Elasticsearch vs OpenSearch: A Comprehensive Comparison for 2026

Introduction

The search engine landscape has transformed dramatically since 2021, when OpenSearch emerged as a community-driven fork of Elasticsearch. As we move into 2026, both Elasticsearch and OpenSearch continue to evolve along divergent paths, offering developers, architects, and DevOps engineers two powerful but distinct options for search, observability, and analytics. This tutorial provides a comprehensive, hands-on comparison to help you navigate the licensing, features, performance, and operational realities of both platforms. You will learn what each project is, why the choice matters, how to implement them, and how to apply best practices that keep your stack resilient and cost-effective.

What is Elasticsearch?

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Elasticsearch is a distributed, RESTful search and analytics engine built on Apache Lucene. Originally released in 2010 by Elastic N.V., it became the heart of the Elastic Stack (formerly ELK Stack) alongside Logstash, Kibana, and Beats. Elasticsearch excels at full-text search, structured and unstructured data analysis, log and metrics aggregation, and vector-based similarity search. In recent years, Elastic introduced Elastic License 2.0 alongside the Server Side Public License (SSPL) for newer versions, moving away from the pure open-source Apache 2.0 license. This shift sparked industry debate but also accelerated the development of proprietary features like Elastic Security, native machine learning jobs, and advanced vector search with scalar quantization.

What is OpenSearch?

OpenSearch is an Apache 2.0-licensed search and analytics suite created as a fork of Elasticsearch 7.10.2 and Kibana 7.10.2. Initiated by Amazon Web Services (AWS) and now governed by the OpenSearch community under the OpenSearch Software Foundation, it aims to provide a truly open-source alternative without the licensing restrictions introduced by Elastic. OpenSearch includes a search engine, a visualization dashboard (OpenSearch Dashboards), and a rich set of plugins for security, alerting, anomaly detection, and machine learning. In 2026, OpenSearch has matured significantly, offering comparable performance, a robust k-NN vector engine, and deep integration with AWS services, while remaining completely independent of any single vendor.

Why the Comparison Matters in 2026

Choosing between Elasticsearch and OpenSearch is no longer just a technical decision—it is a strategic one that affects licensing compliance, cost structure, vendor neutrality, and long-term roadmap alignment. As both projects innovate rapidly, the differences in API compatibility, security defaults, observability tooling, and vector search implementations can directly influence your development velocity. Understanding these nuances helps you avoid lock-in, optimize cloud spending, and deliver high-performance search experiences.

Key Differences and Feature Comparison

Licensing and Governance

Elasticsearch: Uses Elastic License 2.0 or SSPL for versions after 7.10.2. Some features (like certain machine learning capabilities) require a commercial license or Elastic Cloud subscription. Source code is available but with restrictions on redistribution as a managed service.
OpenSearch: Entirely Apache 2.0 licensed. No restrictions on usage, modification, or distribution. Community-driven governance through the OpenSearch Software Foundation ensures multi-vendor participation (AWS, Oracle, Aiven, and others).

Core Search and Indexing Capabilities

Both support full-text search, aggregations, percolator queries, and near real-time indexing.
OpenSearch maintains backward compatibility with Elasticsearch 7.x REST APIs, making migration smoother.
Elasticsearch has introduced new composable index templates and data stream enhancements that differ slightly from OpenSearch’s implementation.

Security Features

Elasticsearch: Security features (SSL/TLS, RBAC, audit logging) are free for basic use but advanced features like SSO integration, field-level security, and encrypted snapshots often require a paid tier.
OpenSearch: Security plugin is included out of the box with full RBAC, audit logging, OpenID Connect/SAML integration, and encryption at rest, all free under Apache 2.0.

Vector Search and Machine Learning

Both engines support k-NN vector search using Lucene’s HNSW implementation. Elasticsearch offers native scalar quantization and SIMD acceleration; OpenSearch provides similar performance with its own k-NN plugin, and in 2026 it supports memory-efficient vector compression.
Elasticsearch ships with proprietary trained models for NLP tasks (sentiment analysis, text classification). OpenSearch relies on open-source ML components and an ML Commons plugin for anomaly detection and custom model serving.

Observability and Logging

Elasticsearch benefits from tight integration with Elastic Observability, offering APM, Synthetics, and infrastructure monitoring in a unified experience—but many advanced features require Elastic Cloud or an Enterprise license.
OpenSearch provides OpenSearch Observability (formerly OpenSearch Trace Analytics) with distributed tracing, log analytics, and monitoring dashboards, all freely available.

Performance and Scalability

Both engines scale horizontally, support cross-cluster replication, and handle petabytes of data.
Benchmarks in 2026 show Elasticsearch slightly ahead in raw indexing throughput for very high write loads due to optimized write paths, but OpenSearch matches or exceeds performance for complex query workloads with its efficient caching and tiered storage support.

How to Choose: Decision Framework

Use the following criteria to guide your selection:

Choose Elasticsearch if: You require advanced Elastic Security features, need proprietary NLP models, want the tightest integration with Elastic Cloud, or your organization already uses Elastic’s enterprise observability stack.
Choose OpenSearch if: License compliance (Apache 2.0) is non-negotiable, you want to avoid vendor lock-in, you operate in multi-cloud or AWS-centric environments, or you need a fully open-source stack without any commercial gatekeepers.

Practical Setup and Code Examples

Running Elasticsearch with Docker

Use the following docker-compose.yml snippet to start a single-node Elasticsearch 8.x instance with Kibana:

version: '3.8'
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.13.0
    container_name: es01
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=true
      - ELASTIC_PASSWORD=yourStrongPassword123
    ports:
      - "9200:9200"
    volumes:
      - esdata:/usr/share/elasticsearch/data

  kibana:
    image: docker.elastic.co/kibana/kibana:8.13.0
    container_name: kib01
    ports:
      - "5601:5601"
    environment:
      - ELASTICSEARCH_HOSTS=http://elasticsearch:9200
      - ELASTICSEARCH_USERNAME=kibana_system
      - ELASTICSEARCH_PASSWORD=yourStrongPassword123

volumes:
  esdata:

Start the stack with docker-compose up -d. Verify the cluster is running:

curl -u elastic:yourStrongPassword123 https://localhost:9200/_cluster/health -k

Running OpenSearch with Docker

A similar setup for OpenSearch 2.x with OpenSearch Dashboards:

version: '3.8'
services:
  opensearch:
    image: opensearchproject/opensearch:2.15.0
    container_name: os01
    environment:
      - discovery.type=single-node
      - OPENSEARCH_INITIAL_ADMIN_PASSWORD=yourStrongPassword123
      - plugins.security.disabled=false
    ports:
      - "9200:9200"
      - "9600:9600"
    volumes:
      - osdata:/usr/share/opensearch/data

  dashboards:
    image: opensearchproject/opensearch-dashboards:2.15.0
    container_name: dash01
    ports:
      - "5601:5601"
    environment:
      - OPENSEARCH_HOSTS=https://opensearch:9200
      - OPENSEARCH_USERNAME=admin
      - OPENSEARCH_PASSWORD=yourStrongPassword123

volumes:
  osdata:

Run docker-compose up -d and test with:

curl -u admin:yourStrongPassword123 https://localhost:9200/_cluster/health -k

Indexing and Querying Data

Both engines share similar REST APIs. Create an index and index a document:

# For Elasticsearch (with user/password)
curl -u elastic:yourStrongPassword123 -k -X PUT "https://localhost:9200/books" -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "title": {"type": "text"},
      "author": {"type": "keyword"},
      "published": {"type": "date"}
    }
  }
}'

# Index a document
curl -u elastic:yourStrongPassword123 -k -X POST "https://localhost:9200/books/_doc/1" -H 'Content-Type: application/json' -d'
{
  "title": "Elasticsearch in Action",
  "author": "John Doe",
  "published": "2025-06-01"
}'

For OpenSearch, replace the credentials and endpoint accordingly. The JSON payload remains identical. Perform a simple search:

curl -u admin:yourStrongPassword123 -k -X GET "https://localhost:9200/books/_search" -H 'Content-Type: application/json' -d'
{
  "query": {
    "match": {
      "title": "Elasticsearch"
    }
  }
}'

Migrating from Elasticsearch to OpenSearch

Migration is straightforward because OpenSearch maintains API compatibility. The recommended approach for 2026 is snapshot-based migration:

# Register a snapshot repository in Elasticsearch (source cluster)
curl -u elastic:password -k -X PUT "https://source-es:9200/_snapshot/migration_repo" -H 'Content-Type: application/json' -d'
{
  "type": "s3",
  "settings": {
    "bucket": "my-snapshot-bucket",
    "region": "us-east-1"
  }
}'

# Create a snapshot
curl -u elastic:password -k -X PUT "https://source-es:9200/_snapshot/migration_repo/snap_2026_01" -d'

# Restore snapshot in OpenSearch (target cluster)
curl -u admin:password -k -X POST "https://target-os:9200/_snapshot/migration_repo/snap_2026_01/_restore" -H 'Content-Type: application/json' -d'
{
  "indices": "books,logs-*",
  "include_global_state": false
}'

After restore, reindex or update mappings as needed. Both engines support the same snapshot repository plugins (S3, GCS, Azure, HDFS).

Using Vector Search for AI Applications

Vector search is essential for semantic search and retrieval-augmented generation (RAG). In Elasticsearch:

# Create an index with a dense_vector field
curl -u elastic:password -k -X PUT "https://localhost:9200/embeddings" -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "text": {"type": "text"},
      "embedding": {
        "type": "dense_vector",
        "dims": 768,
        "index": true,
        "similarity": "cosine"
      }
    }
  }
}'

# Index a document with a vector
curl -u elastic:password -k -X POST "https://localhost:9200/embeddings/_doc/1" -H 'Content-Type: application/json' -d'
{
  "text": "Open source search engines",
  "embedding": [0.12, -0.45, ... 766 more values]
}'

# k-NN search
curl -u elastic:password -k -X GET "https://localhost:9200/embeddings/_search" -H 'Content-Type: application/json' -d'
{
  "knn": {
    "field": "embedding",
    "query_vector": [0.01, -0.33, ... ],
    "k": 5,
    "num_candidates": 100
  }
}'

In OpenSearch, the setup is almost identical, using the knn_vector type and the knn plugin:

curl -u admin:password -k -X PUT "https://localhost:9200/embeddings" -H 'Content-Type: application/json' -d'
{
  "mappings": {
    "properties": {
      "text": {"type": "text"},
      "embedding": {
        "type": "knn_vector",
        "dimension": 768,
        "method": {
          "name": "hnsw",
          "engine": "nmslib"
        }
      }
    }
  }
}'

Querying uses the same knn structure, with minor parameter differences for engine configuration. Both engines support hybrid search combining full-text and vector queries.

Best Practices for Development and Operations

Client Libraries and Compatibility

Use the official clients: elasticsearch-py for Elasticsearch and opensearch-py for OpenSearch. However, OpenSearch maintains an opensearch-py compatibility mode that can work with Elasticsearch 7.x clusters. For new projects in 2026, prefer native clients to leverage engine-specific features. Example snippet in Python for OpenSearch:

from opensearchpy import OpenSearch, RequestsHttpConnection

client = OpenSearch(
    hosts=[{'host': 'localhost', 'port': 9200}],
    http_auth=('admin', 'yourStrongPassword123'),
    use_ssl=True,
    verify_certs=False,
    connection_class=RequestsHttpConnection
)

# Create index
client.indices.create(index='my_index', body={'mappings': {...}})

Index Lifecycle Management

Both platforms offer Index Lifecycle Management (ILM) to automate rollover, shrink, and deletion. Define policies and attach them to indexes or index templates. OpenSearch’s ILM plugin is fully compatible with Elasticsearch’s ILM API, making migration seamless. Regularly test your policies in a staging environment to avoid unexpected data loss.

Security Hardening

Regardless of your choice, enable TLS, enforce RBAC with least privilege, and integrate with your identity provider. For OpenSearch, the security plugin is built-in; configure it via opensearch.yml and the securityadmin script. For Elasticsearch, ensure the security features are enabled and use Kibana spaces for multi-tenancy.

Monitoring and Performance Tuning

Monitor cluster health using the /_cluster/health and /_nodes/stats endpoints. Set up alerts for heap usage, thread pool rejections, and long GC pauses. In Elasticsearch, use Kibana Stack Monitoring; in OpenSearch, use OpenSearch Dashboards’ built-in monitoring. For both, adjust JVM heap to 50% of available memory (up to 32GB) and use SSDs for hot nodes. In 2026, tiered storage (hot/warm/cold/frozen) is mature in both engines, drastically reducing costs for older data.

Conclusion

Elasticsearch and OpenSearch represent two powerful paths for search and analytics in 2026. Elasticsearch offers a tightly integrated ecosystem with advanced proprietary features, while OpenSearch delivers a fully open-source, community-driven alternative free of restrictive licensing. Your choice depends on your organizational values, compliance requirements, and feature needs. By understanding the practical setup, migration strategies, and best practices outlined here, you can confidently implement a high-performance search infrastructure that aligns with your long-term technical strategy. Both projects continue to innovate rapidly, so stay engaged with their communities and roadmaps to make informed decisions as the landscape evolves.