How to Reduce Your LLM API Costs by 30-60%

If you're building applications with ChatGPT, Claude, or other large language models, you've probably noticed: LLM costs add up fast. For production applications processing thousands or millions of requests, API costs can quickly become one of your largest expenses.

The good news? There are proven strategies to reduce these costs by 30-60% without sacrificing quality or functionality. In this comprehensive guide, we'll walk through exactly how to optimize your LLM token usage.

Understanding LLM Pricing

First, let's understand how LLM pricing works. All major providers charge based on tokens - roughly 4 characters or 0.75 words. Here's current pricing (January 2025):

At scale, even small optimizations make a huge difference. Reducing token usage by 40% on 10 million monthly requests can save thousands of dollars.

Strategy #1: Use TOON Instead of JSON

The single biggest optimization you can make is switching from JSON to Token-Oriented Object Notation (TOON) for structured data in your prompts.

Why TOON Saves Tokens

• No repeated keys: JSON repeats field names for every array item. TOON declares them once.
• Minimal syntax: No braces, brackets, or excessive quotes.
• Tabular format: CSV-like efficiency for uniform arrays.

Real Example

{
  "customers": [
    {
      "id": "C001",
      "name": "Alice Johnson",
      "plan": "Pro",
      "mrr": 99
    },
    {
      "id": "C002",
      "name": "Bob Smith",
      "plan": "Enterprise",
      "mrr": 499
    },
    {
      "id": "C003",
      "name": "Carol White",
      "plan": "Basic",
      "mrr": 29
    }
  ]
}

customers[3]{id,name,plan,mrr}:
  C001,Alice Johnson,Pro,99
  C002,Bob Smith,Enterprise,499
  C003,Carol White,Basic,29

Strategy #2: Optimize Prompt Engineering

Beyond data format, how you structure prompts matters:

• Remove redundancy: Don't repeat instructions in every prompt. Use system messages.
• Be concise: "Summarize" instead of "Please provide a summary of the following text"
• Use abbreviations: When context is clear, shorten field names (usr instead of user_information)
• Batch requests: Process multiple items in one API call instead of separate calls

Strategy #3: Choose the Right Model

Not every task needs GPT-4. Consider:

• GPT-3.5 Turbo: 20x cheaper than GPT-4, great for simple tasks
• Claude Haiku: Faster and cheaper for straightforward queries
• Task-specific models: Embedding models for similarity, specialized models for classification

Strategy #4: Implement Caching

Don't make the same API call twice:

• Cache common responses (FAQ answers, product descriptions)
• Use semantic caching for similar queries
• Store embeddings for retrieval instead of re-generating

Implementation Roadmap

Conclusion

Reducing LLM costs by 30-60% is achievable with the right strategies. The combination of TOON format for data, optimized prompts, smart model selection, and caching can dramatically cut your AI expenses without sacrificing quality.

Start small: convert one high-traffic endpoint to TOON and measure the impact. You might be surprised how much you save.