Amazon OpenSearch Service is a managed deployment of OpenSearch (the open-source fork of Elasticsearch). It handles cluster provisioning, patching, scaling, and backups. For AI applications, its primary use cases are vector similarity search (for RAG and semantic search), full-text search over document collections, and log/event analytics.

Official documentation: https://aws.amazon.com/opensearch-service/ Pricing: https://aws.amazon.com/opensearch-service/pricing/ Service quotas: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/limits.html

Azure equivalent: Azure AI Search (formerly Cognitive Search). GCP equivalent: Vertex AI Search.

OpenSearch Serverless vs Managed Clusters

OpenSearch Serverless eliminates cluster management entirely. You create a collection (search or time-series type), and AWS handles scaling, replication, and capacity. Cost is based on OCUs (OpenSearch Compute Units) consumed. For AI RAG applications, Serverless is the default choice: no capacity planning, scales to zero when idle.

Managed clusters give you control over instance types, shard counts, and configuration. Use managed clusters when you need specific performance tuning, very high ingestion rates, or advanced features not yet available in Serverless.

Vector Search for RAG

OpenSearch supports k-nearest-neighbor (k-NN) search using the knn_vector field type. This enables semantic similarity search: given a query vector (embedding), find the N most similar document vectors in the index.

Bedrock Knowledge Bases use OpenSearch Serverless as their default vector store. The integration is managed: Bedrock handles chunking, embedding generation, and index management. You query through the Bedrock API rather than directly through OpenSearch.

For custom RAG implementations, you manage the index directly: embed documents using Bedrock Titan Embeddings or Cohere Embed, index vectors alongside metadata, and query with knn at retrieval time.

OpenSearch supports hybrid search combining vector similarity (semantic relevance) and BM25 full-text scoring. A normalization processor blends the two scores. Hybrid search outperforms either method alone for most information retrieval tasks, especially when users mix keyword and semantic queries.

Full-Text Search Patterns

OpenSearch’s full-text capabilities handle:

  • Multilingual search with language-specific analyzers (stemming, stop words)
  • Fuzzy matching for typo tolerance
  • Autocomplete using edge n-grams
  • Aggregations for faceted navigation (filter by category, date range, etc.)

Analytics and Observability

OpenSearch Dashboards (the open-source Kibana equivalent) provides visualization for log data. Standard use: index Lambda logs, CloudWatch metrics, or application events, then build dashboards showing error rates, latency percentiles, and throughput.

For AI applications, this means you can index LLM traces (from Langfuse or custom logging) into OpenSearch and query patterns across thousands of calls.

Cross-Cloud Comparison

Azure AI Search offers built-in semantic ranking, hybrid search, and a managed indexer pipeline. Vertex AI Search integrates tightly with Google’s foundation models for grounded answers. OpenSearch’s advantage is the open-source ecosystem and its dual role as both a search engine and a log analytics platform within AWS.

  • Amazon Bedrock - Knowledge Bases use OpenSearch Serverless
  • Embeddings - vectors stored and searched in OpenSearch
  • LlamaIndex - framework that integrates with OpenSearch for RAG