Unsiloed AI vs Azure Document Intelligence
Both turn complex documents into structured data. Unsiloed ranks #1 on olmOCR-Bench, returns typed JSON from a schema you define in one call, and runs the same API from cloud to air-gapped. Here is how it compares to Azure on accuracy, output, and cost.
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What Unsiloed does that Azure can't
Unsiloed wins on extraction accuracy, output shape, and deployment reach. Azure fits when your documents already match its prebuilt models and you are committed to the Microsoft stack.
Unsiloed
Extraction
One custom JSON schema in the request, typed output on the first call
No schema at request time. Match a prebuilt model, train a custom one, or bolt on query fields
Typed, nested JSON that matches your schema exactly
Query fields return a flat list; nested structures mean training a model
Extract new fields on the first call, nothing to label
Custom fields need a labeled training set or the query-fields add-on
Output & RAG
Structure-aware chunks with reading order preserved, ready to embed
Returns Layout Markdown you still have to chunk yourself
Confidence, bounding box, and word-level citation on every field
Confidence and bounding regions, but the shape varies by model
Formats
Extract from Office files, PDFs, and scans on one surface
Prebuilt and custom extraction take PDF and images only
20+ formats through the same API, parsing and extraction
Support is split across Read, Layout, prebuilt, and custom models
Operations & setup
All four on one API, one call
Separate models, add-ons, custom training, and async jobs to stitch together
One request returns structured output
Submit, poll an operation URL, then retrieve and normalize
Enterprise & compliance
Cloud, private, on-premise, or fully air-gapped on the same API
Disconnected containers need approval, a commitment tier, and a reduced model set
Zero data retention on Standard and above
Analyze results are retained briefly for retrieval
One plan and page volume across every operation
Priced per model per page; OCR, Layout, query fields, and training bill separately
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
Extraction in one call
Define a JSON schema and send the document. Response is typed, with confidence scores, bounding boxes, and word-level citations per field. No training required.
One API, four operations
Parse, extract, classify, and split share a single composable surface. No routing between models, add-ons, and async jobs to keep the output consistent across a pipeline.
Runs where your data lives
The same API deploys to cloud, private, on-premise, or air-gapped. No separate container approval or commitment tier to process documents inside a controlled boundary.
Where the differences actually show up
- Extraction
Schema-driven vs models and query fields
With Unsiloed the extraction definition lives in the request: one custom JSON schema, one call, typed output. Azure is model-driven. Match a prebuilt model, or label examples and train a custom model, or add query fields. Query fields skip training but work as a flat list of named fields, and Microsoft suggests key-value pairs past about 20 fields. 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, and Office files; prebuilt and custom extraction take PDFs and images. A DOCX can go through Layout but needs conversion for field extraction. Unsiloed parses and extracts across 20+ formats through one surface, and returns hierarchical chunks and schema-shaped JSON directly, with no reassembly layer.
- Accuracy
Benchmark transparency
Unsiloed publishes its result on olmOCR-Bench, a public benchmark, and ranks first at 88.0. Azure does not publish a directly comparable public benchmark for Document Intelligence, so evaluating it means testing on your own document set, especially for custom extraction or mixed formats.
- Integration
Who owns the pipeline logic
Azure analyze jobs are asynchronous: submit a document, poll an operation URL, retrieve the JSON, then normalize output shapes across models. Unsiloed returns structured output in one call. The real difference is ownership. With Azure your team owns the routing between models and add-ons. With Unsiloed the API boundary sits closer to the object your downstream system expects.
- Security
Deployment and data use
Neither service trains on customer documents. Azure retains analyze results briefly for retrieval and offers disconnected containers for air-gapped use, behind approval and commitment-tier licensing with a reduced model set. Unsiloed runs the same API from cloud to air-gapped, with zero data retention on Standard and above.
- Pricing
One plan vs several billing lines
Azure prices per model per page, so OCR, Layout, query fields, custom extraction, and training can become separate cost lines. Read-only OCR can be cheap; structured extraction is where the lines add up. Unsiloed prices by plan and page volume across parsing, extraction, classification, and splitting. Confirm current Azure rates on their pricing page before budgeting.
88.0 vs 48.7
olmOCR-Bench overall accuracy. Unsiloed Parser ranks #1; Azure Document Intelligence scores 48.7. A 39.3-point gap on a public, rerunnable benchmark.
Who should pick which
Different tools fit different pipelines. Here is the honest split.
Choose Unsiloed if…
- Document output has to feed LLMs, agents, or RAG without a custom assembly layer
- You need typed JSON from a custom schema, with no model to train first
- You want confidence scores, bounding boxes, and citations on every extracted field
- You extract from Office files, PDFs, scans, and mixed batches through one API
- You need cloud, private, on-premise, or air-gapped on the same API
Azure may fit if…
- Your documents match its prebuilt models: invoices, receipts, IDs, tax forms
- You are already invested in the Microsoft and Azure ecosystem
- Your team is comfortable assembling output from model-specific JSON
- Your main need is high-volume raw OCR rather than nested structured extraction
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.
Frequently
Asked
- Choose Azure if your documents match its prebuilt models, you want Microsoft's managed stack, and your team can assemble model-specific output. Choose Unsiloed if you need request-time schema extraction, RAG-ready chunks, field-level citations, Office-format extraction, and one surface for parsing, extraction, classification, and splitting.
- Not at request time. Azure extraction is model-driven: you match a prebuilt model, label examples and train a custom model, or add query fields, which return a flat list of named fields. Unsiloed takes one custom JSON schema in the request and returns typed, nested output on the first call.
- Yes, with different trade-offs. Azure offers disconnected containers behind approval and commitment-tier licensing, with a reduced model set. Unsiloed runs the same API across cloud, private, on-premise, and fully air-gapped deployments.
- Azure support depends on the model: Read and Layout take PDFs, images, and Office files, while prebuilt and custom extraction take PDFs and images only. Unsiloed parses and extracts across 20+ formats, including DOCX, XLSX, and PPTX, through one API.
- Azure Layout returns Markdown that you still chunk yourself. Unsiloed returns structure-aware chunks with reading order preserved, ready to embed, plus confidence, bounding boxes, and word-level citations on every extracted field.
- Azure prices per model per page, so OCR, Layout, query fields, custom extraction, and training can become separate cost lines. Unsiloed prices by plan and page volume across parsing, extraction, classification, and splitting. Confirm current Azure rates on their pricing page before budgeting.
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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.