For a new enterprise search experience in 2026, compare OpenSearch with Elasticsearch-native search—not with Elastic’s older standalone Enterprise Search, App Search or Workplace Search products. Elastic says those standalone products are in maintenance mode, are not included in Elasticsearch 9.0 and are not recommended for new search experiences. Its current guidance points new projects toward Elasticsearch’s native search capabilities.
OpenSearch is another platform option, with project-described support for hybrid retrieval, RAG, relevance tools and retrieval access controls. Neither platform is the universal winner: the choice depends on your required features, deployment, support model, security design and results on your own data.
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What is being compared?
“Enterprise search” can mean a company-wide search use case or a specific product family. That distinction matters here. Elastic’s standalone Enterprise Search, App Search and Workplace Search products remain in maintenance mode and are not recommended for new search experiences. Elastic says they will not be included in Elasticsearch 9.0 and recommends Elasticsearch-native tools for new catalog and internal knowledge search.
Elastic’s download page lists Enterprise Search 8.19.22 with a September 23, 2026 release date. That is a dated listing for the standalone product line, not evidence that 8.19.22 is the newest Elastic Stack component overall. For a new comparison, pin the exact Elasticsearch release and deployment you plan to use, then assess its native features and entitlements.
#1 Best Overall
OpenSearch is positioned by its project as an open platform for enterprise search. The comparison, therefore, is between Elasticsearch as a search platform and OpenSearch as a search platform—not two equally current standalone Enterprise Search suites.
How do the platforms differ at a glance?
| Decision area | Elasticsearch | OpenSearch |
|---|---|---|
| Product direction for new search | Elastic recommends Elasticsearch-native tools. Standalone Enterprise Search, App Search and Workplace Search are in maintenance mode and not recommended for new experiences. | The OpenSearch Project presents OpenSearch as a platform for enterprise search use cases. |
| Retrieval approach | Elastic documents full-text, vector, semantic and hybrid search, as well as reranking and query interfaces including retrievers and ES|QL. | The project describes BM25 and vector-based semantic retrieval, hybrid search, RAG and agentic workflows. |
| Relevance work | Documented search and reranking capabilities can be assessed against your relevance objectives and supported release. | The project describes relevance comparison tools and scoring explainability. Feature names alone do not establish result quality. |
| Licensing and feature access | Available features and support depend on license or subscription; details vary by deployment. | The project describes OpenSearch as Apache 2.0 licensed with no software licensing fees. Managed-service, support and operating expenses are separate. |
| Deployment choices | Self-managed, hosted and Serverless options have different feature availability and controls; check the matrix for the planned service and release. | The project describes self-managed, on-premises, hybrid and multicloud use. Amazon OpenSearch Service is an AWS managed deployment option. |
| Performance and total cost | No neutral, controlled head-to-head performance or cost result is established here. | No neutral, controlled head-to-head performance or cost result is established here. |
How do search and relevance capabilities compare?
Elasticsearch
Elastic documents lexical full-text search alongside vector and semantic search, hybrid retrieval, reranking and query interfaces such as retrievers and ES|QL. Those capabilities give teams several ways to build and refine retrieval, but the exact availability depends on release, subscription and deployment. Check Elastic’s current feature and deployment documentation for the configuration you would actually run.
Rank #2
OpenSearch
The OpenSearch Project describes combining BM25 with vector search for hybrid retrieval, connecting retrieval to language models through RAG pipelines, and supporting agentic multi-step workflows. It also describes relevance evaluation and scoring explainability. These are project capabilities and positioning, not independent proof that a particular workload will produce more relevant results.
For either platform, compare the quality of returned results on representative queries, not just whether a feature appears in a product description. Analyzer choices, document structure, ranking rules, filters and the query mix all affect the experience. A feature checklist cannot tell you whether users will find the right document.
Rank #3
What should you check for enterprise security and governance?
OpenSearch’s enterprise-search positioning includes document- and field-level retrieval access controls. Treat those as capabilities to validate in your intended release and configuration, not as a guarantee that an application is secure by default. Access controls must be designed and tested against your identity model and the permissions on the source data.
Elastic’s feature and deployment documentation shows that some security capabilities vary between self-managed, hosted and Serverless configurations, and that entitlements depend on subscription. Verify the controls available for your exact plan and deployment rather than assuming every Elastic installation has the same feature set.
For both candidates, test authorization at retrieval time: use users with different permissions, include document- and field-sensitive cases, and check that search results and any downstream generated answers do not reveal content those users should not see. Separately map your audit, governance, data-residency and compliance requirements to the offering and operating model. A platform feature description alone does not establish compliance.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesHow do licensing, hosting and operating costs compare?
OpenSearch software is described by its project as Apache 2.0 licensed and available without licensing fees. That does not make a production deployment cost-free: infrastructure, storage, managed-service consumption, paid support, upgrades, backups, monitoring and staff time are separate expenses. AWS documents Amazon OpenSearch Service as a managed way to deploy, operate and scale OpenSearch; managed operation changes who handles parts of the work, not whether there is a service bill.
With Elastic, a license or subscription determines available features and support. The scope at which the subscription applies differs across cloud and self-managed deployments, and capabilities also vary across hosted, self-managed and Serverless configurations. Use Elastic’s live entitlement and deployment information for the specific release and service tier under consideration; a single feature list or price would not apply universally.
Compare total cost over the same period and at the same service level. Include licenses, support, managed-service charges, compute and storage, engineering and operations time, upgrades, recovery planning and migration work. No comparable total-cost study or neutral cost benchmark is established here, so a general claim that one platform is cheaper would be unjustified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is OpenSearch a drop-in replacement for Elasticsearch?
There is no universal yes-or-no answer established for compatibility. Client and API behavior, plugins, integrations, ingestion paths and migration effort can depend on the versions and components involved. Do not assume that existing Elasticsearch code, connectors or operational procedures will work unchanged with a particular OpenSearch release—or the reverse.
Inventory the APIs, clients, plugins, connectors and integrations your application actually uses. Then test those components against the exact target versions, including indexing, query behavior, authentication, authorization and recovery procedures. Include data conversion and reindexing effort in the migration estimate.
How should you choose between them?
- Define the workload. Record corpus size and document shape, indexing and update rates, query patterns, expected concurrency, latency objectives, availability needs and the security filters each user requires.
- Set measurable success criteria. Prepare relevance judgments for representative queries, latency percentiles, indexing targets, permission tests and cost boundaries. Decide what counts as acceptable before comparing results.
- Pin the candidates. Select exact product versions and deployment forms. For Elasticsearch, verify feature entitlements and deployment differences. For OpenSearch, decide whether you will operate it yourself or use a managed service.
- Test with the same evidence. Use a representative corpus, query set, user-permission model and workload on each candidate. Compare result quality, security-filter behavior, performance and operational behavior under the conditions you expect in production.
- Estimate the full operating model. Account for software or service charges, support, infrastructure, staff capacity, upgrades, backups, monitoring and any migration work.
- Choose against your constraints. Select the offering that meets required functionality, risk posture, operating capacity and cost at the service level you need—not the one with the broadest feature list.
Which option is the better fit?
Consider Elasticsearch-native search when
- You want Elastic’s current recommended path for a new catalog or internal knowledge search experience.
- The documented full-text, vector, semantic or hybrid capabilities fit your retrieval needs, and the required features are available under your planned subscription and deployment.
- Your team has a clear reason to use Elastic’s hosted, Serverless or self-managed offering and has verified its feature and support entitlements.
Consider OpenSearch when
- You want to evaluate an Apache 2.0-licensed platform and can account separately for infrastructure, support and operations.
- The project-described hybrid retrieval, RAG, relevance tools or access-control capabilities match your use case and pass tests on your own corpus and permissions.
- Your deployment requirements fit self-management or a managed option such as Amazon OpenSearch Service.
These are starting points for evaluation, not outcome guarantees. Neither a project’s stated capabilities nor the licensing model by itself establishes relevance, security, lower cost or better performance for your workload.
Quick Recap
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