Algolia vs Amazon Kendra

Overview

Algolia

Stacks1.4K

Followers1.1K

Votes699

Amazon Kendra

Stacks53

Followers143

Votes0

Algolia vs Amazon Kendra: What are the differences?

Key Differences between Algolia and Amazon Kendra

Search Capability: Algolia is primarily a search-as-a-service platform that provides real-time indexing and querying capabilities for developers, focusing on delivering fast and relevant search results to users. On the other hand, Amazon Kendra is an intelligent search service powered by machine learning algorithms, designed for enterprise-level search applications that can index a wide variety of data sources and provide natural language search capabilities.
Pricing Model: Algolia's pricing is based on the number of records indexed and the number of operations performed, with various pricing tiers available depending on the scale of usage. In contrast, Amazon Kendra follows a pay-as-you-go pricing model based on the volume of data processed and the complexity of queries, making it more suitable for organizations with fluctuating search requirements.
Customization and Control: Algolia offers extensive customization options, allowing developers to fine-tune search relevance and user experience through features like query rules, synonyms, and personalization. Amazon Kendra, while providing some customization capabilities, is more focused on leveraging machine learning models to automatically improve search results and relevance over time, with less granular control for developers.
Deployment Flexibility: Algolia can be easily integrated into a wide range of web and mobile applications via SDKs, APIs, and plugins, making it suitable for a variety of use cases and platforms. Amazon Kendra, being a managed service on AWS, offers seamless integration with other Amazon Web Services such as S3, RDS, and Lambda, enabling customers to leverage existing AWS infrastructure for search capabilities.
Language Support: Algolia supports a wide range of languages out of the box, making it suitable for global applications that require multilingual search capabilities. Amazon Kendra also offers multilingual support; however, the effectiveness may vary depending on the language models trained as part of the machine learning process.
Industry Focus: While Algolia caters to a broad range of industries and use cases, including e-commerce, media, and SaaS applications, Amazon Kendra is specifically tailored for knowledge-intensive industries like healthcare, finance, and legal sectors where accurate and insightful search results are critical for decision-making.

In Summary, Algolia and Amazon Kendra differentiate in search capability, pricing model, customization, deployment flexibility, language support, and industry focus.

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Advice on Algolia, Amazon Kendra

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

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Comments

Detailed Comparison

Algolia	Amazon Kendra
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.	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.
Database search;Multi-attributes;Search as you type;Analytics dashboard; Ranking; Mobile; Search in any language;Understand users mistakes;Smart Highlighting;Realtime indexing;Protect your indexes from misuse;Discover realtime faceting;Search objects by location	Natural language & keyword support; Reading comprehension & FAQ matching; Document ranking; Connectors; Relevance tuning; Domain optimization
Statistics
Stacks 1.4K	Stacks 53
Followers 1.1K	Followers 143
Votes 699	Votes 0
Pros & Cons
Pros 126 Ultra fast 95 Super easy to implement 73 Modern search engine 71 Excellent support 70 Easy setup, fast and relevant Cons 11 Expensive	Cons 3 Expensive
Integrations
React Ruby Jekyll JavaScript React Native Vue.js WordPress Shopify Docusaurus VuePress	Dropbox Bootstrap React AWS IAM Amazon VPC Box Microsoft SharePoint Amazon RDS TypeScript Salesforce Sales Cloud

What are some alternatives to Algolia, Amazon Kendra?

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).

Solr

Solr is the popular, blazing fast open source enterprise search platform from the Apache Lucene project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites.

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 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 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

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

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

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.

Related Comparisons

Algolia vs Amazon Kendra: What are the differences?

Key Differences between Algolia and Amazon Kendra

Search Capability: Algolia is primarily a search-as-a-service platform that provides real-time indexing and querying capabilities for developers, focusing on delivering fast and relevant search results to users. On the other hand, Amazon Kendra is an intelligent search service powered by machine learning algorithms, designed for enterprise-level search applications that can index a wide variety of data sources and provide natural language search capabilities.
Pricing Model: Algolia's pricing is based on the number of records indexed and the number of operations performed, with various pricing tiers available depending on the scale of usage. In contrast, Amazon Kendra follows a pay-as-you-go pricing model based on the volume of data processed and the complexity of queries, making it more suitable for organizations with fluctuating search requirements.
Customization and Control: Algolia offers extensive customization options, allowing developers to fine-tune search relevance and user experience through features like query rules, synonyms, and personalization. Amazon Kendra, while providing some customization capabilities, is more focused on leveraging machine learning models to automatically improve search results and relevance over time, with less granular control for developers.
Deployment Flexibility: Algolia can be easily integrated into a wide range of web and mobile applications via SDKs, APIs, and plugins, making it suitable for a variety of use cases and platforms. Amazon Kendra, being a managed service on AWS, offers seamless integration with other Amazon Web Services such as S3, RDS, and Lambda, enabling customers to leverage existing AWS infrastructure for search capabilities.
Language Support: Algolia supports a wide range of languages out of the box, making it suitable for global applications that require multilingual search capabilities. Amazon Kendra also offers multilingual support; however, the effectiveness may vary depending on the language models trained as part of the machine learning process.
Industry Focus: While Algolia caters to a broad range of industries and use cases, including e-commerce, media, and SaaS applications, Amazon Kendra is specifically tailored for knowledge-intensive industries like healthcare, finance, and legal sectors where accurate and insightful search results are critical for decision-making.

In Summary, Algolia and Amazon Kendra differentiate in search capability, pricing model, customization, deployment flexibility, language support, and industry focus.

Algolia vs Amazon Kendra

Overview