Elasticsearch Vs Opensearch: Key Differences and Similarities

There are many search engines today, as well as many analytic engines. These engines share similar features and, hence, can be categorized under the same open-source search engines.

Elasticsearch and OpenSearch are both search and analytics engines that enable users to store, search and analyze data while online. But note that, while they share a lot of similarities, their differences exist in their

1. Features

2. Capabilities

3. licensing models.

Read on, as the comprehensive analysis we have made in the article will talk about the comparison between Elasticsearch and OpenSearch. We will also be discussing their background, development, key aspects, functionalities, and how they are used.

 Background and Development of Elasticsearch Vs Opensearch

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Both systems were built on the Lucene library and also allow a full-text search engine that is capable of multi-tenant capabilities. They are both linked to an HTTP web interface and JSON documents that are free from schema.


Elasticsearch was first developed by Shay Banon in 2010; it was bought by Elastic. Elastic is a company that makes products that are used for search, observability, and security. And after the purchase, Elastic worked deeply on it and is fully responsible for the development and commercialization of Elasticsearch.

Elastic is responsible for the growth of many other products, like:

1. Kibana

2. Logstash

3. Beats

And they are all collectively known as the Elastic Stack. Elasticsearch also has an amazing set of features and its commercial license (Elastic License) has been in contention for some users because of the restrictions it has in various scenarios. Some of their features include:

1. Machine learning capabilities

2. Advanced analytics

3. Powerful indexing

4. Querying options.


OpenSearch is a newer open-source project that came up as a fork of Elasticsearch in 2021. The launch of one or more separate software development efforts based upon a modified copy of an existing project, especially in free and open-source software.

The OpenSearch project was initiated after the introduction of the Elastic License, which restricted the usage of Elasticsearch in certain scenarios. OpenSearch is maintained by a community of developers and is led by AWS, the original founding sponsor of Elasticsearch. 

The project aims to preserve the open-source nature of the search engine and offers a more permissive license (Apache 2.0), allowing users to freely use, modify and distribute the software without any restrictions.

OpenSearch, on the other hand, is a fork of Elasticsearch that was created after the introduction of the Elastic License. It is an open-source project maintained by a community of developers and is led by AWS, the original founding sponsor of Elasticsearch. OpenSearch retains most of Elasticsearch’s functionalities, including its scalability, distributed architecture, and querying capabilities.

 Licensing and Compliance

The most significant difference between Elasticsearch and OpenSearch lies in the model of their licenses. Elasticsearch falls under the governing body of the Elastic License, but users are restricted from using the software in certain scenarios.

 Some scenarios where users are restricted may include its integration into proprietary products or when it is offered as a managed service without permission from Elastic. This approach has raised concerns among open-source users who prefer a more permissive and flexible license.

 OpenSearch uses an Apache 2.0 license, which helps address these concerns by keeping the licensing model Opel and permissive. It also allows users to use and modify the software without any restrictions. This makes it an attractive alternative for any organization that is seeking to prevent any limitations that might be imposed by the Elastic License.

 The OpenSearch project is focused on maintaining almost the same features and set of compatibility with Elasticsearch. OpenSearch helps to ensure  that this open-source project lasts for a long time.

 Features and Functionalities

Furthermore, although both Elasticsearch and OpenSearch have similar features and functions, here are some key areas where they have differences.

1. Distributed Architecture

Both Elasticsearch and OpenSearch systems are designed to scale horizontally. And this allows you to add more nodes to the cluster in order to increase its capacity and performance.

2. Full-Text Search

Elasticsearch and OpenSearch have a collection of powerful full-text search capabilities. And if users want to perform complex queries, faceting, and aggregations on large datasets, they rely on them.

3. Instantaneous Analytics

Elasticsearch and OpenSearch engines support instantaneous data ingestion and analysis, which makes them suitable for use cases like log analysis, monitoring and alerting.

4. Machine Learning

Elasticsearch and OpenSearch have built-in machine learning capabilities that help extract ideas from data. Examples like;

1. Anomaly detection

2. Clustering

3. Regression analysis also.

5. Integrations

Both systems can work along with various tools and can be integrated with various technologies, including;

1. Kibana (visualization)

2. Apache Kafka (streaming)

3. AWS services (like AWS Lambda and Amazon S3).

But despite their similarities, there are a few differences that can be observed in the feature sets of Elasticsearch and OpenSearch. OpenSearch was used to introduce some enhancements, like improved indexing performance and downgrading ability without downtime.

However, some of these differences are minor and not noticeable, and both systems continue to evolve as new features and improvements are developed.

Ecosystem and Community

Elasticsearch has been in the market for over a decade and has an amazing, vast community of users, developers, and service providers. They have an ecosystem that has led to the development of numerous plugins and third-party tools that extend the functionality of Elasticsearch. Additionally, Elastic offers some commercial support, training, and consulting services to organizations.

OpenSearch is a newer project and is still in the process of building its own ecosystem. However, because it is backed by AWS and the open-source community, it is expected to grow fast and wide in the coming years.

OpenSearch has already gained some level of influence among users who prefer an open-source alternative to Elasticsearch.

Use Cases and Deployment Options

 Furthermore, both Elasticsearch and OpenSearch projects can be used in various other use cases, like:

 Log Analysis: They are used to analyze and monitor logs from various applications and infrastructure components.

 Business Intelligence: They enable immediate analysis of customer data, sales and other business-related metrics.

 Knowledge Management: They are also used in indexing and searching large repositories of documents, such as wikis, knowledge bases, and research papers.

 E-commerce: They can be used to empower search functionality on e-commerce platforms. This overall enables users to find products based on their preferences and requirements.

Deployment Options

Elasticsearch and OpenSearch can be used  on premises, in private clouds, or while using managed services provided by cloud vendors, like:

1. AWS

2. Google Cloud

3.Microsoft Azure

4. IBM Cloud

5. Alibaba Cloud.

 Both systems can also be deployed as a package of applications using Docker or Kubernetes for easier management and portability.

There are several cloud vendors that offer Elasticsearch and Opensearch services with their different approaches. These cloud vendors have adopted different strategies to give their powerful search and analytics engines to their clients.

1. Amazon Web Services (AWS)

First is Amazon Web Services (AWS), one of the leading cloud service providers that also offers both Elasticsearch and Opensearch services. AWS provides the Amazon Elasticsearch Service, a completely controlled platform that allows users to set up and operate Elasticsearch clusters easily.

Additionally, AWS introduced the Amazon OpenSearch Service, which is controlled by an open-source project. This project is normally derived from Elasticsearch and incorporates features from other search engines like Apache Lucene and Google’s Elastic.

 Hence, these services are designed to handle large volumes of data, express immediate analytics and also allow users to build powerful search applications.

2. Google Cloud Platform (GCP)

Google Cloud Platform (GCP) is another tool that offers Elasticsearch on Google Cloud and Google’s managed OpenSearch service. Elasticsearch on Google Cloud is a completely controlled service that allows users to use and manage Elasticsearch clusters without worrying about infrastructure management.


Google’s managed OpenSearch service, called “Google Cloud Elasticsearch,” is a project built on open-source and offers similar capabilities as Elasticsearch. It was programmed to be able to conduct large-scale search and analytics workloads. It also has some features like machine learning integrations, security and scalability.

3. Microsoft Azure

The third is Microsoft Azure, which brings two options for search and analytics called Azure Search and Azure OpenSearch Service. Azure Search is a completely managed cloud service that allows its users to build and use search experiences for their applications. It offers features like ranking, filtering, and spell-checking to help users find relevant results quickly.


Azure OpenSearch Service is also a totally controlled service based on an open-source project that allows its users to build search and analytics projects. It also supports various use cases, which include log analytics, application monitoring, and instantaneous data processing.


4. IBM Cloud

IBM Cloud comes along with the IBM Elasticsearch Service, a service designed for enterprises. It provides features like automatic results, high availability, and security to ensure the reliable and efficient management of large-scale search and analytics workloads.


The IBM Elasticsearch Service comes along with other IBM Cloud services, like Watson Natural Language Understanding and Watson Knowledge Studio. And this enables users to leverage the advanced AI capabilities for enhanced search.

 5. Alibaba Cloud

Alibaba Cloud is the cloud computing arm of Alibaba Group, which offers Apsara Search and is managed by Elasticsearch. It also has some advanced search capabilities like fuzzy matching, synonym support, and geospatial search. Additionally, Apsara Search is part of Alibaba Cloud Services and can be used to build a seasoned project that attends to a user’s needs.


Both Elasticsearch and OpenSearch serve as powerful search and analytics engines with similar functionalities. Their differences are better seen in their licensing models. But with Elasticsearch being restricted by the Elastic License and OpenSearch offering a more open and permissive Apache 2.0 license.


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