I was sitting in the conference room with our company's tech team, locked in a heated debate about our search infrastructure. Half the room was advocating for sticking with Elasticsearch, while the other half was pushing for a switch to the newly minted OpenSearch. As I listened to both sides passionately argue their points, I couldn't help but reflect on how much the search engine landscape had changed in just a few short years.

This scenario isn't unique. Across the tech world, countless teams are grappling with this very decision. Having been in the trenches of data management for over two decades, I've seen technologies come and go, but rarely has a choice been as impactful as this one. So, let's roll up our sleeves and dive deep into this debate. Whether you're a seasoned CTO or a curious tech enthusiast, I promise you'll walk away with a clearer understanding of these powerhouse search engines and how to choose between them.

The Genesis of the Debate

To understand the current state of affairs, we need to step back and look at the history. Elasticsearch, created by Shay Banon in 2010, quickly became the go-to solution for many organizations seeking a scalable, real-time search and analytics engine. Its ease of use, powerful features, and robust ecosystem made it a favorite among developers and data engineers alike.

On elasticsearch

However, the landscape shifted dramatically in 2021 when Elastic, the company behind Elasticsearch, changed its licensing model. This move prompted Amazon Web Services (AWS) to fork the last Apache-licensed version of Elasticsearch, giving birth to OpenSearch. Suddenly, organizations found themselves at a crossroads, forced to reevaluate their search engine strategy.

Core Capabilities: A Side-by-Side Comparison

Both Elasticsearch and OpenSearch share a common ancestry, which means they have many similarities in their core functionalities. Let's break down some key areas:

1. Full-Text Search

Both engines excel at full-text search operations, allowing users to quickly find relevant documents within large datasets.

Elasticsearch Example:

OpenSearch Example:

As you can see, the query syntax is identical, which is great news for teams transitioning between the two.

A Basic Guide To Elasticsearch Aggregations |

2. Aggregations

Both engines provide powerful aggregation capabilities, allowing for complex data analysis.

Elasticsearch Example:

OpenSearch Example:

Again, the syntax is nearly identical, showcasing the shared lineage of these engines.

3. Scalability and Performance

Both Elasticsearch and OpenSearch are designed to scale horizontally, allowing organizations to handle growing data volumes efficiently. They both use a distributed architecture that enables sharding and replication of data across multiple nodes.

In terms of performance, both engines are highly competitive. However, the exact performance characteristics can vary depending on the specific use case, data model, and hardware configuration. It's crucial for organizations to conduct thorough benchmarks tailored to their specific needs.

Diverging Paths: Key Differences

While the core capabilities remain similar, Elasticsearch and OpenSearch have started to diverge in several important areas:

1. Licensing

This is perhaps the most significant difference and the catalyst for the fork. Elasticsearch is now dual-licensed under the Elastic License and the Server Side Public License (SSPL), both of which place restrictions on how the software can be used and distributed. OpenSearch, on the other hand, is fully open-source under the Apache License 2.0.

FAQ on 2021 License Change | Elastic

For many organizations, especially those building commercial products or services on top of their search infrastructure, this licensing difference can be a decisive factor. The Apache License provides more flexibility and fewer legal concerns for commercial use.

2. Cloud Offerings

Elasticsearch is tightly integrated with Elastic Cloud, offering a seamless experience for those who want a fully managed solution. OpenSearch, being an AWS-led project, is naturally well-integrated with Amazon OpenSearch Service.

However, it's worth noting that both can be deployed on various cloud platforms or on-premises. The choice often comes down to existing cloud infrastructure and preferences.

3. Feature Development

Since the fork, both projects have been developing new features independently. Elasticsearch, with its commercial backing, has been pushing forward with advanced machine learning capabilities, including anomaly detection and forecasting.

OpenSearch, while initially playing catch-up, has been rapidly developing its own set of unique features, particularly in areas like security and observability.

4. Community and Ecosystem

Elasticsearch has a mature ecosystem with a wide range of plugins and integrations developed over many years. OpenSearch, being newer, is still building its ecosystem but has seen rapid growth, particularly in areas where the Apache License is preferred.

What is Data Migration and how to create the perfect process | Go Wombat OU

Real-World Considerations

When making the choice between Elasticsearch and OpenSearch, several practical considerations come into play:

1. Migration Complexity

For organizations already using Elasticsearch, the prospect of migrating to OpenSearch can seem daunting. However, due to their shared origins, the process is often smoother than expected.

Consider this Python script for migrating data:

This script demonstrates a basic migration process. In practice, you'd need to handle pagination for large datasets and consider mapping differences, but it illustrates the conceptual similarity between the two systems.

2. Operational Expertise

Many organizations have invested heavily in building Elasticsearch expertise within their teams. The good news is that much of this knowledge transfers directly to OpenSearch. However, there are some differences in tooling and management interfaces that teams need to account for.

3. Cost Implications

While both Elasticsearch and OpenSearch offer free, open-source versions, the total cost of ownership can vary significantly depending on your deployment model and scale.

For cloud deployments, it's worth comparing the pricing of Elastic Cloud against Amazon OpenSearch Service or other managed solutions. For on-premises or self-managed cloud deployments, consider factors like support contracts, operational overhead, and potential licensing costs for advanced features.

4. Future-Proofing

When making a decision, it's crucial to consider the long-term trajectory of both projects. Elasticsearch, with its commercial backing, has a clear roadmap and dedicated resources for development. OpenSearch, while newer, has the backing of AWS and a growing community, suggesting a strong future.

Consider this hypothetical scenario: A large e-commerce company is considering moving from Elasticsearch to OpenSearch. They're concerned about future feature parity. To address this, they could implement a hybrid approach:

This approach allows the company to gradually transition while ensuring they can benefit from unique features from both engines.

Making the Decision

So, how do you decide between Elasticsearch and OpenSearch? Here's a framework to guide your decision:

  1. Licensing Concerns: If you're building commercial products or services on top of your search infrastructure, OpenSearch's Apache License might be more appealing.
  2. Existing Infrastructure: If you're heavily invested in the AWS ecosystem, OpenSearch might integrate more seamlessly. If you're using Elastic Cloud or have a significant investment in Elasticsearch expertise, sticking with Elasticsearch might make more sense.
  3. Feature Requirements: Carefully evaluate the specific features you need. While both engines cover most use cases, there are some differences in advanced features, particularly in machine learning and security areas.
  4. Community and Support: Consider the ecosystem around each engine. Elasticsearch has a more mature community and wider range of third-party tools, but OpenSearch is rapidly catching up.
  5. Long-term Strategy: Consider your organization's long-term data strategy. Are you looking to build in-house expertise, or do you prefer a more managed solution? How important is open-source to your organization's values?


The debate between Elasticsearch and OpenSearch isn't about declaring a clear winner. Both are powerful, capable search engines that can handle a wide range of use cases. The right choice depends on your specific needs, existing infrastructure, and long-term strategy.

As we've seen, the core capabilities of both engines are remarkably similar, making it possible to start with one and switch to the other if needed. The key differences lie in licensing, ecosystem, and some advanced features.

For organizations just starting their search engine journey, OpenSearch's fully open-source nature and AWS backing make it an attractive option. For those already invested in the Elasticsearch ecosystem, the decision to switch should be carefully weighed against the costs and benefits of migration.

Ultimately, the choice between Elasticsearch and OpenSearch is not just a technical decision, but a strategic one that can impact your organization's data infrastructure for years to come. By carefully considering the factors we've discussed and aligning them with your organization's goals, you can make an informed decision that sets you up for success in the ever-evolving world of data management and analytics.

Remember, the search engine you choose is just one part of your overall data strategy. Whichever path you take, focus on building a flexible, scalable architecture that can adapt to your changing needs. In the rapidly evolving world of data, the ability to pivot and adapt is often more valuable than any single technology choice.

1. What prompted the creation of OpenSearch?

A: OpenSearch was created in response to Elastic's licensing change for Elasticsearch. AWS forked the last Apache-licensed version of Elasticsearch to ensure a fully open-source option remained available.

2. Are Elasticsearch and OpenSearch API-compatible?

A: Yes, for the most part. OpenSearch maintains API compatibility with Elasticsearch 7.10.2, making migration relatively straightforward for many use cases.

3. Which option is more cost-effective?

A: It depends on your specific use case. While OpenSearch is free and open-source, Elasticsearch may be more cost-effective if you're already using Elastic Cloud or need specific commercial features. Conduct a thorough TCO analysis for your situation.

4. Can I use Kibana with OpenSearch?

A: No, Kibana is specific to Elasticsearch. However, OpenSearch provides OpenSearch Dashboards, a fork of Kibana that offers similar functionality and is compatible with OpenSearch.

5. How do the machine learning capabilities compare?

A: Elasticsearch currently has more advanced machine learning capabilities, particularly in areas like anomaly detection and forecasting. OpenSearch is actively developing its ML features but currently lags behind in this area.

6. Is it possible to run a hybrid setup with both Elasticsearch and OpenSearch?

A: Yes, it's possible to run a hybrid setup. This can be useful during migration or if you need specific features from both. However, it increases operational complexity and should be carefully considered.

7. How does cloud integration differ between the two?

A: Elasticsearch integrates tightly with Elastic Cloud, while OpenSearch is well-integrated with Amazon OpenSearch Service. Both can be deployed on various cloud platforms, but you may find better tooling and support on their respective preferred platforms.

8. What are the main security differences?

A: Both offer robust security features, but OpenSearch has been making significant strides in this area. It offers features like fine-grained access control and audit logging out-of-the-box, which may require a paid license in Elasticsearch.

9. How active is the development of each project?

A: Both projects are actively developed. Elasticsearch, with its commercial backing, has a steady release cycle. OpenSearch, despite being newer, has seen rapid development with strong backing from AWS and a growing community.

10. If I'm starting a new project, which should I choose?

A: For new projects, OpenSearch often makes sense due to its Apache 2.0 license and growing feature set. However, if you need specific Elasticsearch features or prefer its ecosystem, it may still be the better choice. Consider your long-term needs, in-house expertise, and licensing requirements when deciding.

Rasheed Rabata

Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.