resource-optimization

Resource-Aware Optimization

Not every task requires the smartest, most expensive model. Resource-Aware Optimization (or Dynamic Routing) classifies the complexity of a user request and routes it to the most appropriate model tier. This ensures you aren't using a sledgehammer to crack a nut, saving money and improving speed.

When to Use

• High Volume APIs: When 10% of requests are complex and 90% are simple.
• Latency Sensitivity: Routing simple "Hello" or "Stop" commands to instant, small models.
• Budget Constraints: Ensuring high-end models (like GPT-4 or Opus) are only used when absolutely necessary.
• Fallback: Using a small model first, and only upgrading to a large model if the small one fails/expresses low confidence.

Use Cases

• Tiered Chatbot:
  • Simple (Greetings, FAQs) -> gpt-4o-mini
  • Medium (Summarization, extraction) -> gpt-4o
  • Complex (Coding, Reasoning) -> o1-preview
• Cascade: Try Llama-70B -> if confidence < 0.8 -> Try GPT-4.
• SLA-based: Free users -> Small Model. Paid users -> Large Model.

Implementation Pattern

def optimize_resources(task):
    # Step 1: Complexity Analysis
    # Use a very cheap model or heuristics
    complexity = classifier.classify(task)
    
    # Step 2: Dynamic Selection
    if complexity == "SIMPLE":
        model = "gpt-4o-mini"
    elif complexity == "HARD":
        model = "gpt-4o"
    else:
        model = "o1-preview" # For reasoning heavy tasks
        
    print(f"Routing to {model} for efficiency.")
    
    # Step 3: Execute
    return llm.generate(task, model=model)

Examples

Input: "My agent costs $2 per run and I need it under $0.50."

Optimization audit:

Step	Model Used	Tokens	Cost	Needed?
Document summary	GPT-4o	12,000	$0.18	✅ High complexity
Format conversion	GPT-4o	4,000	$0.06	❌ Switch to GPT-4o-mini
Final answer	GPT-4o	8,000	$0.12	✅ Customer-facing

After optimization: format step uses GPT-4o-mini → saves $0.05/run → now $1.95/run. Further: cache repeated document summaries → estimated $1.40 savings.

Troubleshooting

Problem	Cause	Fix
Costs still high after optimization	Uncached repeated calls	Implement semantic cache with 24h TTL for deterministic queries
Switching to smaller model degraded quality	Task above model capability	Use router: small model for routing, large model only for core reasoning
Latency increased after optimization	Added caching overhead	Use async cache warming; pre-populate cache at job start
Token count growing over time	Prompt not being trimmed	Summarize conversation history every 10 turns; inject summary only