Overview

Elasticsearch

Stacks35.0K

Followers27.1K

Votes1.6K

Azure Search

Stacks80

Followers224

Votes16

Amazon Elasticsearch Service

Stacks377

Followers288

Votes24

Amazon Elasticsearch Service vs Azure Search vs Elasticsearch: What are the differences?

Introduction

In this markdown, we will discuss the key differences between Amazon Elasticsearch Service and Azure Search and Elasticsearch.

Scalability: Amazon Elasticsearch Service offers automatic scaling capabilities, allowing the users to easily scale their cluster up or down based on their needs. On the other hand, Azure Search does not provide automatic scaling and requires manual intervention for scaling the instance.
Available Features: Amazon Elasticsearch Service offers a wide range of features including advanced search capabilities, aggregations, full-text search, and support for multiple languages. Azure Search, on the other hand, provides features like full-text search, filtering, sorting, and faceting, but does not offer the same level of advanced search capabilities as Amazon Elasticsearch Service.
Integration with Cloud Service Providers: Amazon Elasticsearch Service is tightly integrated with the Amazon Web Services (AWS) ecosystem, providing seamless integration with other AWS services like AWS CloudTrail, Amazon CloudWatch, and AWS IAM. On the contrary, Azure Search is integrated with Microsoft Azure suite of products, offering integration with services such as Azure Storage, Azure Active Directory, and Azure Cognitive Services.
Pricing Model: Amazon Elasticsearch Service pricing is based on the instance type and storage used, with additional charges for data transfer and additional features like daily automated snapshot storage. Azure Search pricing, on the other hand, is primarily based on the number of indexes created and the number of documents processed. Thus, the pricing models of the two services differ significantly.
Maintenance and Management: Amazon Elasticsearch Service handles the underlying infrastructure and maintenance tasks like patching, monitoring, and backup, providing a managed service experience for the users. Azure Search also offers a managed service experience, but it does not provide the same level of control and flexibility over the underlying infrastructure as Amazon Elasticsearch Service.
Third-Party Integrations: Amazon Elasticsearch Service has a wide range of third-party integrations available, allowing users to connect with various tools and services. Azure Search also provides a good set of integrations, but it may not offer the same level of integration options as Amazon Elasticsearch Service.

In summary, Amazon Elasticsearch Service and Azure Search differ in terms of scalability, available features, integration with cloud service providers, pricing model, maintenance and management, and third-party integrations.

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Advice on Elasticsearch, Azure Search, Amazon Elasticsearch Service

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!

408k views408k

Comments

Phillip

Developer at Coach Align

Mar 18, 2021

Decided

The new pricing model Algolia introduced really sealed the deal for us on this one - much closer to pay-as-you-go. And didn't want to spend time learning more about hosting/optimizing Elasticsearch when that isn't our core business problem - would much rather pay others to solve that problem for us.

40.7k views40.7k

Comments

André

Nov 20, 2020

Needs adviceon

Elasticsearch

Amazon DynamoDB

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are:

Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON
Allow a strict match mode
Perform the search through all the JSON values (it can reach 6 nesting levels)
Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

60.3k views60.3k

Comments

Detailed Comparison

Elasticsearch	Azure Search	Amazon Elasticsearch Service
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).	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.	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.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License	Powerful, reliable performance;Easily tune search indices to meet business goals;Scale out simply;Enable sophisticated search functionality;Get up and running quickly;Simplify search index management	-
Statistics
Stacks 35.0K	Stacks 80	Stacks 377
Followers 27.1K	Followers 224	Followers 288
Votes 1.6K	Votes 16	Votes 24
Pros & Cons
Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale	Pros 4 Easy to set up 3 Managed 3 Auto-Scaling 2 More languages 2 Easy Setup	Pros 10 Easy setup, monitoring and scaling 7 Kibana 7 Document-oriented
Integrations
Kibana Beats Logstash	Microsoft Azure	No integrations available

What are some alternatives to Elasticsearch, Azure Search, Amazon Elasticsearch Service?

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

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.

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.

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.

Bonsai

Your customers expect fast, near-magical results from your search. Help them find what they’re looking for with Bonsai Elasticsearch. Our fully managed Elasticsearch solution makes it easy to create, manage, and test your app's search.

Azure Cognitive Search

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.