectors, pipelines, or retrieval to assemble 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. LlamaCloud runs on credits, 1,000 at $1.25, with parsing from 1 to 45 credits per page. Confirm current rates on each pricing page.
Discover how we stand out from the competition
See what Unsiloed
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.