OpenSearch vs Elasticsearch: Key Differences for Technical Leaders in 2025
- SquareShift Content Team
- Aug 15, 2025
- 8 min read
Table of Contents:
Introduction
In search and analytics, the competition between Elasticsearch and OpenSearch has been the 2020s' story to tell. What began as a disagreement over licensing escalated into an outright battle between two competing projects, each with its own thought process, feature profile, and market focus. For enterprises, grasping the subtleties of OpenSearch vs Elasticsearch is more than a theoretical exercise; it's an important choice that makes a difference in long-term strategy, expense, and an organization's capacity for innovation.
This blog will get past the hype, giving you an in-depth and current comparison between these two mighty platforms. We'll dive into their core differences, major features, performance indicators, and the practical implications of each. By the end of it, you'll have a clear direction to make the correct decision for your business.
The Backstory: A Fork in the Road
To appreciate the present situation of OpenSearch vs Elasticsearch, you must travel back to 2021. For years, Elasticsearch had been a staple of the open-source ecosystem, licensed under the permissive Apache 2.0 license. This enabled companies, including big cloud providers such as AWS, to construct and provide managed services based on the technology.
But in a bid to safeguard its business interests, Elastic, the maker of Elasticsearch, made a crucial change to its licensing policy. From version 7.11 and going forward, Elasticsearch and Kibana, its visualization engine, were relicensed under the Server Side Public License (SSPL) and the Elastic License 2.0 (ELv2). These "source-available" licenses are not OSI-approved as being open source and have provisions that limit how third parties can distribute the software as a service.
In return, AWS and a group of partners took the final Apache 2.0-licensed version of Elasticsearch (7.10.2) and Kibana (7.10.2) and forked them. The new, community-driven project was called OpenSearch. The fork was an unambiguous statement: AWS and its partners were dedicated to continuing a genuinely open-source alternative. The two projects have since been on divergent development paths, each defining its own identity.
Licensing and Governance: The Philosophical Divide - OpenSearch vs Elasticsearch
The most important and elemental distinction between OpenSearch vs Elasticsearch is in their models of licensing and how they are governed. It is not merely a technicality in terms of legalities; it is a mirror to their fundamental philosophies.
OpenSearch's Open-Source Purity
OpenSearch is and continues to be an entirely open-source project. It is Apache 2.0 licensed, one of the most permissive and most widely used open-source licenses. That implies you are free to use, modify, distribute, and even sell OpenSearch as part of your own offerings without the slightest legal uncertainty.
The project is under the control of the OpenSearch Project, an open-source effort supported by industry giants such as AWS, SAP, and Logz.io. In late 2024, the project further cemented itself by being included under the umbrella of the Linux Foundation, a move from the industry indicating a long-term commitment towards open governance and community contributions. This approach is most attractive to organizations that have stringent open-source policies or are concerned about vendor lock-in. It is secure and flexible, knowing that the future of the project is not associated with the business interests of one particular firm.
Elasticsearch's Hybrid Approach
Elasticsearch's licensing is more nuanced. Its core functionality is covered by the SSPL and ELv2. Although the source code can be inspected, these are not "open-source" licenses according to the OSI. This isn't a concern for the majority of internal use cases. But for those companies looking to create a managed service over Elasticsearch, this needs a commercial agreement with Elastic.
In a recent and significant move, Elastic has also included the GNU Affero General Public License (AGPLv3) as the third option of licensing for the free portion of its code. AGPLv3 is an OSI-approved open-source license but includes a robust "copyleft" provision. This implies that if you make changes to the AGPLv3-licensed code and sell it as a service, you should also provide the source code of your entire service under the same license. This is viewed as a step by Elastic to engage the open-source community once again while protecting its commercial managed service, Elastic Cloud.
For legal and CTO groups, this difference in licensing will be the first and most important point of consideration. If your organization's policy requires use of exclusively OSI-approved licenses, then OpenSearch is the obvious and, candidly, sole choice.
Feature Sets and Ecosystems: Diverging Paths
Both projects have since been quick to innovate, but along divergent paths. The feature gap that previously existed is narrowing, but a marked difference in their ecosystems and direction is now evident.
OpenSearch: The Community-Driven Platform
OpenSearch's first priority was to mirror the capabilities of the previous open-source Elasticsearch release and then extend them. The project has been able to create open-source equivalents for capabilities that were originally included in Elastic's proprietary X-Pack.
Some of the principal OpenSearch features and components of its ecosystem are:
OpenSearch Dashboards: The Kibana user interface and visualization fork. It supports a strong, intuitive interface for data exploration, visualization, and management.
Built-in Security: A major advantage of OpenSearch is that its comprehensive security suite is included by default and is fully open source. This includes fine-grained access control, role-based security, and encryption in transit. For many companies, this eliminates the need for expensive commercial security plugins.
Plugins and Integrations: OpenSearch boasts a maturing plugin environment. AWS, specifically, has built strong integrations with other AWS services such as Amazon S3, Kinesis, and Lambda, which makes it an attractive option for organizations with significant investments in the AWS cloud. The platform also includes a dedicated vector search engine, which makes it a good option for contemporary AI and machine learning applications.
Observability: OpenSearch boasts its own observability features for log analytics, performance monitoring, and tracing, offering a full solution for monitoring distributed applications.
Elasticsearch: The Commercial Powerhouse
Elasticsearch, and the larger Elastic Stack (the ELK Stack: Elasticsearch, Logstash, and Kibana), has further developed as an all-encompassing, commercially-supported platform. Elastic's innovation is frequently integrated tightly into one cohesive product, with various advanced features held back from free subscription levels.
Key Elasticsearch features and components of the ecosystem are:
Advanced Security: Basic security is available for free, but enterprise-level features such as sentoid SSO integrations, field-level security, and audit logging are available with paid plans.
Machine Learning (ML): Elastic provides top-notch, proprietary machine learning capabilities for anomalies, forecasting, and other use cases. This is an important differentiator for organizations that require these advanced features out of the box for security analytics or observability.
Vector Search: Elastic has made significant investments in vector search performance optimization directly within Apache Lucene, which the two projects share. This typically provides Elasticsearch with a performance advantage in AI-driven search solutions.
Elastic Cloud: Elastic's managed service, Elastic Cloud, is a big draw. It offers a turnkey experience that takes care of scaling, upgrades, and maintenance, making it easy for businesses that want a hands-off solution to operate.
Performance and Resource Use: Benchmarks Speak Volumes
Performance tends to be a determining factor for technical teams. Though based on the same technology foundation (Apache Lucene), the two platforms' different paths of development have introduced some differences worth noticing.
Based on a number of independent tests and studies conducted between 2024–2025, Elasticsearch tends to outperform OpenSearch in terms of performance. Tests have indicated that Elasticsearch is between 40% to 140% faster in certain complicated query situations, for instance, text querying and sorting, and more efficient in its resource utilization.
This performance advantage is typically credited to Elastic's singular investment in maximizing its core code and adding proprietary improvements to Lucene. In applications where milliseconds of latency are precious, or for which efficiency of resources directly equates to large cost savings, Elasticsearch's performance advantage can prove a clincher.
Total Cost of Ownership (TCO): The Dollars and Cents
Aside from technical functionality, the total cost of ownership (TCO) is a concern for any business leader. In this case, the licensing models directly come into play.
OpenSearch: The TCO for OpenSearch is typically more stable and perhaps lower. Since the complete set of features, including robust security, is open source, there are no licensing costs. Your expenses are mainly associated with infrastructure (cloud or on-prem) and the operational overhead of maintaining the clusters. If you utilize a managed service such as Amazon OpenSearch Service, your expense is based on usage of resources, and you can select from a range of instance types and pricing models.
Elasticsearch: Elasticsearch's TCO is more complicated. Although the core is open-source, using advanced features (e.g., machine learning, enhanced security) and getting official support costs money. These are not cheap subscriptions, particularly for high-scale usage. For those organizations that require such proprietary features, the expense might be worth it in terms of added value and lower operational complexity of a turnkey, fully supported solution.
The choice typically comes down to a "build vs. buy" decision. With OpenSearch, you're effectively "building" your solution with open-source pieces and hosting them yourself or with third-party assistance. With Elasticsearch, you can "buy" a more integrated, commercially-supported platform that might have more features and less hassle.
Use Cases and Audience: Who Are They For?
The decision between Elasticsearch vs OpenSearch usually comes down to your particular use case and your organization's priorities.
OpenSearch is a Good Fit For:
Organizations that have strict open-source requirements: Organizations that are required to utilize OSI-approved licenses based on policy or philosophy.
AWS-based businesses: Businesses that are deeply invested in the AWS ecosystem can take advantage of deep integrations and the Amazon OpenSearch Service for an integrated, managed experience.
Elasticsearch is a Good Fit For:
Enterprises that need sophisticated, turnkey capabilities: Organizations that have a need for out-of-the-box, sophisticated capabilities such as machine learning for security analysis or observability, and are willing to pay for them.
Organizations that focus on performance: Companies where the query speed and resource utilization are mission-critical.
Fully managed, hands-off teams: Organizations that wish to delegate the operational overhead of cluster management to a managed service such as Elastic Cloud so that they can leave their engineers to work on product development.
End-to-end, single-platform businesses: Companies that desire the tight integration of the complete Elastic Stack (Elasticsearch, Kibana, Beats, Logstash) and a single vendor for support and innovation.
Conclusion: A Strategic Choice, Not Only a Technical One
The controversy of OpenSearch vs Elasticsearch is no longer a basic debate about forks and licensing. It's a strategic decision that indicates an organization's values regarding cost, flexibility, performance, and long-term vision.
If your organization's core values are rooted in open source, community collaboration, and avoiding vendor lock-in, OpenSearch offers a compelling and robust path forward. It provides all the power of the original platform, with a rich and growing feature set, all under a permissive license.
Conversely, if your business requires a high-performance, commercially supported, and feature-rich platform with advanced machine learning capabilities and a preference for a single vendor, Elasticsearch and its managed service, Elastic Cloud, remain a powerful and mature option.
Ultimately, the "right" choice is the one that best aligns with your team's expertise, your project's specific needs, and your company's strategic goals for its data infrastructure.
Ready to Explore Your Options?
Deciding between these two formidable engines can be complicated. If you're a CTO or technical director seeking specialist advice on your search and analytics approach, we're here to assist you. Get in touch with us now to talk about your use case and get a personalized suggestion that guarantees your data infrastructure sets you up for success.