LLM Cost Optimization: Reducing AI Inference Costs by 60-80%

At 1 million API calls per month, even a $0.001 difference in cost per call is $1,000/month. The techniques below compound: apply all six and you're looking at 70–80% cost reduction on realistic production workloads.

Six strategies, ranked by impact

🔀

1. Intelligent Model Routing

Route simple queries to cheaper models (GPT-4o-mini, Gemini Flash) and complex queries to premium models. 70-80% of production queries are simple enough for smaller models.

↓50%

💾

2. Prompt Caching

Cache the processed representation of long system prompts. For agents with 2,000-token system prompts and 100K calls/day, caching alone saves 60–90% of input token costs.

↓65%

✂️

3. Output Length Control

Set max_tokens aggressively. Use structured outputs (JSON) instead of prose when downstream code is parsing the output. Shorter outputs = lower cost + faster latency.

↓20%

📦

4. Batched Processing

Use batch APIs for non-real-time workloads — document processing, nightly analysis, bulk classification. Claude and GPT-4o batch APIs offer 50% discounts vs. standard pricing.

↓50%

🗜️

5. Context Window Management

Trim conversation history aggressively. Summarize old turns instead of passing them verbatim. The longest context is usually the most expensive — don't send tokens you don't need.

↓30%

🏠

6. Self-Hosted for High Volumes

At >10M tokens/day, self-hosted open models (Llama 3, Mistral) on GPU instances beat API pricing. Break-even vs. OpenAI at ~$15K/month API spend.

↓80%

Routing config on MoltBot

# Route by complexity to minimize cost
routing = {
    "rules": [
        {
            "if": "input_tokens < 500 and task == 'classification'",
            "use": "gemini-flash-2",      # $0.00004/1K tokens
        },
        {
            "if": "task in ['summarization', 'formatting']",
            "use": "gpt-4o-mini",          # $0.00015/1K tokens
        },
        {
            "default": "claude-sonnet-4"       # $0.003/1K tokens
        }
    ],
    "cache_system_prompt": True,
    "max_output_tokens": 512,
}
      

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LLM Cost Optimization: Reducing AI Inference Costs by 60–80%