Elasticsearch vs OpenSearch: Which is Better in 2026?

This is the crux. Elasticsearch ships under the Elastic License v2 and SSPL — source-available, not OSI open source — with an AGPLv3 option added for the core in 2024; Elastic controls the roadmap and reserves advanced features for paid tiers. OpenSearch is Apache-2.0, a permissive, OSI-approved open-source license with no usage restrictions, and since September 2024 it’s stewarded by the OpenSearch Software Foundation under the Linux Foundation.

For teams building products on top of a search engine, or wary of another SSPL-style relicensing, that governance guarantee is the whole argument. OpenSearch’s Linux Foundation home means the project is community-driven and vendor-neutral; Elasticsearch’s direction is set by a single commercial company (which cuts both ways — focused investment, but also a paywall).

Because OpenSearch bundles everything under Apache-2.0, its security suite — fine-grained access control, encryption, alerting, anomaly detection — is included for free. In the Elastic world, some of these capabilities historically sat behind paid subscription tiers, so an apples-to-apples “free” comparison often favors OpenSearch on total included functionality.

Elasticsearch counters with breadth and polish at the top end: a more advanced ML stack, ES|QL query language, deep observability/APM tooling, and faster iteration on new capabilities. If you want the most feature-complete platform and are willing to pay for tiers, Elastic tends to be ahead; if you want maximum capability at zero license cost, OpenSearch wins.

Vector search is where Elastic has pushed hardest. Elasticsearch has invested heavily in dense-vector/kNN performance and quantization (int8/BBQ), and Elastic’s own benchmarks claim large kNN speed advantages (up to ~12x on some workloads) — though independent tests often show a much smaller or reversed gap, so treat vendor numbers with caution.

OpenSearch has closed ground fast: OpenSearch 3.x added GPU-accelerated vector search and a solid k-NN plugin with multiple engines (Lucene, Faiss, NMSLIB). For RAG and semantic search in 2026 both are viable; Elastic is generally considered the more cutting-edge on vectors, while OpenSearch offers a strong, free, and rapidly improving alternative.

OpenSearch itself costs nothing — Apache-2.0, self-host free, with managed options from AWS (Amazon OpenSearch Service) and others. If you’re on AWS, that native integration is the natural path and you pay only for infrastructure plus the managed-service margin.

Elasticsearch’s core is free to self-host, but the polished path is Elastic Cloud (managed hosting and serverless) with usage-based pricing, and the most valuable ML/security features live in paid tiers. You’re buying a commercial platform experience. The trade-off: Elastic Cloud is multi-cloud and turnkey, while OpenSearch’s best managed story is AWS-centric.