OpenSearch vs. Elasticsearch in 2025: What’s Changed and What Hasn’t

OpenSearch and Elasticsearch share a common origin, but in 2025, they’re very different platforms. As OpenSearch matures under the community-driven open source model and Elasticsearch continues to evolve under Elastic’s more restrictive license, the gap is widening. Choosing between them involves weighing tradeoffs in licensing, feature sets, performance, extensibility, and ecosystem support.

At Dattell, we support both OpenSearch and Elasticsearch, so our comparison is grounded in hands-on experience with each—giving you a fair and balanced perspective.

Licensing & Governance

OpenSearch is fully licensed under Apache 2.0, making it highly attractive for companies that require open-source compliance, legal clarity, and freedom to modify or redistribute the codebase. This licensing flexibility has helped it become a go-to for cloud-native observability platforms, public-sector deployments, and SaaS providers.

Elasticsearch, since version 7.11, uses the Server Side Public License (SSPL), which Elastic introduced in response to cloud providers commercializing the stack without contributing back. However, SSPL is not recognized as an open source license by the Open Source Initiative (OSI), and its legal limitations have pushed many users and projects toward OpenSearch.

Feature Set

Both platforms share a solid foundation of full-text search, distributed indexing, and powerful querying. But they’ve diverged significantly:

OpenSearch adds:

Integrated Dashboards (formerly Open Distro Kibana), which support tenant separation and fine-grained access control.
Alerting and Anomaly Detection, built into the core distribution.
Customizable ML workflows through community-driven plugins.

Strong security and SIEM capabilities come preloaded with a full suite of security features. More information available here: OpenSearch SIEM.

Elasticsearch adds:

Native Machine Learning, such as data frame analytics, outlier detection, and forecasting—deeply integrated and refined.
Autoscaling capabilities, automatically adjusting data and ML nodes to maintain performance.
Application Performance Monitoring (APM), integrated with Elastic Observability Suite, providing turnkey insights.

Elasticsearch also benefits from UI polish and proprietary features like Elastic Maps and searchable snapshots that allow cold storage querying with performance tradeoffs. Also, keep in mind that some of Elasticsearch’s features are not included in their free license.

Plugin Compatibility

Plugins are where the fork really shows. Many Elasticsearch plugins, especially those from Elastic (like their commercial ML or APM agents), are not supported in OpenSearch. Similarly, OpenSearch-specific plugins (e.g., SQL engine, security dashboards) don’t run on Elasticsearch.

This plugin incompatibility means migrations between the two often require substantial re-architecture, particularly in security, monitoring, and visualization. If you are migrating to OpenSearch, learn about zero downtime OpenSearch migrations.

Performance & Tuning

Out-of-the-box, both perform well for general indexing and search workloads, but recent Elasticsearch versions have introduced significant optimizations:

Adaptive replica selection improves query latency by routing requests to the least-loaded node.
Segment merging optimizations reduce indexing overhead and GC pressure.
Searchable snapshots enable performant querying on low-cost storage tiers.
Heap management improvements reduce memory pressure from large field data.

OpenSearch has remained highly configurable, appealing to power users who want deep control. However, many advanced performance features (like advanced caching strategies, frozen tier support, or tiered storage) are still maturing or require community plugins.

Ecosystem

Elasticsearch benefits from Elastic’s full-stack integrations:

Elastic Observability (logs, metrics, traces)
Elastic Security (SIEM)
Elastic Cloud (turnkey SaaS hosting)

These make Elasticsearch attractive for teams looking for one-vendor simplicity. Elasticsearch is supported by the company itself, along with several other experienced vendors, including us. We provide fully managed Elasticsearch in our clients’ environments, cloud or on-prem.

OpenSearch is thriving in the open-source observability world:

Integrates easily with Grafana, Fluentd, Prometheus, and Jaeger
Supported by AWS OpenSearch Service and several third-party managed offerings. For instance, we offer fully managed OpenSearch services in our clients’ environments.
Gaining popularity in the Kubernetes ecosystem as a logging backend

When to Use Which?

Use OpenSearch when licensing flexibility, cloud-native observability, and community-driven innovation matter most. It’s especially compelling for DevOps and SRE teams who want to deeply customize their stack.
Use Elasticsearch when you need commercial-grade ML, advanced search features, or prefer a tightly integrated vendor-managed experience.

Summing it up

In 2025, OpenSearch and Elasticsearch have become tailored to different priorities. OpenSearch excels in openness, extensibility, and cost control. Elasticsearch currently has the edge on certain enterprise-scale analytics and polished observability suites. The right choice depends on your team’s architecture, budget, and compliance needs.

If you’d like help deciding between Elasticsearch or OpenSearch, schedule a call with one of our engineers to review which might be best for your use case.

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OpenSearch vs. Elasticsearch in 2025: What’s Changed and What Hasn’t