OpenSearch vs. Elasticsearch: What’s the Real Difference?

If you’ve ever searched for “OpenSearch vs Elasticsearch,” chances are you’re exploring open-source search engines, enterprise needs, and cloud-based solutions.

Both OpenSearch and Elasticsearch have carved a niche in the search and analytics domain, but how do they stack up against each other? Let's break it down.

What Are OpenSearch and Elasticsearch?

Elasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene. Its speed, scalability, and versatility gained immense popularity, making it a favorite for use cases like log analysis, full-text search, and observability.

Elastic, the company behind Elasticsearch, has since expanded its offerings with Elastic Cloud, a SaaS platform packed with features like machine learning and advanced analytics.

OpenSearch, on the other hand, is a fork of Elasticsearch, initiated by AWS after Elastic transitioned to a more restrictive licensing model under the Server Side Public License (SSPL).

OpenSearch remains fully open-source under the Apache 2.0 license, focusing on a community-driven approach to development. This makes it a popular choice for those looking to avoid vendor lock-in while maintaining compatibility with Elasticsearch APIs.

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Key Differences: OpenSearch vs. Elasticsearch

1. Licensing and Community

One of the most significant differences lies in their licensing models:

• Elasticsearch: Originally released under the Apache 2.0 license, Elasticsearch now operates under the SSPL and Elastic License. These licenses are less permissive, restricting how the software can be used, especially in managed services.
• OpenSearch: Fully open-source and licensed under Apache 2.0, OpenSearch allows free use, modification, and distribution. It is maintained by a community-driven project supported by AWS and contributors worldwide.

2. Features and Plugins

• Elasticsearch:

Offers a comprehensive ecosystem of tools such as Kibana for visualization, Logstash for data ingestion, and advanced plugins for functionality like machine learning and anomaly detection.

• OpenSearch:

Provides OpenSearch Dashboards as an alternative to Kibana and includes open-source plugins for security, observability, and real-time alerting.

While OpenSearch covers most core functionalities of Elasticsearch, some advanced features like Elastic’s proprietary machine learning tools are unavailable.

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3. Cloud Integrations

Both tools integrate well with cloud platforms but have their specialties:

• Elasticsearch:

Offers a managed service called Elastic Cloud, available on AWS, Azure, and Google Cloud. This platform provides effortless integration with Elastic’s ecosystem.

• OpenSearch:

Primarily tailored for Amazon OpenSearch Service, with broader integrations across AWS services like Lambda and S3. OpenSearch’s open-source nature makes it easier to deploy on self-hosted environments or alternative clouds.

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4. Performance and Scalability

Performance can be a deciding factor for many:

• Elasticsearch:

Known for its high performance and scalability, Elasticsearch excels in scenarios requiring large-scale data processing, thanks to its machine learning capabilities and advanced optimizations.

• OpenSearch:

Optimized for real-time analytics and distributed search, OpenSearch continues to evolve with contributions from the open-source community. Its scalability features are robust but may require additional fine-tuning compared to Elasticsearch’s enterprise-focused offerings.

5. Use Cases

Both platforms cater to similar use cases:

• Enterprise Search: Powering internal and external search engines.
• Log Analytics: Processing and analyzing massive log data for infrastructure monitoring.
• Observability: Tracking metrics, logs, and traces in modern, distributed systems.
• SIEM (Security Information and Event Management): Detecting and responding to security threats in real-time.

While Elasticsearch often shines in enterprise-grade scenarios with its premium features, OpenSearch appeals to users seeking open-source flexibility without licensing constraints.

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Ecosystem and Tools

Visualization and Dashboards

• Kibana: As part of the Elastic Stack (ELK Stack), Kibana provides rich visualization tools, interactive dashboards, and machine learning integrations.
• OpenSearch Dashboards: Offers similar capabilities, aligning with OpenSearch’s open-source approach. While slightly less polished than Kibana, it continues to improve with community contributions.

Ingestion Pipelines

• Logstash: Elastic’s data ingestion tool supports complex data pipelines with powerful filtering and transformation capabilities.
• OpenSearch: Compatible with various data ingestion tools, including Logstash and Fluentd, providing flexibility for different deployment needs.

APIs and Compatibility

OpenSearch maintains API compatibility with Elasticsearch up to version 7.10. This ensures an easy migration path for users transitioning from Elasticsearch to OpenSearch without significant changes to their existing workflows.

Security and Licensing

Security Features

Both platforms emphasize robust security features such as:

• Role-based access control (RBAC).
• Authentication and encryption.
• Audit logging and fine-grained access control.

Elastic offers advanced security capabilities as part of its premium tiers. In contrast, OpenSearch provides security plugins as part of its open-source model, ensuring accessibility without additional costs.

Licensing Implications

The switch from Apache 2.0 to SSPL by Elastic has been a major point of contention. For organizations prioritizing truly open-source solutions, OpenSearch’s Apache 2.0 licensing offers peace of mind.

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Scalability and Performance

Scalability is key for search engines handling growing workloads:

• Elasticsearch uses its enterprise-grade features like machine learning and automated index lifecycle management to handle large-scale deployments efficiently.
• OpenSearch focuses on modularity and flexibility, making it a solid choice for real-time analytics and smaller-scale projects where customizability is paramount.

OpenSearch also benefits from its community-driven development, ensuring continuous improvements tailored to users’ needs.

Pricing Models

• Elastic: Operates on a tiered pricing model, with basic features available for free and advanced functionalities locked behind premium plans like Platinum. Managed Elastic Cloud services add additional costs but simplify deployment and maintenance.
• OpenSearch: Fully free and open-source. Costs depend on the hosting platform (e.g., AWS) and resource consumption, but the absence of license fees makes it an attractive option for budget-conscious organizations.

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Conclusion

OpenSearch and Elasticsearch are both strong options for search and analytics, each with its advantages.

Elasticsearch is known for its advanced features and polished ecosystem, while OpenSearch is a great open-source alternative backed by a strong community. The best choice depends on whether you prefer advanced capabilities or the flexibility of an open-source solution.

If you're looking to simplify observability, Last9 could be just what you need. It brings together metrics, logs, and traces in one easy-to-use view, making it easier to connect the dots across your systems.

With integrations like Prometheus and OpenTelemetry, Last9 helps teams gain deeper insights, manage alerts more efficiently, and troubleshoot faster—especially in complex, distributed systems and microservices environments.

Schedule a demo with us to know more or try it for free to understand the product more!

FAQs

Is OpenSearch the same as Elasticsearch?
No, OpenSearch is a fork of Elasticsearch. While similar in functionality, OpenSearch focuses on open-source licensing and community-driven development.

Is OpenSearch slower than Elasticsearch?
Performance depends on workload and deployment. OpenSearch is optimized for real-time analytics and continues to evolve.

What is the purpose of OpenSearch?
OpenSearch is designed as an open-source search and analytics engine for real-time data analysis, observability, and log analytics.

Is OpenSearch a drop-in replacement for Elasticsearch?
Yes, OpenSearch maintains API compatibility with Elasticsearch versions up to 7.10, making it a suitable replacement for many use cases.

What is AWS ELK?
AWS ELK refers to the managed Amazon Elasticsearch Service (now Amazon OpenSearch Service), which supports both Elasticsearch and OpenSearch workloads.

Can OpenSearch Benchmark test Elasticsearch?
Yes, OpenSearch Benchmark supports Elasticsearch testing for performance evaluations.

Are Elasticsearch and Kibana better than OpenSearch?
It depends on your requirements. Elasticsearch and Kibana are feature-rich, but OpenSearch and Dashboards provide free, open-source alternatives.

How does OpenSearch differ from Elasticsearch in scalability?
Elastic’s advanced tools like machine learning make it more suited for large-scale predictive workloads. OpenSearch excels in real-time analytics and offers flexibility in deployment.