Understanding AI Cost Drivers — AI Cost Optimization

Understanding what drives AI costs is essential for building a cost optimization strategy.

Cost Components

LLM API Costs

Input tokens: Cost per 1K tokens of prompt/context
Output tokens: Usually 2-4x more expensive than input
Requests: Some providers charge per API call
Compute type: GPU type and usage duration

Infrastructure Costs

Resource	Cost Driver	Optimization Lever
GPU compute	Utilization %, hours	Right-sizing, spot instances
GPU memory	Model size, batch size	Quantization, model sharing
Network	Data transfer volume	Caching, regional deployment
Storage	Dataset and model size	Compression, lifecycle policies
Monitoring	Log volume, metrics	Sampling, retention limits

Hidden Costs

Inference over-provisioning (idle GPUs)
Failed requests and retries
Development and testing inference
Data preprocessing compute

Cost Benchmarks

LLM API Pricing (approximate)

Model	Input $/M tokens	Output $/M tokens
GPT-4o mini	$0.15	$0.60
Claude 3 Haiku	$0.25	$1.25
GPT-4o	$2.50	$10.00
Claude 3.5 Sonnet	$3.00	$15.00
GPT-4 Turbo	$10.00	$30.00

ROI Framework

Cost Per Task = (Input Tokens × Input Price + Output Tokens × Output Price) / Tasks
Target: <$0.01 per task for high-volume, <$0.10 for complex tasks

Example: 100K customer support queries/month
- GPT-4o: 100K × 500 tokens avg = 50M tokens = $125-250/month
- Claude Haiku: 100K × 500 tokens = 50M tokens = $12.50-62.50/month
- Open source (self-hosted): $2,000-5,000/month infrastructure
  BUT: unlimited requests, no per-token cost

Key Takeaways

•Output tokens are 2-4x more expensive than input — optimize response length

•Model selection is the highest-impact cost lever — right-size model to task complexity

•Self-hosted open source breaks even at >10M tokens/month vs API pricing

•Hidden costs like idle GPUs and failed requests often represent 20-30% of total spend

AI Model Wiki