Overview

Algolia

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Followers1.1K

Votes699

Azure Cognitive Search

Stacks39

Followers67

Votes1

Algolia vs Azure Cognitive Search: What are the differences?

Introduction

In this article, we will explore the key differences between Algolia and Azure Cognitive Search, two popular search-as-a-service platforms.

Scalability: Algolia is known for its excellent scalability, allowing businesses to handle millions of queries per second with ease. On the other hand, Azure Cognitive Search also offers scalable solutions, but its scalability may not be as robust as Algolia in certain cases, especially for extremely high query volumes.
Feature Set: Algolia provides a rich set of features specifically designed for search functionality, including typo-tolerance, geo-search, customizable relevance ranking, and advanced filtering options. In contrast, Azure Cognitive Search offers a broader range of AI-powered features beyond search, such as natural language processing, text analytics, and computer vision capabilities, making it more versatile for complex use cases.
Ease of Use: Algolia is renowned for its developer-friendly interface and comprehensive documentation, allowing users to quickly and easily integrate powerful search functionality into their applications. Azure Cognitive Search, although also user-friendly, may require a bit more configuration and setup, particularly if one wants to leverage its advanced AI capabilities.
Indexing Speed: Algolia is known for its lightning-fast indexing speed, making the search results almost real-time. Azure Cognitive Search also offers efficient indexing capabilities, but the speed may vary depending on the complexity and size of the dataset. In case of large datasets or frequent updates, Algolia may have an edge in terms of indexing speed.
Search Relevance and Personalization: Algolia provides extensive tools for tweaking and fine-tuning search relevance, allowing businesses to optimize results based on custom ranking criteria and user behavior. Azure Cognitive Search also offers search relevance capabilities, but its focus is more on providing AI-driven personalized recommendations, which can be beneficial for e-commerce and content platforms.
Pricing and Cost: Algolia provides transparent pricing based on various factors such as the number of records, operations, and data transfer. It offers free-tier options and flexible pricing plans suitable for businesses of all sizes. Azure Cognitive Search, being part of Microsoft Azure, follows a pay-as-you-go model and offers tiered pricing based on factors like document count, query volume, and additional AI services, which may result in slightly higher costs compared to Algolia.

In summary, Algolia shines with its scalability, comprehensive search features, ease of integration, and blazing fast indexing speed, while Azure Cognitive Search offers a more versatile set of AI-powered capabilities and robust integration with the broader Azure ecosystem. The choice between the two will depend on the specific requirements, preferences, and budget of the business.

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Advice on Algolia, Azure Cognitive Search

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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Detailed Comparison

Algolia	Azure Cognitive Search
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 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.
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	Start, maintain and scale with minimal investment;Create searchable content using integrated AI;Customise to meet goals and industry requirements
Statistics
Stacks 1.4K	Stacks 39
Followers 1.1K	Followers 67
Votes 699	Votes 1
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	Pros 1 111
Integrations
React Ruby Jekyll JavaScript React Native Vue.js WordPress Shopify Docusaurus VuePress	Postman Java Node.js Python C# PowerShell

What are some alternatives to Algolia, Azure Cognitive Search?

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.