Module 825 minutes
Cost Management and Optimization
Control AI costs at scale. Optimize token usage, caching, and model selection.
cost-optimizationapi-costscachingefficiency
Learning Objectives
- ✓Calculate and predict AI costs
- ✓Implement cost optimization strategies
- ✓Use caching effectively
- ✓Choose cost-effective models
AI Costs Add Up Fast
Learn to optimize before costs spiral.
Cost Calculation
GPT-4: ~$0.03/1K input, $0.06/1K output
GPT-3.5: ~$0.0015/1K (20x cheaper)
Claude: Similar to GPT-4
Example: 1M API calls at 1K tokens each = $30K-$60K/month
Optimization Strategies
1. Use cheaper models when possible
- GPT-3.5 for simple tasks
- GPT-4 only when needed
2. Reduce token usage
- Shorter prompts
- Truncate context
- Remove redundancy
3. Implement caching
- Cache common queries
- Store embeddings
- Reuse results
4. Batch requests
- Group API calls
- Process asynchronously
Monitoring Costs
```python
def track_usage(model, input_tokens, output_tokens):
cost = calculate_cost(model, input_tokens, output_tokens)
log_to_monitoring(cost)
alert_if_threshold_exceeded(cost)
```
Key Takeaways
- →Calculate costs before deploying at scale
- →Use GPT-3.5 for simple tasks, GPT-4 only when needed
- →Implement aggressive caching
- →Monitor costs in real-time
- →Set alerts for unusual spending
Practice Exercises
Apply what you've learned with these practical exercises:
- 1.Calculate costs for your use case
- 2.Implement caching layer
- 3.Test cheaper model alternatives
- 4.Set up cost monitoring