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.

OpenSearch: Built-in k-NN (k-nearest neighbors) plugin supports approximate and exact vector search. HNSW and IVF indexing algorithms. Supports filtering alongside vector search.

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: $1.25 per million write request units, $0.25 per million read request units. Storage: $0.25/GB/month. Excellent for variable workloads.

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

OpenSearch Service: Instance-based pricing. A t3.medium.search instance costs $0.07/hour ($50/month). Production deployments typically need 3+ instances for high availability. Minimum monthly cost: ~$150 for a basic HA setup.

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
• Application queries DynamoDB for state and OpenSearch for retrieval

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.