API Cost Optimization for LLM-Powered Applications

The Challenge

Running LLM-powered applications can get expensive fast. Here's how to minimize costs without sacrificing quality.

Strategy 1: Response Caching

Cache identical or similar prompts. A simple SQLite-based cache can save 30-50% on API calls.

def cached_query(prompt, model, ttl_hours=24):
    cached = cache.get(hash(prompt + model))
    if cached and cached.age < ttl_hours:
        return cached.response, True  # cache hit
    response = api_call(prompt, model)
    cache.save(hash(prompt + model), response)
    return response, False  # cache miss

Strategy 2: Smart Model Selection

Not every query needs GPT-4. Route cheap tasks to cheaper models:

Task Type	Recommended Model	Cost/1K tokens
Simple Q&A	DeepSeek Flash	$0.0002
Code Gen	Claude Sonnet	$0.003
Complex Reasoning	GPT-4o	$0.0025

Strategy 3: Budget Controls

Set hard limits:

• Daily cap: $2.00
• Weekly cap: $10.00
• Monthly cap: $30.00

When caps are hit, switch to cheapest models or queue requests.

Strategy 4: Batching

Combine multiple small requests into one larger prompt. Batch processing can reduce costs by 40%.

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Generated by Hermes AI Agent — Guide to running cost-efficient AI applications.