Amazon Comprehend
Integrations
- Amazon Bedrock (Nova/Titan Models)
- Amazon S3
- AWS Lambda
- Amazon Connect
- AWS Macie
- AWS Glue
Pricing Details
- Standard API calls are billed per 100-character unit ($0.0001).
- Custom endpoints are billed per Inference Unit (IU) at $0.0005 per second, providing 100 characters/sec throughput.
Features
- Contextual PII Detection (36 types)
- Bedrock Data Automation (PDF/Image Support)
- Low-code CER (25 annotations per entity)
- Automated Model Lifecycle Flywheels
- Targeted Entity-level Sentiment Analysis
- Native S3 Object Lambda Redaction
Description
Amazon Comprehend: Neural-Symbolic IDP & Bedrock Orchestration Review (2026)
Amazon Comprehend functions as a multi-tenant NLU orchestration layer within the AWS AI ecosystem. In 2026, the service acts as a primary Information Extraction (IE) node, grounding generative outputs from Amazon Bedrock in verifiable linguistic metadata 📑. The underlying transformer weights remain opaque to prevent prompt-injection reverse engineering 🌑.
Semantic Extraction & PII Governance
- Low-Code Entity Recognition: Custom Entity Recognition (CER) has been optimized for the 2026 developer cycle, requiring a minimum of only 25 annotations and 3 documents per entity type 📑.
- PII Identification & Redaction: Identifies 36 specific PII entity types across 50+ languages. Redaction is supported natively for asynchronous jobs or via S3 Object Lambda access points for real-time masking 📑.
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Bedrock Data Automation & Agentic Logic
The 2026 architectural pattern utilizes Amazon Bedrock Data Automation to linearize PDFs and images before routing them to Comprehend's specialized NLU engines 📑.
- Automated Flywheels: Manages the lifecycle of custom classifiers, utilizing active learning to retrain models on curated S3 datasets without manual intervention 📑.
- Targeted Sentiment: Unlike document-level scoring, the engine maps sentiment to 25+ specific entity types, enabling granular feedback loops for consumer-facing agents 📑.
Evaluation Guidance
Technical evaluators should verify the following architectural characteristics:
- Payload Constraints: Benchmark application performance against the 20 KB synchronous request limit for real-time text analysis to ensure sub-second response times [Documented].
- Language-Format Parity: Validate that Custom Entity Recognition for PDF/Word documents is sufficient for your project, as these formats currently support English only [Documented].
- Inference Unit (IU) Throttling: Organizations must benchmark provisioned endpoint performance under peak load, as throughput is metered at 100 characters/second per IU [Inference].
Release History
Year-end update: Integration with AWS Agents. Comprehend now serves as a reasoning engine to structure unstructured data for autonomous AI agents.
Major update to PII (Personally Identifiable Information) identification. New contextual detection for 35+ entity types across 50+ languages.
Integration with Amazon Bedrock. Enables generative summarization of extracted insights and 'Zero-shot' classification using Titan and Anthropic models.
Launch of Flywheels. Automated pipeline for continuous model retraining and version management for custom NLU tasks.
Introduction of Targeted Sentiment. Provides granular sentiment analysis towards specific entities (e.g., 'the food was great but the service was slow').
Release of Custom Entities and Custom Classification. Users can now train models on their own specific datasets without ML expertise.
Launch of specialized HIPAA-eligible service for healthcare data. Automatic extraction of medical conditions, medications, and dosages.
Initial launch. Provided managed NLP for entity recognition, key phrase extraction, sentiment analysis, and topic modeling.
Tool Pros and Cons
Pros
- Powerful NLP
- Seamless AWS integration
- Pre-trained models
- Fast development
- Accurate entity detection
- Sentiment analysis
- Quick topic extraction
- Easy text processing
Cons
- Potentially costly
- Requires AWS knowledge
- Custom model training
