Elasticsearch vs. OpenSearch: Which one to choose? [Updated 2025]

TL;DR

Elasticsearch and OpenSearch, once nearly identical, have taken very different paths.

• Licensing: Elasticsearch added AGPLv3 in 2024; OpenSearch stays Apache 2.0 under the Linux Foundation.
• Community: Elastic controls Elasticsearch; OpenSearch has 400+ orgs & 3,300+ contributors.
• Performance & AI: Elastic shows faster vendor benchmarks; OpenSearch 3.2 adds “agentic AI” and hybrid search.
• Features: Elastic = RAG API, SIEM, ML (paid). OpenSearch = free security, anomaly detection, FAISS/NMSLIB vectors.
• Pricing: Elastic Cloud only via subscription; OpenSearch has multiple cheaper managed providers.

Bottom line: Elasticsearch = enterprise features at a cost. OpenSearch = open, community-driven, and fast-evolving.

Table of Contents

Introduction

Choosing the right search and analytics engine is crucial for modern data‑driven applications. In the last few years the landscape has shifted: Elasticsearch, once the undisputed king of the ELK/EFK stack, changed its licence in 2021, prompting AWS to fork the last Apache‑licensed version into OpenSearch. Since then, both projects have evolved rapidly. Elastic has re‑embraced an OSI‑approved licence for its open‑source core, while OpenSearch has joined the Linux Foundation and is developing its own identity. This update brings the Knowi comparison up to date with the latest licensing, governance, performance and feature changes.

What is Elasticsearch?  

Elastic developed Elasticsearch, a distributed, open-source analytics engine for all types of data, including structured and unstructured data, specialized for blazing fast search capabilities. Elasticsearch is the central part of the ELK stack. The ELK stack comprises Elasticsearch, Logstash, Kibana, and Beats, providing data storage, integration, visualization, and shipping solutions. This is a convenient approach for small and large companies handling big data. 

In August 2024 Elastic announced that the free part of the Elasticsearch and Kibana source code can be licensed under the AGPLv3 in addition to the Server Side Public Licence (SSPL) and Elastic Licence 2.0. While binary releases remain under Elastic’s commercial terms, this change means the open‑source core is once again OSI‑approved. Elastic’s paid tiers continue to include advanced machine‑learning jobs, anomaly detection, cross‑cluster replication and observability features.

Explore more about Elasticsearch and its use cases in this blog post: What is Elasticsearch used for? 

What is OpenSearch?  

OpenSearch was born as a forked project of Elasticsearch. Basically, the final open-source versions of Elasticsearch and Kibana 7.10.2 were forked, and those fork versions became OpenSearch and OpenSearch Dashboards 1.0. OpenSearch is a community-driven, open-source project managed by Amazon. It is Apache 2.0 licensed, which means you are free to use, contribute to, change, or update the source code.

By 2025 OpenSearch has grown into an independent search and analytics platform. It supports full‑text search, distributed indexing and multi‑tenant architectures like Elasticsearch, but differentiates itself with a plugin‑oriented architecture. Core search continues to use Lucene, but OpenSearch adds vector engine, FAISS and NMSLIB, to perform high‑dimensional vector searches. The project’s community is thriving: over 400 organisations contribute, with more than 3,300 individual contributors, and it ranks 17th among all Linux Foundation projects

Now that we’ve some basic understanding of Elasticsearch and OpenSearch, let’s dive into the feature comparison in detail.  

Elasticsearch vs. OpenSearch: What’s the difference? 

Let’s compare the following aspects of Elasticsearch and OpenSearch to understand which would better fit your next project.  

1. Features 
2. Query Types 
3. Community 
4. Licensing 
5. Security 
6. Performance 
7. Support and Documentation 
8. Pricing 

Features 

Both Elasticsearch and OpenSearch offer extensive features, including full-text search, distributed search, multi-tenancy, and robust analytics engines. 

With a premium subscription, Elasticsearch offers some unique features, like geoshape aggregations and extra dashboard features for Kibana, including Canvas (a presentation tool) and Lens (smart suggestions for data visualization), which are currently unavailable with OpenSearch. 

Elasticsearch also offers proprietary add‑ons for observability (APM and SIEM), enterprise search and machine‑learning. The new RAG Retriever API simplifies retrieval‑augmented generation workflows, and Elastic integrates vector search into the core product. Elastic’s vector fields currently support up to 4,096 dimensions.

While Elasticsearch has been in the field longer than OpenSearch, OpenSearch is catching up and has some unique features to offer, like Trace Analytics. Trace Analytics allows users to analyze trace data from distributed applications. This can be particularly beneficial for troubleshooting and performance optimization.  

OpenSearch emphasises openness: its default distribution includes role‑based access control, document‑ and field‑level security, anomaly detection, cross‑cluster replication, machine‑learning models (regression, classification) and connectors (JDBC/ODBC) at no cost. The project added support for FAISS and NMSLIB vector engines, enabling vector dimensions up to 16k. OpenSearch 3.2 introduced “agentic AI” capabilities-hybrid search, streaming ingestion, multimodal embeddings, and OCSAR, an AI‑driven release assistant

Below are the features OpenSearch offers for free which are only available in the premium versions of Elasticsearch: 

1. Anomaly detection 
2. Centralized user accounts/access control 
3. Cross-cluster replication 
4. IP filtering 
5. Configurable retention period 
6. Machine learning features such as regression and classification 
7. Tableau connector 
8. JDBC driver 
9. ODBC driver 

However, in OpenSearch, some functionalities, like anomaly detection alerting found in premium Elasticsearch, require the integration of additional plugins.  

Query Types 

Both Elasticsearch and OpenSearch are equipped to handle various standard query types, including full-text, term, and match queries. However, each offers unique capabilities in advanced query types. 

Elasticsearch’s Advanced Query Capabilities 

• Scripted Queries: Provide the flexibility to use custom scripts for complex search criteria. 
• Span Queries: Ideal for locating specific sequences of terms within a document. 
• Geo Queries: These allow you to locate documents based on geographic data, like proximity to a point or within a defined polygon. 
• “More Like This” Queries: Useful for finding documents that are content-similar to a reference document. 
• Specialized Queries: Cater to niche use cases, such as nested queries for searching within complex, nested data structures, and fuzzy queries to find terms with slight spelling differences. 

OpenSearch’s Query Support 

• Joining Queries: Allow for merging data from multiple indices or sources. 
• Full-Text Queries: Enable searching for particular text strings within one or more documents. 
• Term-Level Queries: Useful for finding documents that contain specific terms or phrases. 
• Geographic and XY Queries: Facilitate searches based on geographical coordinates or shapes, such as locating documents within a certain radius of a point. 
• Span Queries: Similar to Elasticsearch, these identify specific term sequences within a field. 

In summary, the core query types (full‑text, match, term, boolean, range) remain similar. Elasticsearch retains specialised queries, scripted, span, geo, fuzzy and “more like this”, while OpenSearch focuses on join queries, geospatial (XY) searches and term‑level filters. Both projects support k‑nearest‑neighbour vector queries. Elastic’s RRF (reciprocal rank fusion) and retriever framework streamline AI applications, whereas OpenSearch relies on plugins and external models for RAG workflows.

Community 

Elastic’s ecosystem is large but company‑controlled. Contributions are publicly visible, yet commit access remains restricted. OpenSearch’s move to the Linux Foundation has accelerated community growth. By early 2025, more than 1,400 unique contributors and hundreds of maintainers across 100+ GitHub repositories work on OpenSearch. Its technical steering committee includes multiple vendors, enhancing transparency and reducing AWS dominance.

Licensing 

Licensing is now more nuanced. Elastic’s addition of AGPLv3 to the free code base means the OSS edition is once again open source; however, premium features and official binaries remain under Elastic License 2.0.

OpenSearch embraces the Apache 2.0 license, maintaining a solid commitment to being open-source. This licensing choice ensures that the code remains freely available for anyone to use, modify, and distribute. It also implies fewer restrictions on using the software, making it a more flexible option for product development. 

Security 

When we talk about databases, security is of great importance.The free Elastic distribution includes TLS encryption and basic authentication. Advanced security, role‑based access control (RBAC), field‑level security and audit logging, requires paid subscriptions.

In contrast, OpenSearch provides all of these features out of the box, plus fine‑grained access control and multi‑tenant support. Piped Processing Language (PPL) upgrades in OpenSearch 3.2 improve trace analytics and anomaly detection

Performance 

Elasticsearch uses a distributed architecture to parallelize search operations, leading to quick and relevant results. Its horizontal scalability allows you to add nodes to the cluster, distributing the workload and enhancing performance as data volume increases. It utilizes caching and advanced indexing techniques to optimize query performance, especially for frequently accessed data. 

Being a fork of Elasticsearch, OpenSearch, apart from similar performance optimizations, maintains compatibility while introducing optimizations and improvements. It also has lower latency for search queries. It benefits from ongoing enhancements contributed by the community. 

Elastic publishes benchmarks showing that Elasticsearch is 40–140 % faster for log‑analytics workloads and 2–12× faster for vector search. Independent tests tell a more mixed story: a March 2025 Trail‑of‑Bits benchmark found OpenSearch 2.17.1 faster overall on the “Big 5” workload and moderately faster in default vector search. OpenSearch has focused on query optimisation, segment replication (boosting ingestion throughput by ~25 %) and remote‑backed storage. OpenSearch 3.2 claims a 91 % reduction in query latency compared to version 1.3

Documentation 

Elasticsearch has well-maintained and diverse documentation from blogs, guides, videos, newsletters, a Slack channel, a YouTube channel, and more. If you’re stuck with something while working with ElasticSearch, you can go to their documentation and find an answer immediately. 

OpenSearch, on the other hand, has many gaps since it is new to the market. However, these gaps will likely be filled soon, given the vast community of OpenSearch. 

Elastic offers extensive documentation, blogs, webinars, videos, Slack and an official forum. Its paid customers receive direct support from Elastic engineers. OpenSearch’s documentation has improved; community forums and OpenSearchCon conferences (now hosted by the OSSF) foster knowledge sharing. AWS, Aiven and other vendors provide commercial support.

Pricing 

Both software projects can be self‑hosted for free. Elastic Cloud is the only official managed service for Elasticsearch; its pricing is tiered (Standard, Gold, Platinum, Enterprise) and includes support and advanced features. Amazon OpenSearch Service and third‑party providers like Aiven and Instaclustr offer managed OpenSearch with consumption‑based pricing. OpenSearch includes cost‑saving features like the frozen tier and remote storage as part of the open distribution. For many workloads OpenSearch services are cheaper, but they may lack some of Elastic’s integrated tooling.

Comparison Table 

At Knowi 

Knowi is a Business Intelligence (BI) platform that unifies analytics across structured, unstructured, and semi-structured data. Knowi has firmly established itself as a leader in the Elasticsearch and OpenSearch domains. Knowi also provides Elasticsearch analytics; give it a try if you want to elevate your business data with insights, embedded analytics, and visualizations. Make sure to contact us for any of your database queries. 

Conclusion 

Choosing between Elasticsearch and OpenSearch depends on your requirements, considerations, and values. OpenSearch may be ideal if you prioritize a fully open-source solution with a robust community-driven ethos and are on a budget. We also have to consider the learning curve involved in switching to OpenSearch. However, if you have an existing Elasticsearch infrastructure and are comfortable with the recent licensing changes, sticking with Elasticsearch might make sense. If you’re still confused about which database would suit you, schedule a quick call with Knowi to discuss your requirements. Book a demo today with Knowi and embark on a transformative analytics journey.

Frequently Asked Questions