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Elasticsearch vs OpenSearch in 2025: What the Fork?

Elasticsearch vs OpenSearch in 2025: What the Fork?

A 2025 Perspective on the Ongoing Search Tech Divide

This blog is a 2025 update to our Elasticsearch vs. OpenSearch blogs first published in 2022. A lot has changed in the search landscape, so we thought this was worth revisiting.

Since the licensing fork in 2021, the battle between Elasticsearch and AWS OpenSearch has become more than just a story about open source. It’s a case study in how AI and search infrastructure decisions impact long-term flexibility, performance, and cost—especially for enterprises scaling intelligent applications.

The Backstory: Licensing Sparks a Fire

The rift began when Elastic changed the license for Elasticsearch and Kibana from the permissive Apache 2.0 to the Server Side Public License (SSPL). Elastic’s move was aimed at preventing cloud providers from offering Elasticsearch “as a service” without contributing to the project. AWS responded by forking both projects and launching OpenSearch, keeping it under Apache 2.0—and thereby preserving the ability to offer a managed service.

This was more than a licensing squabble. It marked a clear divergence in vision: Elastic focused on innovation under tighter control, while AWS aimed to keep things open and tightly integrated with its cloud ecosystem.

New in 2024: Elastic Adds AGPL to the Mix

In a major licensing update in late 2024, Elastic added the GNU AGPLv3 license to the Elasticsearch and Kibana source code, alongside SSPL and Elastic License v2 (ELv2). AGPLv3 is OSI-approved and is considered a “true” open-source license, which helps address community concerns about openness and license ambiguity.

This addition gives users more flexibility and could make Elasticsearch more palatable for teams that have strict policies requiring OSI-approved licensing. While AGPL still carries copyleft obligations (especially around SaaS use), it signals Elastic’s intent to re-engage the open-source community and counter OpenSearch’s positioning.

Performance: It Depends

Elastic has published benchmarks showing Elasticsearch outperforms OpenSearch by 40%–140%, while consuming fewer compute resources. From our own testing at Pureinsights, that performance gap is often real—especially at enterprise scale or under complex query loads.

That said, many organizations won’t notice a dramatic difference until they push the limits of throughput or analytics complexity. For simpler log analytics or moderate-size search use cases, OpenSearch delivers solid performance with the added benefit of being fully managed in AWS.

Ecosystem and Integration

This is where OpenSearch has leaned into its advantage. It integrates natively with AWS services like IAM, KMS, and CloudWatch, making it an easy choice for teams already deep in the AWS ecosystem. It’s also backed by a growing library of AWS-authored plugins and features, such as observability dashboards and anomaly detection.

Elasticsearch, meanwhile, continues to evolve its full Elastic Stack, including proprietary features like Elastic Security and Elastic APM. It’s a more comprehensive platform for companies that need fine-grained tuning and advanced search capabilities—and are willing to navigate licensing considerations.

Elasticsearch vs Opensearch 2025 update

Governance: The Linux Foundation Effect

In a strategic shift last year, AWS handed off OpenSearch governance to the Linux Foundation, establishing the OpenSearch Foundation in September 2024. This move signals a commitment to community-driven development and adds credibility for organizations wary of vendor lock-in—even when that vendor is AWS.

Elastic, for its part, remains a strong independent player with a tight roadmap and active investment in AI integrations.

What About Solr, Vespa, and Other Alternatives?

While Elasticsearch and OpenSearch dominate headlines, they’re not the only options—especially as vector search and AI integration become core to enterprise search strategies.

  • Apache Solr still powers many large-scale search applications and is known for its stability, customizability, and deep roots in the Lucene ecosystem. It’s a solid choice for traditional keyword and faceted search use cases, especially in on-prem environments.
  • Vespa.ai, developed by Yahoo and now fully open source, is gaining attention for its native support of hybrid search (vector + lexical), large-scale on-the-fly inference, and deeply distributed architecture. It’s a compelling platform for building real-time, AI-powered apps at scale.
  • MongoDB Atlas Search  now includes built-in vector search alongside Lucene-powered full-text search, enabling hybrid queries directly within MongoDB. It’s a great fit for teams already using MongoDB as their primary data store, simplifying architecture for AI-powered apps like semantic search and RAG.
  • Other emerging players like Weaviate, Qdrant, Milvus, and Pinecone focus on vector-first architectures, excelling at semantic similarity search. However, many of these solutions struggle with hybrid search, where combining keyword and vector relevance is critical. They’re often best used as components in a broader GenAI or retrieval-augmented generation (RAG) stack, rather than full replacements for traditional search engines.

At Pureinsights, we often design hybrid architectures that combine the best of both worlds: traditional search engines for structured retrieval, and vector databases for semantic and AI-driven experiences.

So, Which Should You Choose?

At Pureinsights, we advise clients to start by asking four simple questions:

  1. Where do you run most of your infrastructure?
    If you’re all-in on AWS, OpenSearch is compelling—especially for log analytics or simpler search implementations.
  2. Do you need advanced features like ML-powered relevance tuning or full-stack observability?
    Elasticsearch is the stronger player here, assuming your team can navigate its licensing and deployment model.
  3. How important is open-source purity or community governance?
    OpenSearch now has the Linux Foundation backing. But with the AGPLv3 option, Elastic is signaling that it’s recommitting to openness—albeit on its terms. 
  4. How important is cost predictability and operational overhead?
    Both Elasticsearch and OpenSearch can be self-hosted at no licensing cost, which appeals to teams with strong DevOps capabilities. However, OpenSearch via AWS often provides a more predictable and streamlined cost model. Elasticsearch’s commercial features may justify their cost for teams that need them—but understanding the trade-offs is key to long-term sustainability.

And don’t ignore the alternatives: if your search use case is evolving toward GenAI or real-time inference at scale, it may be time to look at Vespa.ai or explore a hybrid stack that includes vector-native tools.

Final Thoughts: The Fork Has Created Choice—Use It Wisely

As in 2025, the “war” between Elasticsearch and OpenSearch in 2025 isn’t about picking winners. It’s about understanding trade-offs in flexibility, performance, licensing, and ecosystem alignment. And as AI-driven search continues to evolve, we’re helping organizations pick the right stack not just for today’s workloads, but for what’s coming next.

Have questions about how to choose between OpenSearch and Elasticsearch—or what a hybrid AI + Search stack might look like for your business? Let’s Talk.

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