Elasticsearch vs OpenSearch
Compare Elasticsearch and OpenSearch for search applications, vector retrieval, client and plugin compatibility, deployment and operating costs.
Which database approach fits the workload?
Consider Elasticsearch when its current APIs, search capabilities and chosen Elastic deployment match the application. Consider OpenSearch when the project’s engine, plugins and selected self-managed or provider arrangement match the brief. Their shared history does not establish current drop-in compatibility. Evaluate the actual query set, mappings, clients and migration route rather than choosing from the project names alone.
Seven differences that change the shortlist
| Buying question | Elasticsearch | OpenSearch |
|---|---|---|
| Product boundary | Elastic search engine with selected subscription and cloud options | OpenSearch project engine, Dashboards and related tooling |
| Search brief | Text, structured and vector retrieval in the chosen version | Search and vector capabilities through the chosen version and plugins |
| Application interface | Validate the current Elasticsearch client and API behaviour | Validate the current OpenSearch client and API behaviour |
| Visual tooling | Specify the required Kibana and surrounding solution scope | Specify Dashboards and the required plugins |
| Migration | Check mappings, queries, pipelines, snapshots and extensions | Check source compatibility and the proposed migration path |
| Free access | Free self-managed Basic; paid features and cloud differ | Free project software; managed providers and support differ |
| Operating bill | Deployment, indexed data, retention, query and optional service scope | Cluster or provider costs, storage, ingestion, operations and support |
Four worked buying briefs
These are hypothetical scenarios with explicit assumptions. They do not establish equal capabilities, measured usability or a negotiated price.
| Requirement | Proposed exercise | Accept when |
|---|---|---|
| Relevance | Review a fixed labelled query set | The application owner can explain acceptable and failed results |
| Freshness | Correct a test record and trace its indexed version | The expected record becomes visible within the stated requirement |
| Compatibility | Exercise real application calls and required extensions | Supported versions and remaining code changes are recorded |
| Migration | Move a representative collection in isolation | Counts, mappings, query behaviour and rollback are verified |
A relevance set
Do agreed search intents return useful results?
Elasticsearch: Run a labelled set against the intended Elasticsearch configuration.
OpenSearch: Run the same set against the intended OpenSearch configuration.
Include exact names, misspellings and filtered results; agree relevance judgements before comparing output.
A changed document
When does a corrected record become searchable?
Elasticsearch: Trace ingestion, indexing and the application read path.
OpenSearch: Trace the equivalent ingestion and indexing path.
Use an approved sandbox record and record the application’s freshness requirement.
An application client
Can the real application use required queries and plugins?
Elasticsearch: Exercise its client, query and extension versions.
OpenSearch: Exercise the proposed replacement client, queries and plugins.
A successful basic request does not establish full API compatibility.
A controlled migration
Can a representative collection be moved and verified?
Elasticsearch: Export or reindex using the supported proposed path.
OpenSearch: Import or reindex using the supported proposed path.
Compare document counts, mappings, queries and rollback steps.
Define search quality before comparing engines
Build a small relevance set around actual search intents. Include a product code, a misspelling, an ambiguous phrase and a filter that must exclude unavailable records. Assign acceptable results before tuning either configuration.
Vector retrieval adds choices about embeddings, filtering and ranking. Scope any external model or inference service separately and retain the query, configuration and expected judgement with the evaluation.
Treat migration as an application change
Elasticsearch and OpenSearch have a shared project history, but current clients, plugins, mapping behaviour and features must be checked against specific versions. Do not infer compatibility from a similar endpoint or query example.
Write a migration inventory covering mappings, ingestion pipelines, dashboards, access controls and application calls. Include a verified data path, a change window and a rollback plan before estimating the move.
Questions before you choose
Is OpenSearch the same service as Amazon OpenSearch Service?
No. OpenSearch is the project software. Amazon OpenSearch Service and other managed offers have separate hosting, features and commercial conditions.
Can I swap current Elasticsearch and OpenSearch without changes?
Do not assume that. Validate the actual clients, versions, APIs, plugins and migration route used by the application.
Which search engine is more relevant?
This page reports no relevance benchmark. Use a fixed labelled query set and agreed judgements with the actual configurations.
Your practical evaluation pack
Download the database evaluation checklist (CSV)
Four proposed checks with blank evidence and owner fields. Record the exact version, edition, region, deployment and workload used.
Turn the comparison into your own evaluation
Use the same criteria and priorities for both products, keep unknowns unassessed and record the plan and evidence behind your ratings.
Build a Elasticsearch vs OpenSearch scorecard →Keep the assumptions with the figures
Use the date, billing cycle, units and limits with each example. Check current account terms before purchasing. Unverified prices and model costs stay unpriced.
Download the buying examples as JSON ↓Share the comparison
Use these examples when discussing the shortlist with your team.