AWS Cost Optimization for AI Workloads: Strategies That Work

AI workloads on AWS can quickly become expensive, but with the right strategies, you can reduce costs by 40-70% while maintaining performance and reliability.

Understanding AI Workload Costs

Primary Cost Drivers

• Compute: GPUs and high-memory instances are expensive
• Storage: Large models and datasets require significant storage
• Data Transfer: Moving data between services adds up
• API Calls: Bedrock and other managed services charge per token/request

Typical Cost Breakdown

• 60-70%: Compute (EC2, ECS, SageMaker)
• 15-20%: Storage (S3, EBS, EFS)
• 10-15%: Data Transfer
• 5-10%: Managed Services (Bedrock, etc.)

Instance Selection Strategies

Choose the Right Instance Type

• G4dn: Good balance for most LLM inference (NVIDIA T4 GPUs)
• G5: Better performance (NVIDIA A10G) but higher cost
• Graviton: ARM-based, 20-40% cheaper for CPU workloads
• Inferentia: AWS's custom AI inference chips, very cost-effective

Right-Sizing

• Start with smaller instances and scale up
• Monitor utilization and downsize if underutilized
• Use CloudWatch metrics to identify idle resources

Spot Instances

• Can save 70-90% compared to On-Demand
• Use for fault-tolerant workloads
• Combine with On-Demand for high availability
• Auto-recovery strategies in your application

Architectural Optimizations

Caching Strategies

• Response Caching: Cache frequent queries (ElastiCache)
• Embedding Caching: Cache vector embeddings
• CDN: CloudFront for static content
• Application-Level: In-memory caching for hot data

Batch Processing

• Process multiple requests together
• Use SageMaker Batch Transform for large datasets
• Schedule batch jobs during off-peak hours

Async Processing

• Use SQS for async request handling
• Lambda for event-driven processing
• Reduce need for always-on resources

Bedrock vs Self-Hosted Cost Analysis

When Bedrock Makes Sense

• Variable workloads with unpredictable traffic
• Need for multiple models without commitment
• Small to medium volume (<100M tokens/month)
• Want to avoid infrastructure management

When Self-Hosted Wins

• Consistent, high-volume workloads
• Predictable traffic patterns
• Large volumes (>500M tokens/month)
• Need for custom models or fine-tuning

Cost Comparison Example:

• Bedrock Claude 3 Opus: ~$15 per 1M input tokens
• Self-hosted Llama 3 70B on G5: ~$3-5 per 1M tokens (at 80% utilization)
• Break-even point: ~200M tokens/month

Reserved Instances & Savings Plans

Reserved Instances (RIs)

• 1-year commitment: 30-40% savings
• 3-year commitment: 50-60% savings
• Good for predictable, steady workloads

Savings Plans

• More flexible than RIs
• Applies across instance families
• 1-year: 30-40% savings, 3-year: 50-60% savings

When to Commit

• Predictable usage patterns
• Long-term projects
• Can commit to 1-3 years
• Workloads that need specific instance types

Storage Optimization

S3 Storage Classes

• Standard: Frequently accessed data
• Intelligent-Tiering: Automatic cost optimization
• Glacier: Long-term archival (70-90% cheaper)

EBS Optimization

• Use gp3 instead of gp2 (20% cheaper, better performance)
• Delete unused snapshots
• Use EBS lifecycle policies
• Consider EFS for shared storage

Data Lifecycle Management

• Move old data to cheaper storage classes
• Delete unused data regularly
• Archive completed projects

Monitoring & Cost Management

Cost Allocation Tags

• Tag all resources consistently
• Track costs by project, environment, team
• Use tags for automated resource management

Budgets & Alerts

• Set budgets for projects and services
• Configure alerts at 50%, 80%, 100% thresholds
• Daily cost monitoring for large projects

Cost Explorer

• Analyze spending trends
• Identify cost drivers
• Forecast future spending
• Identify optimization opportunities

Practical Optimization Checklist

Immediate Wins (No Code Changes)

• [ ] Review and terminate unused instances
• [ ] Delete unused snapshots and AMIs
• [ ] Move old data to cheaper storage classes
• [ ] Enable S3 Intelligent-Tiering
• [ ] Review and optimize CloudWatch log retention

Quick Wins (Minimal Code Changes)

• [ ] Implement response caching
• [ ] Use Spot instances for non-critical workloads
• [ ] Optimize instance sizes based on metrics
• [ ] Implement auto-scaling policies
• [ ] Use gp3 instead of gp2 EBS volumes

Strategic Changes (Requires Planning)

• [ ] Migrate to self-hosted models at scale
• [ ] Implement batch processing
• [ ] Use Reserved Instances or Savings Plans
• [ ] Optimize architecture for cost
• [ ] Consider multi-region for cost arbitrage

Real-World Example: 60% Cost Reduction

Initial Setup:

• Bedrock API for all LLM calls: $15K/month
• On-Demand G5 instances: $8K/month
• Storage and transfer: $2K/month
• Total: $25K/month

Optimized Setup:

• Self-hosted models on Spot G5 instances: $3K/month
• Response caching: Reduced API calls by 40%
• Reserved Instances for steady workload: $2K/month
• Optimized storage classes: $1K/month
• Total: $10K/month

Savings: 60% reduction with improved performance

Cost optimization is an ongoing process. Regularly review your usage, monitor costs, and adjust your strategy as your workload evolves.