OpenSearch vs Elasticsearch — Which One to Use in 2026

In 2021, Elastic changed its license from Apache 2.0 to SSPL (Server Side Public License) — a move that AWS called too restrictive. AWS forked Elasticsearch 7.10 and created OpenSearch. Both have evolved significantly since.

In 2026, choosing between them is less obvious than it used to be. Here's the full picture.

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Quick Summary

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Licensing: The Core Difference

This is what started the split and still matters in 2026.

Elasticsearch (Elastic License 2.0 + SSPL):

• Free to use for most purposes
• Cannot be used to offer Elasticsearch as a managed service without Elastic's permission
• SSPL requires open-sourcing your entire application if you make Elasticsearch's functionality available as a network service
• Basically: you can use it internally, but you can't build a competing SaaS on it

OpenSearch (Apache 2.0):

• Fully open source — no restrictions
• Anyone can use it, modify it, sell it, build products on it
• AWS, community contributors, and other vendors can all build managed services

Why it matters:

• If you're building a SaaS product that includes search/logging: OpenSearch is safer legally
• If you're just using it internally: both are fine
• If you want contributions and features from the broadest community: Elasticsearch still has a larger ecosystem

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Feature Comparison (2026)

Search Capabilities

Both started from the same codebase (ES 7.10) but have diverged:

Elasticsearch:

• ESRE (Elasticsearch Relevance Engine) — combines BM25 + vector search + ML ranking
• Learned Sparse Retrieval (ELSER) — zero-shot sparse vector model for semantic search
• Reciprocal Rank Fusion — combines multiple search results intelligently
• Very strong full-text search with analyzers, boosting, and relevance tuning

OpenSearch:

• k-NN (k-Nearest Neighbor) plugin for vector search
• Neural search pipeline with transformer model support
• Hybrid search (BM25 + vector) built-in
• Improving rapidly but still slightly behind Elasticsearch for advanced search relevance

Verdict: For pure search applications, Elasticsearch has a slight edge on advanced relevance. For logging/observability, they're roughly equivalent.

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Observability and Logging

This is where most DevOps engineers use these tools.

OpenSearch:

• Strong observability features (Trace Analytics, Log Analytics)
• PPL (Piped Processing Language) — SQL-like syntax for log queries
• Anomaly detection built-in (free)
• Alerting built-in (free)
• OpenSearch Dashboards — Kibana fork, all features free

Elasticsearch:

• Elastic Observability — APM, logs, metrics, synthetics (mostly requires paid license for production)
• Machine learning anomaly detection (paid in self-hosted)
• Kibana with more polished UI
• Elastic Agent — unified data collection

Verdict: OpenSearch gives you more features for free in the observability space. Elasticsearch's observability suite is more polished but most good features require Elastic Cloud or a paid license.

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Vector Search and AI (2026)

Both have invested heavily in vector search for AI applications.

Elasticsearch:

• Native dense vector search with HNSW index
• ELSER (Elastic Learned Sparse EncodeR) — semantic search without needing your own model
• Elastic AI Assistant — AI-powered search (Elastic Cloud)
• Integration with OpenAI, Azure OpenAI, Amazon Bedrock

OpenSearch:

• k-NN plugin with FAISS, NMSLIB, Lucene backends
• Neural search pipeline — runs sentence transformers directly in OpenSearch
• ML Commons — deploy and run ML models inside OpenSearch
• Integration with SageMaker, Bedrock, Cohere, OpenAI

Verdict: Both are competitive for vector search in 2026. Elasticsearch's ELSER is unique (no external model needed). OpenSearch's ML Commons gives more flexibility for self-hosted AI pipelines.

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Managed Services

Amazon OpenSearch Service

• Fully managed by AWS
• VPC integration, IAM authentication
• UltraWarm for cost-effective cold storage
• Multi-AZ deployment
• Serverless option (pay per query, no cluster management)
• OpenSearch Ingestion (managed Logstash alternative)
• Pricing: based on instance type + storage

Elastic Cloud

• Elastic's official managed service (available on AWS, GCP, Azure)
• More polished UI than Amazon OpenSearch
• Auto-scaling
• Built-in APM, synthetics, ML features
• Pricing: based on ECU (Elastic Compute Units) — typically more expensive than OpenSearch Service

Self-hosted

OpenSearch: Completely free, all features included.

Elasticsearch: Free version (formerly Basic) includes core features. Many advanced features (ML, advanced security, cross-cluster replication, ILM fine-grained policies) require paid license.

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Performance

Both are built on the same Lucene core, so raw indexing and query performance are similar for standard workloads. Differences:

• Indexing throughput: Roughly equivalent at scale
• Vector search: OpenSearch with FAISS backend vs Elasticsearch with HNSW — both competitive, benchmark depends on dataset
• Query latency at scale: Elasticsearch has had longer to optimize its distributed query execution layer
• Memory management: Both similar; JVM heap tuning applies to both

For most teams, performance is not the deciding factor.

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Ecosystem and Integrations

Elasticsearch:

• Beats (Filebeat, Metricbeat, Packetbeat) — lightweight data shippers
• Logstash — mature, 200+ plugins
• Elastic Agent — modern unified agent
• Large community, more Stack Overflow answers
• Many commercial tools integrate with Elasticsearch first

OpenSearch:

• Data Prepper — OpenSearch's Logstash alternative (growing)
• Fluent Bit / Fluentd support
• Compatible with many Elasticsearch clients (7.x API compatible)
• AWS native integrations (CloudWatch, Kinesis, S3)

Note: OpenSearch maintains API compatibility with Elasticsearch 7.x — most Elasticsearch clients work with OpenSearch with minimal changes.

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Which One to Choose

Choose OpenSearch if:

• You're on AWS and want native AWS integrations (IAM, VPC, CloudWatch)
• You're building a product and want no licensing concerns
• You need anomaly detection, alerting, or ML features without paying
• You're cost-sensitive (self-hosted OpenSearch vs Elastic's paid tiers)
• Your organization prefers Apache 2.0 open source

Choose Elasticsearch if:

• You need the most advanced search relevance (ELSER, ESRE)
• You're already invested in the Elastic stack (Beats, APM, Kibana)
• You want Elastic's observability platform (APM + logs + metrics in one UI)
• Your team has deep Elasticsearch expertise
• You're using Elastic Cloud and the polish matters to you

For logging/observability on AWS: OpenSearch Service is the pragmatic choice — cheaper, fully featured for ops use cases, and native AWS.

For search-first applications: Elasticsearch has a slight edge on advanced relevance features.

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Further reading: Grafana Loki vs ELK Stack | OpenTelemetry Guide

Affiliate note: Amazon OpenSearch Serverless lets you run OpenSearch without managing clusters — pay per query. Ideal for variable workloads. Elastic Cloud offers a 14-day free trial with full features.