OpenSearch vs. Elasticsearch in 2025 - What’s Changed and What Hasn’t
Source: https://dattell.com/data-architecture-blog/opensearch-vs-elasticsearch-in-2025-whats-changed-and-what-hasnt/ Publisher: Dattell (managed-services vendor supporting both platforms) Published: 2025-04-03
A balanced Elasticsearch vs. OpenSearch comparison across licensing, features, plugins, performance, and ecosystem — from a vendor that operates both.
Key points
- Licensing/governance — OpenSearch is Apache 2.0 (OSI-recognized; attractive for compliance, redistribution). Elasticsearch uses SSPL since 7.11 (not OSI-recognized), introduced to counter cloud providers; this pushed many users toward OpenSearch.
- Feature divergence — OpenSearch ships integrated Dashboards (tenant separation, fine-grained access), built-in alerting/anomaly detection, community ML, and a strong SIEM/security suite. Elasticsearch has more refined native ML (data-frame analytics, outlier detection, forecasting), autoscaling, APM via Elastic Observability, and polished proprietary features (Elastic Maps, searchable snapshots) — some gated behind paid tiers.
- Plugins — largely incompatible across the fork; migrations often require re-architecture in security, monitoring, visualization.
- Performance — recent Elasticsearch optimizations (adaptive replica selection, segment-merge tuning, searchable snapshots, heap management). OpenSearch stays highly configurable but several advanced features (tiered/frozen storage, advanced caching) are still maturing or plugin-based.
- Ecosystem — Elastic offers one-vendor full-stack (Observability, Security, Cloud); OpenSearch thrives in open observability (Grafana, Fluentd, Prometheus, Jaeger, Kubernetes logging; AWS OpenSearch Service).
When to use which
- OpenSearch — licensing flexibility, cloud-native observability, deep customization (DevOps/SRE).
- Elasticsearch — commercial-grade ML, advanced search features, tightly integrated vendor-managed experience.