OpenSearch and Elasticsearch share the same codebase ancestry but have diverged since the 2021 fork. For AI workloads - particularly vector search, RAG retrieval, and neural search - the differences matter. Both support vector operations, but their implementations, ML integrations, and managed service options differ.

The governance picture also changed. In September 2024, AWS transferred OpenSearch to the new OpenSearch Software Foundation under the Linux Foundation, making it a vendor neutral project rather than an AWS owned one. In the same month, Elastic added AGPLv3 as a license option for Elasticsearch and Kibana (alongside the existing Elastic License v2 and SSPL), which let Elastic call Elasticsearch open source again. Both projects have since shipped major releases: OpenSearch reached 3.0 in May 2025, and Elasticsearch reached 9.0 in 2025.

Overview

AspectOpenSearchElasticsearch
LicenseApache 2.0AGPLv3, Elastic License v2, or SSPL
GovernanceOpenSearch Software Foundation (Linux Foundation)Elastic (single vendor)
Managed ServiceAmazon OpenSearch ServiceElastic Cloud
Vector Searchk-NN plugin (Faiss, Lucene)Dense vector field (HNSW via Lucene)
ML IntegrationML Commons pluginElasticsearch ML nodes
Neural SearchNeural search pluginELSER (semantic search)
LLM IntegrationOpenSearch AI connectors and MCPElastic AI Assistant

OpenSearch’s k-NN plugin supports two main engines: Faiss and Lucene. Faiss provides the best performance for large-scale vector search with support for IVF, HNSW, and PQ indexing methods. Lucene is a good option for smaller deployments and adds smart filtering, where pre-filtering, post-filtering, or exact k-NN is chosen automatically. The older NMSLIB engine was deprecated in OpenSearch 2.16 and removed in 3.0, where Faiss became the default, so new indices should use Faiss or Lucene. OpenSearch also supports exact k-NN search with script scoring for smaller datasets. OpenSearch 3.0 added experimental GPU accelerated vector indexing (using NVIDIA cuVS) and a derived source feature that reduces vector storage by recreating source documents on demand.

Elasticsearch uses Lucene’s HNSW implementation for approximate nearest neighbor search. In Elasticsearch 9.x, Better Binary Quantization (BBQ) is the default for higher dimensional dense vectors, compressing vectors aggressively while keeping ranking quality close to uncompressed float32. Elastic also added ACORN-1 to speed up filtered vector search and a DiskBBQ format to reduce memory pressure. Elasticsearch’s vector search is well-integrated with its query DSL, supporting hybrid queries that combine vector similarity with traditional keyword filters.

Both support hybrid search (combining vector and BM25 scores), which is important for RAG pipelines where pure vector search can miss keyword-specific matches.

OpenSearch ML Commons provides a framework for hosting ML models directly within the cluster. You can deploy embedding models, cross-encoder rerankers, and sparse encoding models on dedicated ML nodes. The neural search plugin uses these models to automatically convert text queries and documents into vectors at index and query time.

Elasticsearch offers ELSER (Elastic Learned Sparse EncodeR), a sparse encoding model trained specifically for retrieval. ELSER can outperform dense embedding models for certain English-language retrieval tasks. Elasticsearch also supports deploying third-party NLP models through its ML inference pipeline.

RAG Integration

For RAG architectures, both work as the retrieval layer. OpenSearch integrates directly with Amazon Bedrock Knowledge Bases through both OpenSearch Serverless and OpenSearch managed clusters. It is one of several supported vector stores (others include Amazon Aurora PostgreSQL with pgvector, Amazon Neptune Analytics, Pinecone, MongoDB Atlas, and Redis), and it has long been the default quick create option for OpenSearch Serverless backed knowledge bases. The AI connectors framework allows OpenSearch to call external LLM APIs for query rewriting and response generation, and OpenSearch 3.0 added native Model Context Protocol (MCP) support so AI agents can talk to a cluster directly.

Elasticsearch integrates with LangChain, LlamaIndex, and Elastic’s own AI Assistant. The Playground feature in Elastic Cloud lets you build RAG prototypes with a visual interface. ESRE (Elasticsearch Relevance Engine) packages vector search, ELSER, and reranking into a cohesive retrieval pipeline.

Managed Services

Amazon OpenSearch Service provides fully managed clusters on AWS with built-in security, backups, and scaling. OpenSearch Serverless offers a consumption-based model without cluster management. The AWS integration is deep - IAM, VPC, CloudWatch, and direct ingestion from Kinesis and S3.

Elastic Cloud is available on AWS, Azure, and GCP. It provides a fully managed experience with Elastic’s commercial features: security, alerting, machine learning, and the Elastic AI Assistant. Elastic Cloud’s multi-cloud availability is an advantage for organizations not exclusively on AWS.

When to Choose OpenSearch

Choose OpenSearch when you are building on AWS and want native integration with Bedrock and other AWS AI services. OpenSearch is also the right choice when you need the Faiss engine for high-performance vector search at scale, or when Apache 2.0 licensing matters for your compliance requirements.

When to Choose Elasticsearch

Choose Elasticsearch when you need multi-cloud deployment, when ELSER’s sparse encoding provides better retrieval quality for your use case, or when you want Elastic’s commercial features like the AI Assistant and advanced security analytics. Elasticsearch is also preferred when your team already has Elastic expertise.

Practical Recommendation

For AWS-native AI workloads, OpenSearch is the default choice due to Bedrock integration and managed service simplicity. For multi-cloud deployments or teams with existing Elastic investments, Elasticsearch’s broader platform features justify the licensing model. Test retrieval quality with your actual data - the difference between engines often matters less than the quality of your embeddings and chunking strategy.

  • /comparisons/kendra-vs-opensearch-rag/
  • /comparisons/milvus-vs-opensearch/
  • /comparisons/weaviate-vs-pgvector/
  • /comparisons/rag-vs-fine-tuning/

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