Amazon S3 Vector Search

Overview

Using Amazon S3 as the storage layer for vector databases can achieve 70-95% cost reduction compared to traditional database storage, particularly for large-scale datasets.

Cost Benefits

S3 Storage Pricing:

• Standard: $0.023/GB/month
• Glacier: $0.004/GB/month (archive)
• Intelligent-Tiering: Automatic cost optimization

vs. Traditional Storage:

• 10-20× cheaper than database storage
• No provisioned capacity needed
• Pay only for what you use

Architecture Patterns

Cold Storage:

• Source of truth in S3
• Load to memory/SSD for queries
• Rebuild indexes from S3 as needed

Tiered Storage:

• Hot: Frequently accessed vectors in database
• Warm: Recent vectors in S3
• Cold: Archive vectors in Glacier

Use Cases

• Large datasets (billions of vectors)
• Infrequent query patterns
• Backup and disaster recovery
• Cost-sensitive applications
• Historical data archival

Trade-Offs

Pros:

• Dramatically lower storage costs
• Unlimited scalability
• High durability (99.999999999%)

Cons:

• Higher latency than in-memory/SSD
• Egress costs for data transfer
• Not suitable for real-time queries

Integration

Vector databases supporting S3:

• LanceDB (native S3 support)
• Milvus (S3 as object storage)
• Custom implementations

Best Practices

1. Use for source-of-truth storage
2. Cache frequently accessed vectors
3. Batch operations to minimize API calls
4. Consider S3 Intelligent-Tiering
5. Account for egress costs

Availability

Amazon S3 in all AWS regions