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  4. Search As A Service
  5. Amazon Kendra vs Azure Cognitive Search

Amazon Kendra vs Azure Cognitive Search

OverviewComparisonAlternatives

Overview

Amazon Kendra
Amazon Kendra
Stacks53
Followers143
Votes0
Azure Cognitive Search
Azure Cognitive Search
Stacks39
Followers67
Votes1

Amazon Kendra vs Azure Cognitive Search: What are the differences?

Amazon Kendra and Azure Cognitive Search are two popular search solutions offered by cloud service providers. Here are the key differences them.

  1. Natural Language Processing (NLP) capabilities: Amazon Kendra provides advanced NLP capabilities, including intent detection, entity recognition, and language detection. These NLP features enable Kendra to understand and interpret complex queries, enhancing the search results accuracy. On the other hand, Azure Cognitive Search offers basic NLP functionalities such as language detection and tokenization but lacks advanced features like intent detection.

  2. Built-in connectors and ingestion: Amazon Kendra offers pre-built connectors for various data sources like Amazon S3, SharePoint, and Salesforce, making it easier to ingest and index data from different sources. In contrast, Azure Cognitive Search requires custom development or the use of third-party connectors to ingest data from different sources.

  3. Knowledge Graph support: Amazon Kendra supports knowledge graphs, allowing users to create relationships between entities and leverage this connected data within search results. This feature enables Kendra to provide more contextual and relevant search results. However, Azure Cognitive Search does not provide a built-in support for knowledge graphs, limiting its ability to offer similar contextual search experiences.

  4. Pricing model: Amazon Kendra follows a pay-per-query pricing model, where users are charged based on the number of queries executed. On the other hand, Azure Cognitive Search primarily follows a capacity-based pricing model where users are billed based on the document processing units (DPUs) and data storage. This difference in pricing models allows users to choose a model that aligns with their search usage requirements and budget.

  5. Analytics and monitoring: Amazon Kendra provides detailed analytics and monitoring capabilities, allowing users to gain insights into search usage, query performance, and user behavior. These analytics are valuable for optimizing search performance and improving the overall search experience. In contrast, Azure Cognitive Search offers limited analytics and monitoring capabilities compared to Amazon Kendra.

  6. Machine Learning capabilities: Amazon Kendra incorporates machine learning algorithms to continuously improve search relevance and query understanding over time. It utilizes techniques like click-through analysis and user feedback to refine search results. On the other hand, Azure Cognitive Search does not have similar built-in machine learning capabilities.

In summary, Amazon Kendra stands out with its advanced NLP capabilities, built-in connectors, knowledge graph support, pay-per-query pricing model, extensive analytics and monitoring, and incorporation of machine learning algorithms. Azure Cognitive Search, while providing basic NLP functionalities and customization options, falls behind in terms of advanced NLP capabilities, built-in connectors, knowledge graph support, pricing model, analytics, and machine learning integration.

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Detailed Comparison

Amazon Kendra
Amazon Kendra
Azure Cognitive Search
Azure Cognitive Search

It is a highly accurate and easy to use enterprise search service that’s powered by machine learning. It delivers powerful natural language search capabilities to your websites and applications so your end users can more easily find the information they need within the vast amount of content spread across your company.

It is the only cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. Formerly known as Azure Search, it uses the same integrated Microsoft natural language stack that Bing and Office have used for more than a decade and AI services across vision, language and speech. Spend more time innovating and less time maintaining a complex cloud search solution.

Natural language & keyword support; Reading comprehension & FAQ matching; Document ranking; Connectors; Relevance tuning; Domain optimization
Start, maintain and scale with minimal investment;Create searchable content using integrated AI;Customise to meet goals and industry requirements
Statistics
Stacks
53
Stacks
39
Followers
143
Followers
67
Votes
0
Votes
1
Pros & Cons
Cons
  • 3
    Expensive
Pros
  • 1
    111
Integrations
Dropbox
Dropbox
Bootstrap
Bootstrap
React
React
AWS IAM
AWS IAM
Amazon VPC
Amazon VPC
Box
Box
Microsoft SharePoint
Microsoft SharePoint
Amazon RDS
Amazon RDS
TypeScript
TypeScript
Salesforce Sales Cloud
Salesforce Sales Cloud
Postman
Postman
Java
Java
Node.js
Node.js
Python
Python
C#
C#
PowerShell
PowerShell

What are some alternatives to Amazon Kendra, Azure Cognitive Search?

Elasticsearch

Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Swiftype

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

MeiliSearch

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

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