DynamoDB vs OpenSearch for AI Applications

DynamoDB and OpenSearch serve different roles in AI applications, but their capabilities overlap in areas like vector search and metadata storage. Understanding where each excels prevents architectural mistakes.

Core Strengths

DynamoDB is a fully managed NoSQL key-value and document database. Designed for single-digit millisecond latency at any scale. Excels at simple key-based lookups and writes with predictable performance.

OpenSearch is a managed search and analytics engine. Designed for full-text search, log analytics, and vector search. Excels at complex queries across many fields with relevance scoring.

AI Application Use Cases

Conversation History Storage

DynamoDB: Natural fit. Store conversations by session_id (partition key) and message_timestamp (sort key). Single-digit millisecond reads. Scales automatically. TTL for automatic expiration of old conversations.

OpenSearch: Overpowered for this use case. Adds unnecessary complexity and cost for simple key-based retrieval.

Winner: DynamoDB

Vector Search (RAG)

DynamoDB: No native vector search support. Cannot perform similarity search on embeddings. To add vector search, AWS provides a zero-ETL integration that replicates DynamoDB items into OpenSearch Service through Amazon OpenSearch Ingestion, optionally generating embeddings with Amazon Bedrock during ingestion. DynamoDB stays the system of record while OpenSearch handles the search.

OpenSearch: Built-in k-NN (k-nearest neighbors) plugin supports approximate and exact vector search. HNSW and IVF indexing algorithms (Lucene and Faiss engines). Supports filtering alongside vector search. Recent OpenSearch Service releases add GPU-accelerated and auto-optimized vector indexes, plus a tiered approach that keeps “cold” vectors in Amazon S3 Vectors for cost savings while serving “hot” vectors from the domain.

Winner: OpenSearch

Metadata Search and Filtering

DynamoDB: Limited query flexibility. Efficient for queries on partition key and sort key. Global secondary indexes support additional query patterns but with limitations. Complex filters are possible but expensive (scan operations).

OpenSearch: Excels at complex queries. Full-text search, range queries, aggregations, nested object queries, and faceted search are all native capabilities.

Winner: OpenSearch for complex queries; DynamoDB for simple key-based access

User Profile and Session State

DynamoDB: Excellent fit. Key-value access pattern matches perfectly. Consistent low latency. On-demand capacity handles variable traffic.

OpenSearch: Unnecessary complexity for simple profile lookups.

Winner: DynamoDB

Analytics and Aggregation

DynamoDB: Not designed for analytics. Aggregation queries require scanning the entire table or using DynamoDB Streams to feed data to an analytics system.

OpenSearch: Built for analytics. Aggregation framework supports complex analytics queries: histograms, percentiles, term aggregations, nested aggregations.

Winner: OpenSearch

Comparison Table

Cost Comparison

DynamoDB on-demand: Roughly $1.25 per million write request units and $0.25 per million strongly consistent read request units (eventually consistent reads are about half that), plus storage at $0.25/GB/month, in US East. AWS cut on-demand throughput pricing by 50 percent in late 2024 and now recommends on-demand as the default mode. Excellent for variable workloads. Check the AWS pricing page for current rates by region and table class.

DynamoDB provisioned: Cheaper per-request for steady, predictable workloads. Reserved capacity is available for additional savings.

OpenSearch Service (provisioned domains): Instance-based pricing per instance-hour. A small instance starts low, but production deployments typically need at least 3 nodes (or a multi-AZ setup) for high availability, so the realistic floor is well above a single instance. Reserved Instances cut the cost of steady 24/7 clusters.

OpenSearch Serverless: Capacity is billed in OpenSearch Compute Units (OCUs) at roughly $0.24 per OCU-hour, with a minimum of 2 OCUs for production redundancy. Convenient for spiky or low-touch workloads, but for steady 24/7 traffic a provisioned domain with Reserved Instances is usually cheaper.

For AI applications, the cost comparison depends on the access pattern:

• If you need vector search, you need OpenSearch (or another vector database). DynamoDB cannot substitute.
• If you need only key-value access (session state, user profiles), DynamoDB is simpler and often cheaper.
• If you need both, use both. This is the common pattern in AI applications.

Common AI Architecture Patterns

Pattern 1: DynamoDB + OpenSearch

The most common pattern for AI applications:

• DynamoDB stores conversation history, user sessions, and application state
• OpenSearch stores document embeddings and handles vector search for RAG (see /comparisons/milvus-vs-opensearch/ if you are weighing OpenSearch against a dedicated vector database)
• Application queries DynamoDB for state and OpenSearch for retrieval
• The DynamoDB zero-ETL integration with OpenSearch Service can keep the search index in sync automatically, so you do not hand-roll a replication pipeline

Pattern 2: OpenSearch as Primary

For search-heavy applications:

• OpenSearch stores everything: documents, embeddings, metadata
• Used when the primary access pattern is search and retrieval
• Simpler architecture but higher operational complexity

Pattern 3: DynamoDB as Primary

For simple AI applications:

• DynamoDB stores everything except vector search data
• LLM inference handled by Bedrock (no local vector search needed)
• Works when RAG is not required or when Bedrock Knowledge Bases handles retrieval

Recommendation

Use DynamoDB for application state, session management, and simple key-value access patterns. Use OpenSearch for vector search, full-text search, complex queries, and analytics. Most production AI applications benefit from using both, each for its strength. For a related decision specific to retrieval, see /comparisons/kendra-vs-opensearch-rag/.

Sources

• Vector search for Amazon DynamoDB with zero-ETL for Amazon OpenSearch Service (AWS Database Blog)
• k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service (AWS docs)
• Amazon OpenSearch Service improves vector database performance and cost with GPU acceleration and auto-optimization (AWS News Blog)
• Amazon S3 Vectors is now generally available (AWS What’s New)
• Service quotas and constraints in Amazon DynamoDB (AWS docs)
• Amazon DynamoDB pricing