OpenSearch vs Elasticsearch for AI Workloads

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.

Overview

Vector Search

OpenSearch’s k-NN plugin supports multiple engines: Faiss, NMSLIB, and Lucene. Faiss provides the best performance for large-scale vector search with support for IVF, HNSW, and PQ indexing methods. You can choose the engine based on your accuracy/speed/memory tradeoffs. OpenSearch also supports exact k-NN search with script scoring for smaller datasets.

Elasticsearch uses Lucene’s HNSW implementation for approximate nearest neighbor search. Recent versions added quantization support for reduced memory usage. 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.

ML and Neural Search

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, making it the default vector store for Bedrock RAG. The AI connectors framework allows OpenSearch to call external LLM APIs for query rewriting and response generation.

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.