Amazon S3 Vectors is now available in AWS GovCloud (US) Regions
Federal and regulated workloads can now run serverless, billion-vector AI search within GovCloud compliance boundaries at up to 90% lower cost.
View original announcement →What's New
Amazon S3 Vectors, AWS's purpose-built vector storage service for AI workloads, is now available in the AWS GovCloud (US-East) and AWS GovCloud (US-West) regions. The service enables federal agencies and regulated-industry customers to store, access, and query vectors at billion-vector scale without provisioning any infrastructure. This expansion brings the same cost-optimized, serverless vector storage capabilities—previously available in commercial regions—to workloads requiring US government compliance boundaries.
How It Works
- Vector Buckets: A new S3 bucket type purpose-built for vector data, distinct from standard S3 object buckets, providing a dedicated storage layer for embeddings.
- Vector Indexes: Within a vector bucket, data is organized into vector indexes (up to 10,000 per bucket), each capable of holding up to 2 billion vectors, enabling logical partitioning of embedding datasets.
- Dedicated API: A separate set of API operations (endpoint format:
s3vectors.<region>.api.aws) allows you to PUT, GET, and query vectors using approximate nearest-neighbor (ANN) similarity search without managing any underlying infrastructure. - Metadata Filtering: Vectors can be stored with attached metadata (e.g., timestamps, categories), enabling filtered similarity queries that combine semantic search with structured conditions.
- Sub-second Query Performance: Delivers latency as low as 100 ms for frequent queries and sub-second performance for infrequent queries, with automatic internal optimization as datasets grow.
- Strong Consistency: Writes to S3 Vectors are strongly consistent, meaning newly written or updated vectors are immediately available for query.
- Access Control: Leverages existing Amazon S3 access control mechanisms—IAM policies and bucket policies—so no new security model needs to be learned.
- Private Connectivity: Supports AWS PrivateLink interface endpoints for VPC-isolated access, critical for GovCloud compliance architectures.
- Native Integrations: Natively integrated with Amazon Bedrock Knowledge Bases (including SageMaker Unified Studio) and Amazon OpenSearch Service for tiered vector storage strategies.
Why It's Important
- GovCloud Compliance Unlocked: Federal agencies, defense contractors, and regulated workloads (FedRAMP High, ITAR, EAR) can now build AI-powered RAG pipelines and semantic search applications within compliant AWS GovCloud boundaries.
- Eliminates Infrastructure Overhead: Government customers no longer need to provision, scale, or manage dedicated vector database clusters (e.g., OpenSearch, pgvector on RDS), reducing operational burden significantly.
- Dramatic Cost Reduction: AWS claims up to 90% lower costs for storing, uploading, and querying vectors compared to traditional vector database solutions, making large-scale AI workloads economically viable for agencies with constrained budgets.
- Enables Sovereign AI Agents: AI agents and RAG applications processing sensitive government data (classified documents, PII, health records) can now maintain vector memory stores without data leaving GovCloud boundaries.
- Scales to Mission-Scale Datasets: Support for up to 20 trillion vectors across a bucket (10,000 indexes × 2B vectors each) accommodates even the largest government document corpora or intelligence datasets.
How It's Different
- First Cloud Object Store with Native Vector Support: Unlike traditional object stores that require a separate vector database layer, S3 Vectors embeds vector indexing and ANN search directly into the storage service itself.
- Serverless vs. Provisioned Vector DBs: Competing solutions like Amazon OpenSearch Service (with k-NN), Pinecone, or Weaviate require cluster provisioning and capacity planning; S3 Vectors is fully serverless with zero infrastructure management.
- Cost Structure: Charges only for storage and queries actually used (no idle cluster costs), contrasting with provisioned vector databases that incur costs regardless of utilization.
- S3 Durability Guarantees: Inherits S3's 11 nines (99.999999999%) durability and high availability SLAs, which dedicated vector databases typically do not match at equivalent cost.
- Tiered Strategy with OpenSearch: Rather than replacing OpenSearch, S3 Vectors enables a cost-tiered architecture—store the bulk of vectors cheaply in S3 Vectors and promote hot vectors to OpenSearch for highest-performance access.
- Integrated Access Control: Uses S3's mature IAM and bucket policy model rather than introducing a new permissions system, simplifying governance in regulated environments.
When to Prefer It
- RAG Applications in GovCloud: When building Retrieval Augmented Generation pipelines for government LLM deployments (e.g., Amazon Bedrock in GovCloud) that must keep embedding data within FedRAMP or ITAR boundaries.
- Large, Infrequently Queried Vector Datasets: When you have billions of vectors that are queried at moderate frequency and sub-second latency (100 ms+) is acceptable, making the cost savings over always-on clusters compelling.
- Cost-Sensitive AI Workloads: When budget constraints make provisioned vector database clusters impractical, and you need to store millions-to-billions of embeddings economically.
- Long-Term Vector Archival: When maintaining historical vector indexes (e.g., past document embeddings, archived media) that need to remain queryable but don't justify dedicated infrastructure.
- Semantic Search over S3 Data: When your primary data already lives in Amazon S3 and you want to add semantic search without introducing a separate data platform.
- AI Agent Memory at Scale: When building stateful AI agents that require persistent, large-scale vector memory stores accessible across sessions without managing a database.
- Tiered Vector Architecture: When you want to combine S3 Vectors (cost-optimized bulk storage) with Amazon OpenSearch Service (high-performance hot tier) for a hybrid vector search strategy.
Availability
- Status: Generally Available (GA) in AWS GovCloud regions as of July 7, 2026; the service has been GA in commercial regions prior to this announcement.
- GovCloud Regions: Available in AWS GovCloud (US-East) [
us-gov-east-1] and AWS GovCloud (US-West) [us-gov-west-1], with endpointss3vectors.us-gov-east-1.api.awsands3vectors.us-gov-west-1.api.aws. - Commercial Region Coverage: Also available across a broad set of commercial regions including US East/West, all major European regions, and extensive Asia Pacific coverage (see AWS Regions and endpoints documentation for the full list).
- Pricing Model: Pay-per-use based on vectors stored, write requests, and query requests; no minimum charge and no idle infrastructure costs. Pricing details available on the Amazon S3 pricing page under the "Vectors" tab.
- Service Quotas: Up to 10,000 vector indexes per bucket, up to 2 billion vectors per index, and up to 20 trillion vectors per bucket; quota increases may be requestable via AWS Support.
- Connectivity: Supports dual-stack (IPv4/IPv6) endpoints and AWS PrivateLink interface endpoints for private VPC connectivity—essential for GovCloud network isolation requirements.
- Access Protocol: HTTPS only, Signature Version 4 authentication.
