Quick Answer
DynamoDB vector search is now a native candidate for agent memory, grounding, similarity, recommendation, and RAG when embeddings and operational attributes need to live together with real-time updates. AWS announced general availability on August 5, 2026, including approximate nearest-neighbor search, a configurable vector-index partition key, attribute filtering, and availability in all commercial and AWS GovCloud (US) Regions.
AWS reports single-digit millisecond latency at more than 99% recall and designs the feature for datasets reaching trillions of vectors. Treat those as AWS service claims, not an application guarantee. Your embedding model, dimensions, partition distribution, filters, top-k, update rate, item size, evaluation corpus, Region, and access pattern determine the result.
Architecture Decision
| Use DynamoDB vector search when | Validate another design when |
|---|---|
| Memory attributes and embeddings benefit from one operational store | The retrieval or analytics workload needs capabilities not proven in the current feature |
| Fresh writes must become searchable in a bounded time | Batch-built indexes or separate retrieval systems better fit cost and operations |
| Partition and filter fields can be designed from real access patterns | Hot partitions, high-cardinality filters, or cross-tenant isolation remain unresolved |
| The team wants DynamoDB’s serverless operations | Existing vector infrastructure already meets quality, latency, and governance targets |
Build the Evaluation Before the Index
Create a frozen set of queries, relevant items, hard negatives, tenant and policy filters, and time-sensitive updates. Measure:
- recall or relevance at the exact top-k used by the agent;
- p50, p95, and p99 latency under expected and burst concurrency;
- write-to-search freshness, updates, deletes, and stale-memory handling;
- partition distribution, throttling, retries, filter selectivity, and empty results;
- embedding generation, model version, dimension, normalization, and migration;
- table, vector index, reads, writes, storage, backup, transfer, logs, and model cost;
- tenant isolation, IAM, encryption, sensitive-data removal, retention, and deletion proof.
An agent’s useful answer is the final acceptance surface. A fast nearest-neighbor call can still retrieve unsafe, stale, unauthorized, or irrelevant memory.
Rollout and Rollback
Shadow the new retrieval path beside the current system. Log query and document identifiers according to policy, compare relevance and latency, and avoid exposing shadow results to users. Define a feature flag, index rebuild path, embedding-version migration, backfill validation, and a fallback when vector search or embedding generation is unavailable.
Use the DynamoDB vector search readiness tool to generate the evaluation contract.
Frequently Asked Questions
Is DynamoDB vector search generally available?
Yes. AWS announced general availability on August 5, 2026 and listed all commercial and AWS GovCloud (US) Regions.
Does it support filtered vector search?
AWS says approximate nearest-neighbor searches can filter on attributes to scope results. Test filter correctness, selectivity, latency, and tenant isolation.
Does 99% recall mean my agent will be accurate?
No. AWS’s recall claim concerns vector retrieval under its service conditions. Agent accuracy also depends on embeddings, corpus, relevance labels, filters, prompts, generation, freshness, and review.
Can I store agent memory and embeddings together?
AWS says embeddings can be stored beside other DynamoDB attributes. Design size, partition, access, retention, privacy, and deletion rules before doing so.
Official Sources
Source check: August 15, 2026. Recheck API, quota, dimension, pricing, Region, consistency, and operational documentation.