Itamar Syn-Hershko
Side-by-side comparison of OpenSearch and Elasticsearch - covering performance benchmarks, features, AI and vector search, security, licensing, managed service offering, and migration paths. Updated for 2026.
Elasticsearch has been around since 2010, built on top of the Apache Lucene search library (the same foundation that powers Apache Solr). It quickly became the industry standard for full-text search, log analytics, and real-time BI. In January 2021, following Elastic's license change away from Apache 2.0, Amazon forked Elasticsearch 7.10.2 to create OpenSearch - an open-source alternative under the Apache License 2.0. Since the fork, the two projects have been diverging steadily.
Both Elasticsearch and OpenSearch shipped major version bumps in 2025 - Elasticsearch 9.0 in April, OpenSearch 3.0 in May. These weren't minor increments. Both upgraded to Lucene 10, introduced breaking changes, and continued to diverge in product direction: Elastic is steering more toward an integrated platform for security, AI, observability, and search, while OpenSearch remains more focused on the core engine, analytics, and extensible open source infrastructure. The two projects still share the same DNA from the 2021 fork, so core search and core log analytics behavior remains broadly comparable, but their surrounding solutions and priorities now differ significantly. Both also now offer serverless options - Elasticsearch via Elastic Cloud Serverless, and OpenSearch via Amazon OpenSearch Serverless.
At BigData Boutique, we've worked with both technologies for over 15+ years and now maintain production clusters for clients across search, analytics, and observability workloads. We recently joined the OpenSearch Software Foundation as a General Member. This comparison reflects what we see across thousands of real-world deployments - not vendor marketing.
Project Status, Governance, and Licensing
Elasticsearch is now triple-licensed under AGPLv3, SSPL 1.0, and Elastic License 2.0 (user's choice). Elastic added AGPL in late 2024, which means parts of the source are again available under an OSI-approved license. In practice, though, Elastic's default distributions and managed offerings remain under Elastic License 2.0, and the licensing picture for commercial embedding and managed-service use is still more restrictive than a permissive open source project. For most organizations using Elasticsearch internally as a backend, this changes little. For vendors building commercial products or hosted services on top of it, the licensing still requires careful review.
OpenSearch stays on Apache 2.0 - the most permissive widely-used open source license. In September 2024, AWS transferred governance to the OpenSearch Software Foundation (OSSF) under the Linux Foundation. The foundation has grown since: BigData Boutique, OpenSource Connections, and Resolve Technology joined as General Members in March 2026, alongside 400+ contributing organizations and 3,000+ active contributors. This is no longer just "AWS's project" - it has vendor-neutral governance with a Technical Steering Committee and an open roadmap.
On the version front: Elasticsearch is at 9.3.3 (April 2026), OpenSearch at 3.6 (April 2026). Both are on Lucene 10 and Java 21+.
| Elasticsearch | OpenSearch | |
|---|---|---|
| License | AGPLv3 / SSPL / ELv2 (triple) | Apache 2.0 |
| Governance | Elastic Co | OSSF (Linux Foundation) |
| Latest Version | 9.3.3 (Apr 2026) | 3.6 (Apr 2026) |
| Lucene Version | 10 | 10 |
| Release Cadence | ~Monthly minors | ~Bimonthly minors |
Performance and Scalability
Both projects share the Lucene core, which means raw text search and core log analytics performance is broadly comparable on equivalent hardware and configurations. The differences show up in the optimizations built on top of Lucene, the surrounding platform features, and in specialized workloads. In practice, either engine can win on specific operations depending on the version, data model, and query mix; for vector database-style workloads, OpenSearch's Faiss support gives it a meaningful flexibility advantage.
Elasticsearch 9.x brought ES|QL to full production readiness. LogsDB index mode reached GA with up to 65% storage reduction for log data through synthetic source and doc-value-only fields. Elastic has also been pushing further into log processing and AI-assisted workflows through Streams, though some of the newer automation capabilities were still rolling out in preview form across the 9.x line.
OpenSearch 3.0 claims a 9.5x performance improvement over OpenSearch 1.3, with range queries 25% faster on the Big5 benchmark suite. Concurrent segment search is now enabled by default. OpenSearch 3.0 also introduced experimental gRPC support and experimental pull-based ingestion from Apache Kafka and Amazon Kinesis, pointing to a broader push on ingestion and transport efficiency.
A word on benchmarks: vendor-published numbers are marketing. Elastic's own benchmarks show 40-140% better performance than OpenSearch on log analytics workloads. An independent Trail of Bits benchmark from March 2025 found OpenSearch faster on Big5 mixed workloads. The truth depends on your workload, hardware, and configuration. For text search, the difference is usually marginal. For specialized workloads - vector search, time-series, log analytics - the engine-specific optimizations matter more than the shared Lucene foundation.
Query Languages
Both platforms now have dedicated query languages competing for the same space. Elasticsearch has ES|QL, a pipe-based language tightly integrated into Kibana with autocomplete and visualization support. OpenSearch has PPL (Piped Processing Language), which received substantial updates in OpenSearch 3.3 with new commands and functions for log analytics and observability workflows. Both also support SQL. The choice between them is largely an ecosystem decision - if you're on Kibana, ES|QL is the natural fit; if you're on OpenSearch Dashboards, PPL is.
Core Feature Differences
While the core search and analytics functionality remains broadly the same, some features have diverged in naming, availability, or direction:
- Index Lifecycle Management vs Index State Management: Elasticsearch calls it ILM, OpenSearch calls it ISM. Both handle time-based index rollover, retention, and deletion, but the APIs and policy syntax differ.
- Rollups: Elasticsearch deprecated and removed its rollup feature in favor of downsampling in the TSDB index mode. OpenSearch still supports index rollups as part of ISM.
- Cross-cluster replication: Both support CCR, but in Elasticsearch it requires a Platinum/Enterprise subscription. In OpenSearch, cross-cluster replication is a free, built-in feature.
- Cross-cluster search: Available in both, free in OpenSearch, requires a paid license in Elasticsearch for some configurations.
Vector Search and AI Capabilities
This is where the two projects diverge the most, and where the competition has been fiercest in 2025-2026.
Vector Engines and Dimensions
Both platforms support dense vector fields for storing embeddings used in semantic search, RAG, and AI workflows.
Elasticsearch uses Lucene's native HNSW as its sole vector engine. It compensates with aggressive quantization innovations: BBQ (Better Binary Quantization) became the default for vectors with >=384 dimensions in ES 9.1, reducing memory by 95%+ compared to float32. DiskBBQ (GA in 9.2) makes disk-backed vector search a practical option for cost-sensitive deployments with very large indexes. NVIDIA cuVS GPU acceleration is in tech preview in 9.3, delivering up to 12x faster indexing. A significant limitation is the max vector dimensions: 4,096 - which may become a constraint as newer embedding models trend toward higher dimensionality.
OpenSearch supports two vector engines: Lucene HNSW and Faiss (Facebook AI Similarity Search). Note that nmslib was deprecated in OpenSearch 2.16 and removed for new index creation in 3.0. Faiss provides IVF (Inverted File Index) and product quantization (PQ) as alternatives to HNSW, which matter for workloads where memory is constrained or where approximate search with different recall/latency tradeoffs is needed. OpenSearch supports multiple quantization methods: byte vectors, FP16, product quantization, and binary quantization (since 2.17, via Faiss with 32x compression). OpenSearch also has its own disk-based vector search mode - it uses a two-phase approach where a compressed binary-quantized index lives in memory and full-precision vectors are rescored from disk, cutting costs by roughly 67%. It doesn't have Elasticsearch's BBQ yet, though there's an active RFC to integrate Lucene's BBQ. Concurrent segment search for k-NN is enabled by default in 3.0, delivering up to 2.5x faster vector queries. Max vector dimensions: 16,000 via Faiss - nearly 4x Elasticsearch's 4,096 limit. This is a meaningful differentiator as newer embedding models increasingly use higher dimensionality, and Elasticsearch's Lucene-only approach is constrained by Lucene's own dimension limits.
| Elasticsearch 9.x | OpenSearch 3.x | |
|---|---|---|
| Vector Engines | Lucene HNSW | Lucene HNSW, Faiss |
| Max Dimensions | 4,096 | 16,000 |
| Default Quantization | BBQ (9.1+) | Configurable (byte, FP16, PQ, binary) |
| Disk-based Search | DiskBBQ (GA 9.2) | Faiss with disk mode |
| GPU Acceleration | cuVS (tech preview 9.3) | Via Amazon OpenSearch Service |
| ANN Algorithms | HNSW | HNSW, IVF |
RAG Workflows and AI Agents
Elasticsearch's ESRE (Elasticsearch Relevance Engine) and Retriever framework together provide a comprehensive RAG pipeline that can encapsulate the full complexity of retrieval-augmented generation - from generating embeddings at index and query time to executing multi-stage retrieval pipelines in a single _search call, combining knn, RRF, text similarity reranking, diversification, and rule-based pinning. The Jina AI acquisition (October 2025) brought three multilingual embedding models directly into the Elastic Inference Service. The Elastic Agent Builder (GA January 2026) lets developers build AI agents over Elasticsearch data with natural language, and supports MCP server import/export for integration with Claude Desktop, Cursor, and LangChain.
OpenSearch took a different path, building on its Neural Search query type which allows running ML models (local or remote) during query and index time to generate embeddings and power semantic search natively. The agentic search capability, introduced in the 3.x line, uses the Flow Framework plugin to orchestrate AI-driven search workflows, achieving 82% query translation accuracy and up to 235% relevance improvements in evaluation benchmarks. OpenSearch 3.4 added a no-code UX for building agents with MCP integration. The Launchpad (April 2026) is an AI-powered tool that generates a running search application from plain-language requirements in minutes - a real time-saver for teams new to search.
Perhaps the most distinctive OpenSearch AI feature is Agent Health - open-source observability and evaluation specifically for AI agents. It provides trace-level visibility into agent execution, automated benchmarking, and LLM-as-judge evaluation. For teams running AI agents in production, this addresses a real gap: agents fail silently, and without dedicated monitoring you won't know until users complain.
Solutions, Ecosystem, and Security
Observability
Elastic's observability stack remains the more mature offering. Full OpenTelemetry support with a managed OTLP endpoint (available from October 20, 2025) means you can ship traces directly from OTel SDKs without running a local collector. Combined with APM, log correlation, and ES|QL, Elastic offers a complete observability platform that competes with Datadog and Splunk.
OpenSearch has been closing the gap. The OpenTelemetry integration in OpenSearch 3.1 provides service maps and trace analytics for distributed microservices (see our practical guide to OpenTelemetry with OpenSearch for a hands-on walkthrough). PPL's expanded observability commands in 3.3 streamline log analytics workflows. OpenSearch Dashboards also continued to unify log analytics, distributed tracing, and visualizations in the 3.x line.
SIEM and Security Features
Elastic Security is a full-blown SIEM with detection rules, SOAR automation, and threat intelligence integrations. Elastic was also recognized in analyst coverage for security analytics in 2025. For organizations that need an enterprise-grade SIEM, this is a strong differentiator.
OpenSearch doesn't offer a directly comparable built-in SIEM, though it does include a Security Analytics module with basic detection functionality. For fuller SIEM/XDR use cases, Wazuh - an open source XDR and SIEM built on OpenSearch - fills this gap for many deployments.
Where OpenSearch wins decisively is on built-in security features. LDAP, Active Directory, SAML, OpenID authentication, role-based access control, field-level security, document-level security, audit logging - all of this is free and open source in OpenSearch. Elasticsearch includes core security and RBAC in its free Basic tier, but features like LDAP/AD, SAML/OIDC SSO, document- and field-level security, and audit logging require paid subscriptions. For organizations that need those enterprise controls without additional licensing costs, OpenSearch has a clear advantage.
Client Libraries
OpenSearch's client library situation has come a long way since the early post-fork days. Actively maintained clients now exist for Python, Java, JavaScript, Go, Ruby, PHP, .NET, and Rust. The Python and Java clients are the most mature.
Data Ingestion
When the fork happened, Elasticsearch enforced version checks in Logstash, Beats, and its client libraries, blocking connections to OpenSearch clusters. This created a significant divergence in the data ingestion ecosystem.
Logstash can send data to OpenSearch via the logstash-output-opensearch plugin, maintained by the OpenSearch project. However, OpenSearch recommends staying on Logstash 7.16.x or earlier for guaranteed compatibility - newer Logstash versions may work but aren't actively tested against OpenSearch.
Beats (Filebeat, Metricbeat, etc.) have more limited OpenSearch support. Beats 7.12.x and earlier work natively with OpenSearch, but newer versions include validation that rejects non-Elasticsearch clusters. The common workaround is to route Beats through Logstash with the OpenSearch output plugin.
For new OpenSearch deployments, Data Prepper is the recommended ingestion solution. It's purpose-built for OpenSearch with native OpenTelemetry support, and offers better throughput and lower latency than Logstash for OpenSearch workloads. Dedicated connectors also exist for Kafka (via Kafka Connect) and Flink.
None of this is an issue for Elasticsearch - Logstash, Beats, and Elastic Agent all work seamlessly as part of the Elastic Stack.
Monitoring
Monitoring your cluster is crucial for maintaining its health, performance, and stability. Both Elasticsearch (via Kibana Stack Monitoring) and OpenSearch (via OpenSearch Dashboards) offer built-in tools for cluster monitoring. The managed solutions add their own layers - Elastic Cloud provides cluster monitoring on its control plane, and Amazon OpenSearch Service offers CloudWatch metrics.
However, built-in monitoring only tells you what's happening - not what you should do about it. For actionable insights, Pulse provides automated monitoring and recommendations for both Elasticsearch and OpenSearch clusters. Beyond dashboards and metrics, Pulse acts as an automated consultant - identifying issues, explaining their impact, and providing specific, tailored recommendations to keep your clusters healthy and performant.
Support
OpenSearch is a community-driven open source project, which means there is no official vendor support from the project itself. Managed services like Amazon OpenSearch Service handle infrastructure, but not how you use the technology. For organizations that need expert support, BigData Boutique is the first accredited OpenSearch LTS support provider, offering 24/7 production support, consulting, and hands-on development services.
Elastic offers support through its subscription licenses and Elastic Cloud. For organizations running self-managed Elasticsearch who want an alternative to Elastic's own support, BigData Boutique also provides independent Elasticsearch support - often more tailored and hands-on than what's available through standard vendor subscriptions.
Pricing and Cost Efficiency
Both technologies are free to run self-managed. The cost picture changes dramatically once you factor in managed services and licensed features.
Managed service options: Elasticsearch is available only on Elastic Cloud (deployable on AWS, GCP, Azure) and as an Azure native integration. OpenSearch has multiple competing providers: Amazon OpenSearch Service, Aiven, Instaclustr (NetApp), and others. More competition means lower prices.
Searchable snapshots remain an important cost differentiator for cold data. This feature - serving queries from object storage instead of keeping all historical data on SSD-backed nodes - is highly relevant for log analytics and observability workloads where most data is cold. In OpenSearch, searchable snapshots are free. In Elasticsearch, they require an Enterprise subscription.
Amazon OpenSearch Service continues to add cost-optimization features: GPU acceleration for bulk indexing and vectorization workflows, and Zstandard (zstd) compression for up to 32% index size reduction. Elastic Cloud updated its serverless pricing model in late 2025, moving to VCU-based compute pricing.
On a like-for-like basis, Amazon OpenSearch Service is typically 30-50% cheaper than Elastic Cloud for equivalent workloads. Factor in the licensing cost for features that are free in OpenSearch (security, searchable snapshots, cross-cluster replication) and the gap widens further.
Summary
| Feature | Elasticsearch 9.x | OpenSearch 3.x |
|---|---|---|
| License | AGPLv3 / SSPL / ELv2 | Apache 2.0 |
| Governance | Elastic Co | OSSF (Linux Foundation) |
| Vector Engines | Lucene | Lucene, Faiss |
| Max Vector Dimensions | 4,096 | 16,000 |
| Vector Quantization | BBQ (default), DiskBBQ, int8, bfloat16 | Byte, FP16, PQ, binary (32x) |
| Disk-based Vector Search | DiskBBQ (GA 9.2) | Faiss on_disk mode (2-phase rescore) |
| RAG Framework | ESRE + Retrievers (GA) | Neural Search + Flow Framework + Agentic Search (GA) |
| AI Agent Builder | Elastic Agent Builder + MCP | No-code agent builder + MCP |
| AI Agent Monitoring | Via APM | Agent Health (dedicated) |
| Query Language | ES|QL | PPL + SQL |
| Security (LDAP, SAML) | Paid subscription | Free, built-in |
| Searchable Snapshots | Paid (Enterprise) | Free |
| APM / SIEM | Full-featured, mature | Basic (Wazuh for SIEM) |
| Data Ingestion | Logstash, Beats, Elastic Agent | Data Prepper, Logstash (via plugin), Kafka Connect |
| Managed Options | Elastic Cloud | Amazon OpenSearch, Aiven, Instaclustr, others |
| Support | Elastic subscriptions, Elastic Cloud | Community + accredited providers (BigData Boutique) |
| Onboarding | Kibana guided setup | Launchpad (AI-powered) |
When to choose Elasticsearch:
- You need the full Elastic platform - APM, SIEM, Enterprise Search as integrated solutions
- Your team already invests in the Elastic ecosystem and Kibana
- You need the most polished, integrated RAG developer experience
- Licensing terms are acceptable for your use case
When to choose OpenSearch:
- Cost efficiency is a priority, especially for observability and log analytics workloads
- You need permissive licensing (Apache 2.0) for embedding in commercial products or offering as a service
- Vector search at scale is a primary use case - Faiss support, higher dimensions, and more quantization options give you flexibility
- You want enterprise security features (LDAP, SAML, field-level security) without additional licensing costs
- You prefer vendor-neutral governance and a broader choice of managed service providers
When it genuinely doesn't matter: For standard text search, core log analytics workflows, dashboards, and basic alerting - the core functionality is very similar. Pick based on your ecosystem, team expertise, and total cost of ownership.
Need help deciding, or planning a migration between the two? Our team has guided hundreds of companies through this decision. Reach out to us to discuss, or check out our OpenSearch consulting and Elasticsearch support services.
