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Quick Answer

AI Content Classification Tools for 1,000–10,000 Employees

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The short answer

Classify by job: one tool for sensitive data at rest, one for routing incoming documents. For a company of 1,000 to 10,000 people, the right choice follows your cloud and productivity suite. As of October 21, 2026:

  • Microsoft Purview — sensitivity labels and DLP across Microsoft 365, Windows and macOS; the default for Microsoft shops.
  • Amazon Macie — sensitive-data discovery in S3.
  • Google Sensitive Data Protection — discovery and inspection across BigQuery, Cloud SQL and Cloud Storage.
  • Azure Document Intelligence / Google Document AI custom classifiers — route incoming documents by type at $3–$5 per 1,000 pages.
  • Reducto Classify — classification inside a parse-and-extract pipeline for messy documents.

The comparison

ToolClassifiesPrice (list, Oct 21, 2026)Best fit
Microsoft PurviewSensitive info types, trainable classifiers, sensitivity labelsPer-user E3/E5 licences; pay-as-you-go meters per asset scanned for on-demand classification and non-M365 sourcesMicrosoft 365 estates
Amazon MaciePII, financial data, credentials in S3$0.10 per bucket/month · $0.01 per 100K objects monitored · $1 per GB inspected (1 GB free for automated discovery) · 30-day trialAWS data lakes
Google Sensitive Data ProtectionPII and custom infoTypes in GCP dataDiscovery $0.03 per GB profiled (BigQuery capped at 3 TB per table) or $2,500 per subscription unit; inspection billed per GBGoogle Cloud data
Azure Document IntelligenceDocument type (custom classifier)$3 per 1,000 pagesIncoming mail and forms, Microsoft
Google Document AIDocument type (custom classifier and splitter)$5 per 1,000 pages ($3 above 1M)Incoming documents, Google
Reducto ClassifyDocument type (first 5 pages)$7.50 per 1,000 pages · Deep Classify $15Messy scans, then extraction
Amazon Comprehend custom classificationText categoriesAsync $0.0005 per 100-character unit; real-time endpoints $0.0005 per second per inference unit; training $3/hourFree-text tickets and emails on AWS

Which tool for which job

Labelling sensitive content in Microsoft 365. Purview is already in most E5 estates. Microsoft’s billing documentation describes two complementary models: per-user licensing for Microsoft 365 and endpoints, and pay-as-you-go meters that extend classification to non-Microsoft sources and to on-demand scans of SharePoint and OneDrive files that were never classified. Run the in-product cost estimate before a large on-demand scan.

Finding personal data in cloud storage. Macie and Google Sensitive Data Protection both separate cheap profiling (which tables or buckets probably hold sensitive data) from expensive inspection (reading the bytes). Use profiling everywhere, then targeted inspection on what it flags. On AWS, Macie’s own example shows a 150 GB account costing about $151.50 a month for automated discovery.

Routing incoming documents. If documents arrive as scans or PDFs (claims, invoices, HR forms), a document classifier in front of extraction saves money: only the right pages go to the expensive extraction model. Azure ($3 per 1,000 pages) is cheapest at list; Google’s classifier also splits multi-document PDFs; Reducto Classify is the natural choice if Reducto already extracts the fields.

Categorising text (emails, tickets, chat). Comprehend custom classification on AWS, or an LLM prompt with a fixed label set. For an LLM approach, a small model such as Claude Haiku 5.5 or GPT-6 Luna (both $0.10 per million input tokens; see current API prices) is usually cheaper than a hosted classifier at mid-size volume.

How to choose

  1. List your three classification jobs (sensitive data, document routing, text categories) — few companies need all three on day one.
  2. Use the platform you already license for sensitive data: Purview for Microsoft, Macie for AWS, Sensitive Data Protection for Google.
  3. Test document classifiers on 300 real documents across your top ten types; measure accuracy and how many land in “unknown”.
  4. Budget profiling, not full inspection, for data at rest.

Related: the hub on AI document analysis platforms for 1,000–10,000 employees and best enterprise data extraction tools for mid-size companies.

Last verified: October 21, 2026.

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