Unsiloed AI vs Azure Document Intelligence
Unsiloed scores 88.0 to Azure’s 48.7 on olmOCR-Bench, extracts typed JSON from a schema you define in the request with no model to train, and runs from managed cloud to air-gapped. Here is how it compares to Azure on accuracy, output, and cost.
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Where Unsiloed makes the difference
Unsiloed leads on parsing accuracy, request-time schemas, and deployment reach. Here is where that shows up in a production pipeline.
Unsiloed
Extraction
Send a JSON schema with the document; get typed values back
Match a prebuilt model, train a custom one, or add up to 20 query fields
Nested objects and arrays shaped by your schema
Query fields take a flat list of names; nested structures come from prebuilt or trained models
Change the schema and extract new fields immediately, nothing to label
Custom fields need a labeled training set or the paid query-fields add-on
Output & RAG
Structure-aware chunks with reading order and source coordinates, straight from the parser
Layout returns Markdown; chunking happens downstream in a separate integration
Accuracy & formats
93.2 on the table category
87.6 for Azure prebuilt-layout
Extract from Word, Excel, PowerPoint, PDFs, images, and scans on one API
Prebuilt and custom extraction take PDF and images only
Operations & setup
Four endpoints on one API, with one key and one page rate
Read, Layout, prebuilt, custom, and classification models, each with its own output shape
Enterprise & compliance
Managed cloud, VPC, on-premises, or fully air-gapped
Disconnected containers need an access request, a commitment plan, and a version-dependent model set
Zero data retention on Standard and Growth plans
Analysis data and results kept for 24 hours
One per-page rate on every plan, with no credits or paid add-ons
Priced per model per page; query fields and other add-ons cost extra, and custom training is billed past the free hours
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
Your schema, no training set
Send a document and a JSON schema, and get typed field values with a confidence score on each one. Turn on citations for page and bounding-box coordinates. Change the schema whenever your fields change, with no custom model to train.
One API, four operations
Parse, extract, classify, and split are composable endpoints on one API. Parsing returns retrieval-ready chunks; extraction returns the fields your application needs. No routing between Azure models and add-ons.
Runs where your data lives
Run Unsiloed as a managed cloud service, in your VPC, on-premises, or fully air-gapped. Keep document processing inside the boundary your compliance team requires.
Where the differences actually show up
- Extraction
Schema-driven vs models and query fields
With Unsiloed the extraction definition lives in the request: a JSON schema with nested objects and arrays, and typed output back. Azure is model-driven. Match a prebuilt model, label examples and train a custom model, or add query fields. Query fields skip training but take a flat list of names, capped at 20 per request. A nested 40-field schema for contracts or claims is where the two paths separate.
- Coverage
Document coverage and output shape
Azure support depends on the model. Read and Layout take PDFs, images, Office files, and HTML, but prebuilt and custom extraction take only PDFs and images, and query fields exclude Office files. A DOCX can go through Layout but needs conversion before field extraction. Unsiloed parses and extracts across 20+ formats through one API, including Word, Excel, and PowerPoint as well as PDFs and images, and its parser returns hierarchical chunks ready to embed.
- Accuracy
Benchmark transparency
Unsiloed scores 88.0 on olmOCR-Bench against 48.7 for Azure prebuilt-layout, on the public dataset with the upstream scorer, and leads all ten services in the run. On the table category the gap is 93.2 to 87.6. The harness and full results are public, so you can rerun it yourself.
- Integration
Who owns the pipeline logic
Both services process documents as asynchronous jobs. The difference is what your team builds around them. With Azure you choose between Read, Layout, prebuilt, and custom models, add query fields where needed, and normalize output shapes that vary by model. With Unsiloed the schema you send defines the object you get back, so the API boundary sits closer to what your downstream system expects.
- Security
Deployment and data use
Unsiloed offers zero data retention on Standard and Growth plans, and VPC, on-premises, or air-gapped deployment on Enterprise. Azure keeps cloud analysis data and results for 24 hours unless you delete them earlier through the v4.0 API. Its disconnected containers for offline use require an access request and a commitment plan, and the available models depend on the container version.
- Pricing
One rate vs several billing lines
Unsiloed charges one per-page rate on every plan, with no credits and no paid add-ons. Azure prices per model per page, so Read, Layout, prebuilt or custom extraction, query fields, and custom neural training past the first 10 free hours can each become a separate cost line. Read-only OCR can be cheap; structured extraction is where the lines add up. Confirm current Azure rates on its pricing page before budgeting.
88.0 vs 48.7
olmOCR-Bench overall score. Unsiloed scores 88.0 and leads all ten services in the run; Azure prebuilt-layout scores 48.7. A 39.3-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…
- Document output has to feed LLMs, agents, or RAG as ready-to-embed chunks
- You need typed JSON from a custom schema, with no model to train first
- You want a confidence score and a page citation on every extracted field
- You extract from Office files, PDFs, scans, and mixed batches through one API
- You need managed cloud, VPC, on-premises, or air-gapped deployment
The accuracy, especially on tables, is meaningfully better than anything we tested. We evaluated over 15 solutions and Unsiloed was the only one that worked reliably.
- Choose Unsiloed when you need higher parsing accuracy, typed JSON from a schema you define in the request, retrieval-ready chunks, and deployment anywhere up to air-gapped, without training a single custom model.
- Not at request time. Document Intelligence extracts with prebuilt or trained model schemas. Query fields let you request up to 20 named fields without training, but they take a flat list of names rather than a nested JSON schema. Unsiloed takes the schema in the request and returns typed values with a confidence score on each field.
- Yes, with different overhead. Unsiloed deploys to VPC, on-premises, or fully air-gapped environments on Enterprise. Azure disconnected containers require an access request and a commitment plan, and the supported models depend on the container version.
- Azure Read and Layout accept PDFs, images, Office files, and HTML, but prebuilt and custom extraction accept only PDFs and images. Unsiloed parses and extracts from 20+ formats through one API, including PDFs, images, scans, Word, Excel, and PowerPoint.
- Azure Layout returns Markdown and structural elements, and Azure integrations add semantic chunking downstream. Unsiloed returns ordered, structure-aware chunks with source coordinates straight from the parser, ready to embed.
- Unsiloed charges one per-page rate on every plan, with no credits or paid add-ons. Azure prices per model per page, with query fields and other add-ons billed extra and custom neural training billed by the hour after the first 10 free hours. Confirm current Azure rates before budgeting.
- Yes. Azure Form Recognizer was renamed Azure AI Document Intelligence, and Microsoft now lists it under Foundry Tools. The comparison on this page applies under any of those names.
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.