Process language seamlessly with this NLP platform
(18 ratings)
Starts from $1Flat Rate Per Job, also offers free forever plan
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Amazon Comprehend is a Natural Language Processing (NLP) Software that extracts structured information from text. It helps developers build applications that make sense of large volumes of unstructured data, such as text documents and PDFs. With Amazon Comprehend, user can ... Read More
Starts from $1, also offers free forever plan
Yearly plans
Show all features
Free Tier
For each of the 7 APIs (Key Phrase Extraction, Sentiment Analysis, Entity Recognition, Language Detection, Syntax Analysis, Custom Entities, and Custom Classification) per month.
Key Phrase Extraction
Sentiment Analysis
Entity Recognition
Language Detection
Syntax Analysis
Topic Modeling
$1
Flat Rate Per Job
For the first 100 MB: $1 Flat Rate Per Job
For every MB above 100 MB: $0.004 Per MB
Key Phrase Extraction
Sentiment Analysis
Entity Recognition
Language Detection
Syntax Analysis
Custom Comprehend
For asynchronous classification and entity recognition: $0.0005 Per Unit
For synchronous classification: $0.0005 Per Second
For Model Training: $3 Per Hour
For Custom Model Management: $0.50
Key Phrase Extraction
Sentiment Analysis
Entity Recognition
Language Detection
Screenshot of the Amazon Comprehend Pricing Page (Click on the image to visit Amazon Comprehend 's Pricing page)
Disclaimer: Pricing information for Amazon Comprehend is provided by the software vendor or sourced from publicly accessible materials. Final cost negotiations and purchasing must be handled directly with the seller. For the latest information on pricing, visit website. Pricing information was last updated on .
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Location
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Amazon Comprehend is a Natural Language Processing (NLP) Software that extracts structured information from text. It helps developers build applications that make sense of large volumes of unstructured data, such as text documents and PDFs. With Amazon Comprehend, user can use natural language processing (NLP) to automatically identify entities, topics, and sentiments in text data. This enables new types of applications relying on a deeper understanding of data.
Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].
Researched by Rajat Gupta
