ELK Stack vs. OpenSearch: Which Log Management Platform Should You Choose?
Country Manager, Sweden
AI, DevOps, Security, and Cloud Solutioning. 12+ years leading enterprise cloud transformation across Scandinavia
ELK Stack vs. OpenSearch: Which Log Management Platform Should You Choose?
In 2021 Elastic relicensed Elasticsearch and Kibana from Apache 2 to a dual SSPL/Elastic-License model. AWS forked the projects, named the fork OpenSearch, and re-released the codebase under Apache 2. The two projects have diverged since, but at the architectural level they remain 90%+ compatible. For teams choosing a log-management platform in 2026, the decision is no longer "ELK or not" — it is "which of the two ELK-derived stacks fits your operating model."
This is a practical, side-by-side comparison drawing on what we see in actual customer deployments. We will cover features, performance, licensing, ecosystem, and the operational realities that move the needle one way or the other.
Origin and Licensing
Elasticsearch and Kibana are now released under either the SSPL (Server Side Public License) or the Elastic License. Both are non-OSI-approved licenses; both forbid offering the software as a managed service that competes with Elastic. For most users running ELK internally this is irrelevant. For users who want to embed the software in a SaaS product, or who require strict open-source provenance for procurement reasons, it is decisive.
OpenSearch and OpenSearch Dashboards are Apache-2 licensed. AWS leads the project but the OpenSearch Software Foundation (under the Linux Foundation, since 2024) governs it. There are no commercial-use restrictions. AWS, Aiven, Bonsai, and others offer managed OpenSearch services that compete on price and operating model.
Feature Parity Today
The cores diverged starting at Elasticsearch 7.10 / OpenSearch 1.0 in 2021. The deltas now matter for advanced users.
| Capability | Elastic Stack | OpenSearch |
|---|---|---|
| Vector search (kNN) | Yes — strong, with Elastic's own ranking | Yes — kNN plugin, FAISS or Lucene backends |
| SIEM / security analytics | Mature, with built-in detection rules and ML jobs | Security Analytics plugin, smaller rule library |
| APM / observability | Elastic APM, fully integrated | Trace Analytics on OpenTelemetry data |
| Machine learning | Anomaly detection, classification, regression — commercial tier | Anomaly detection plugin, fewer model types |
| RBAC / field security | Built-in, requires Platinum subscription for SAML / LDAP at scale | Built-in via Security plugin, free, includes SAML / LDAP / OIDC |
| Index lifecycle management | Mature, broad ILM policy expressiveness | ISM plugin, equivalent for most workloads |
For most log-management deployments — ingest, search, dashboards, alerts — the two stacks are interchangeable. The decision criteria sit on the edges: vector search quality, advanced ML, SIEM rule maturity, and licensing posture.
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Performance
Independent benchmarks (most recently OpenSearch's own benchmarks, plus the rally benchmarks Elastic publishes, plus third-party tests by Logz.io and Aiven) consistently show:
- Indexing throughput: roughly equivalent for typical log workloads, with slight edges to OpenSearch on bulk ingest after the OpenSearch 2.x optimisations
- Search latency: roughly equivalent for term and match queries, with Elastic edging ahead on aggregations against very large datasets
- Vector search: Elastic ahead in 2024-25 on accuracy at small k, OpenSearch closing rapidly with FAISS-based kNN improvements
Performance is rarely the deciding factor. Both platforms handle multi-terabyte daily ingest reliably when sized and tuned correctly.
Ecosystem and Tooling
Elastic Stack has the larger commercial ecosystem: Elastic Cloud (managed, on AWS / Azure / GCP), Beats family for collection, Elastic APM agents in 14 languages, and integrations into hundreds of vendor products. Beats, Logstash, and the Elastic agents are still the dominant collection tools — including in many OpenSearch deployments, because they ingest cleanly into both backends.
OpenSearch ecosystem maturity caught up rapidly post-fork. AWS managed OpenSearch (formerly Amazon Elasticsearch Service) is the largest commercial deployment surface. Aiven, Bonsai, and Logit.io offer managed OpenSearch alternatives. Data Prepper is the OpenSearch-native ingest pipeline; Vector and Fluent Bit are language-agnostic options used across both stacks.
The Decision Framework
From customer engagements over the past 18 months, the choice usually breaks along three axes.
Pick Elastic Stack when: you need Elastic's commercial security analytics or APM features, you already have deep Elastic operational expertise in-house, you want vendor-managed Elastic Cloud, or you depend on advanced ML features only Elastic ships.
Pick OpenSearch when: you want pure Apache-2 licensing, you are comfortable with AWS or another OpenSearch-managed-service vendor, you do not need Elastic's commercial-tier features, or you want SAML / LDAP without an Elastic Platinum subscription.
Either works equally when: the use case is straightforward log management with dashboards and basic alerting, ingest volume is under ~5 TB/day, and there are no hard licensing requirements either way. Most mid-market deployments fall here.
Migration Between the Two
Migration in either direction is operationally manageable. Snapshots taken in Elasticsearch up to 7.10 restore cleanly into OpenSearch 1.x. Beyond that point, you need a logical migration: re-index from one cluster to the other using the cross-cluster reindex API or a tool like elasticdump. Dashboards and saved objects export cleanly via Kibana / OpenSearch Dashboards APIs, with some dashboard JSON edits required for plugin-specific visualisations.
Plan 4-8 weeks for a migration of a meaningfully sized cluster, primarily because of the data backfill rather than the platform switch itself. Most customers run the two clusters in parallel for the cutover window.
How Opsio Helps
Opsio operates both Elastic Stack and OpenSearch deployments under managed-service contracts, and our how Opsio delivers elk stack practice supports either backend transparently. Customers in regulated sectors typically prefer Elastic Cloud for vendor-side compliance certifications; cost-sensitive customers and those with strict OSI-license requirements typically prefer OpenSearch on managed services. We tie either stack into broader end-to-end datadog monitoring or Prometheus observability programmes where appropriate.
About the Author
Country Manager, Sweden
Johan leads Opsio's Sweden operations, driving AI adoption, DevOps transformation, security strategy, and cloud solutioning for Nordic enterprises. With 12+ years in enterprise cloud infrastructure, he has delivered 200+ projects across AWS, Azure, and GCP — specialising in Well-Architected reviews, landing zone design, and multi-cloud strategy.
Editorial standards: This article was written by a certified practitioner and peer-reviewed by our engineering team. We update content quarterly to ensure technical accuracy. Opsio maintains editorial independence — we recommend solutions based on technical merit, not commercial relationships.