AWS S3 Vectors Pricing Deep Dive: Real Cost Analysis vs Pinecone

The Hidden Truth About Vector Database Costs

AWS S3 Vectors promises up to 90% cost savings compared to traditional vector databases—but is this claim accurate? We've conducted a detailed cost analysis comparing S3 Vectors to Pinecone across different usage patterns, and the results reveal a nuanced pricing story that depends heavily on your query volume.

AWS S3 Vectors Pricing Structure Explained

S3 Vectors Pricing Components (US East - N. Virginia)

S3 Vectors uses a multi-component pricing model that scales with usage:

1. Storage Costs

• Rate: $0.06 per GB per month
• What's included: Vector data, metadata, and keys
• Calculation: Dimensions × 4 bytes × number of vectors

2. PUT Operation Costs

• Rate: $0.20 per GB uploaded
• Optimization: Batch multiple vectors per PUT request

3. Query Costs (Two-Part Pricing)

• API calls: $0.0025 per 1,000 requests
• Data processing: Tiered pricing based on index size
  • First 100K vectors: $0.004 per TB
  • Over 100K vectors: $0.002 per TB

Understanding S3 Vectors Query Cost Scaling

The critical insight: S3 Vectors query costs scale with data processed, not just API calls. Every query processes the entire index size multiplied by average vector size, making large indexes expensive at high query volumes.

S3 Vectors vs Pinecone: Detailed Cost Comparison

Pricing Comparison for 1M Vectors (1024 dimensions)

Let's analyze costs for a typical production workload with 1 million vectors:

Storage Size Calculation

1M vectors × 1024 dimensions × 4 bytes = 4.1 GB vector data
+ 0.5 KB metadata per vector = 0.5 GB metadata
+ 0.1 KB keys = 0.1 GB keys
Total: ~4.7 GB storage

Monthly Cost Breakdown by Query Volume

Low Volume (1,000 queries/month)

Medium Volume (100,000 queries/month)

High Volume (1,000,000 queries/month)

*Pinecone charges shown assume ~4.7 RUs/query on Standard plan ($16/M RUs). Actual RU usage varies by namespace size and query parameters.

Query Cost Calculation Details

S3 Vectors query costs = API calls + Data processed

• API calls: $2.50 per million requests
• Data processed: Average vector size × vectors in index × queries × tiered rate
• For 1M vectors with 4.6KB avg size: ~$11.92 per 1M queries

Pinecone's $50 minimum applies until usage exceeds it (~650K queries/month at 4.7 RUs/query)

S3 Vectors Cost Calculator: Real-World Examples

Example 1: Small RAG Application

Scenario: 250K vectors, 10K queries/month

S3 Vectors Cost:
- Storage: 1.2 GB × $0.06 = $0.07
- Queries: ~$0.03
- Total: $0.10/month

Pinecone Cost: $50/month (minimum)
Savings with S3 Vectors: 99.8%

Example 2: Medium Enterprise Deployment

Scenario: 10M vectors across 40 indexes, 1M queries/month

S3 Vectors Cost:
- Storage: 59 GB × $0.06 = $3.54
- PUT operations: $1.97
- Queries: $5.87
- Total: $11.38/month

Pinecone Pod Cost: ~$51-140/month
Savings with S3 Vectors: 77-92%

Example 3: Large-Scale Production System

Scenario: 400M vectors, 10M queries/month

S3 Vectors Cost:
- Storage: 2,354 GB × $0.06 = $141.22
- PUT operations: $78.46
- Queries: $997.62
- Total: $1,217.29/month

Pinecone Enterprise: ~$2,000-5,000/month
Savings with S3 Vectors: 39-76%

When S3 Vectors Saves Money (And When It Doesn't)

S3 Vectors Cost Advantages

✅ Low Query Volumes (<10K queries/month)

• Up to 130x cheaper than alternatives
• Perfect for development and testing
• Ideal for archival storage

✅ Small to Medium Datasets (<1M vectors)

• Storage costs 5-10x lower than competitors
• Excellent for proof-of-concepts
• Cost-effective for startups

✅ Infrequent Access Patterns

• Cold data archives
• Compliance storage
• Backup embeddings

When S3 Vectors Becomes Expensive

❌ Very Large Indexes (>7.5M vectors per index)

• At ~7.5M vectors, S3 query costs can exceed Pinecone's per-query rate
• Per-TB query pricing scales with index size
• Consider partitioning or alternative solutions

❌ High Query Volumes on Large Indexes

• Query data processing costs escalate
• Each query processes entire index
• Traditional vector DBs more efficient

❌ Frequent Updates

• $0.20/GB PUT costs accumulate
• Updates require full vector rewrite (upsert)
• Consider alternatives for dynamic data

S3 Vectors Hidden Costs and Limitations

Costs Not Immediately Obvious

1. Data Transfer: Standard S3 transfer rates apply
2. Oversized Metadata: Wasted storage if metadata < 2KB limit
3. Failed Queries: Still charged for data processing
4. Index Rebuilds: Full PUT costs for updates

Cost-Related Limitations

• 50M vectors per index: May require multiple indexes (more complexity)
• Max 10,000 vector indexes: Hard limit for multi-tenant applications
• Updates via upsert: PutVectors with same key overwrites entire vector

S3 Vectors Pricing Optimization Strategies

Reduce Storage Costs

1. Minimize metadata: Store only essential filterable fields
2. Optimize dimensions: Use smallest embedding size that maintains quality
3. Compress keys: Use short, efficient identifiers

Minimize Query Costs

1. Partition into smaller indexes: Data processed = index size × avg vector size (filtering doesn't reduce this)
2. Batch operations: Combine similar queries
3. Cache frequently accessed data: Implement application-level caching
4. Monitor query patterns: Identify and optimize expensive queries

PUT Operation Optimization

1. Batch uploads: Maximize vectors per PUT request
2. Scheduled updates: Consolidate changes
3. Incremental updates: Only update changed vectors

S3 Vectors Total Cost of Ownership (TCO) Analysis

Comparing 3-Year TCO for 10M Vectors, 500K queries/month

S3 Vectors TCO

Year 1: $636 ($53/month)
Year 2: $636
Year 3: $636
Total: $1,908

Pinecone Serverless TCO

Year 1: $1,800 ($150/month average)
Year 2: $1,800
Year 3: $1,800
Total: $5,400

Dedicated Infrastructure TCO

Year 1: $15,000 (hardware + setup)
Year 2: $5,000 (maintenance)
Year 3: $5,000 (maintenance)
Total: $25,000

S3 Vectors provides 65% lower TCO than Pinecone and 92% lower than self-managed infrastructure for this use case.

S3 Vectors Cost Comparison Summary

Key Pricing Insights

1. S3 Vectors is not always cheaper: Cost advantage depends on index size and query volume
2. Index size matters: At ~7.5M vectors per index, S3 query costs can exceed Pinecone's per-query rate
3. Storage is incredibly cheap: $0.06/GB is 5-10x lower than competitors
4. Query costs scale with index size: Data processed = avg vector size × vectors in index

Best Value Scenarios for S3 Vectors

When to Choose Alternatives

Conclusion: S3 Vectors Pricing Strategy

AWS S3 Vectors delivers on its promise of cost savings—but only for specific use cases. The pricing model heavily favors:

• Low to medium query volumes (<100K/month)
• Cold and warm data tiers
• Read-heavy workloads
• Cost-sensitive applications

For high-query-volume production systems, the per-TB query pricing can make S3 Vectors more expensive than purpose-built alternatives. The key is understanding your query patterns and calculating the crossover point for your specific workload.

The Bottom Line: S3 Vectors can reduce vector database costs by 70-95% for appropriate use cases, but careful analysis of query volume and growth projections is essential to ensure it remains economical as your application scales.

Code and Resources

The complete S3 Vectors implementation with cost optimization examples is available at: github.com/ColeMurray/aws-rag-s3-vectors