Read model

Security impact is direct because the Read model processes documents that may contain customer data, contracts, personal identifiers, or regulated records. Protect the Document Intelligence resource with Microsoft Entra-based access where supported, private endpoints, network restrictions, key rotation, and controlled storage containers. Avoid sending document URLs that are publicly reachable or long-lived. Store extracted text with the same classification as the source document, because OCR output can expose sensitive content even when the original file is locked down. Diagnostic logs should capture operational evidence without leaking document payloads, and access to model results should be separated from access to submit documents.

Cost comes from Document Intelligence transactions, storage, orchestration, logging, and downstream processing. A Read model call may be cheap compared with manual review, but costs can grow quickly when every upload, duplicate file, failed retry, or reprocessing job triggers analysis. FinOps owners should watch page counts, batch size, retry loops, retention of extracted text, and whether expensive downstream AI workflows run on every OCR result. Storing searchable PDFs, full text, and bounding data can also increase storage and index costs. Good controls include deduplication, idempotency keys, lifecycle rules, budget alerts, and separate metering for production and experimentation Chargeback tags should distinguish experimentation, production intake, and replay batches clearly.

Reliability depends on the whole document pipeline, not only the Read model. OCR calls can fail because of unsupported formats, oversized files, regional service issues, network restrictions, throttling, or downstream storage failures. Production designs should queue work, use idempotent job IDs, retry safely, preserve original files, and track analysis status until completion. Operators need a dead-letter path for documents that repeatedly fail and a way to replay batches after service recovery. If Read output feeds search or AI answers, stale or missing OCR results can quietly reduce quality, so monitor completion rates, latency, errors, and backlog depth Backlog alarms should include owner, source container, and replay instructions.

Performance depends on document size, page count, image quality, regional latency, service throttles, and downstream processing. The Read model is often part of an asynchronous pipeline, so operators should measure end-to-end processing time, not only API response time. Poor scans, large PDFs, retries, or over-parallelized batch jobs can create backlogs and delay search indexing or approvals. Co-locating storage, compute, and Document Intelligence resources reduces unnecessary latency. Use queues to smooth spikes, parallelize within documented limits, and capture timing for upload, analysis submission, completion polling, result storage, and downstream enrichment so bottlenecks are visible Baseline runs with representative files prevent unrealistic expectations from tiny demo documents.

Operators inspect Read model workflows through Document Intelligence Studio tests, API responses, diagnostic logs, storage queues, Function logs, and downstream records. Common work includes validating file formats, checking resource keys or identities, confirming private endpoint DNS, reviewing failed operation IDs, and comparing extracted text against source documents. Teams should document accepted file types, maximum sizes, retry rules, retention policy, and escalation paths for low-confidence output. For large batches, operators also track throughput, throttling, regional capacity, and processing backlog. A good runbook explains how to replay one document without duplicating results or losing audit history Versioned test documents should remain available for regression checks after model or workflow changes.