LLM Cost Optimization: 5 Ways to Reduce AI Spend

Your AI feature worked perfectly in development. It cost $50/month during testing. Now it's in production, and the bill is $5,000. What happened?

This is one of the most common stories in AI development. A feature that seemed cheap becomes expensive at scale. The good news: there are proven strategies to reduce costs without sacrificing quality.

Here are five approaches that actually work, ranked from easiest to most complex.

1. Know Where Your Money Goes

Before optimizing anything, you need to know what's costing you money. This sounds obvious, but most teams skip this step. They try to optimize everything equally instead of focusing on what matters.

Action steps:

• Tag every AI call with the feature it belongs to
• Track cost per feature, not just total cost
• Review the data weekly
• Focus optimization efforts on the top 2-3 cost drivers

Without this visibility, you're optimizing blind. You might spend a week reducing costs on a feature that only accounts for 5% of your bill.

2. Choose the Right Model for Each Task

Not every task needs GPT-4o. Many tasks work just as well with smaller, cheaper models. Here's a quick guide:

The cost difference is significant:

That's a 16x cost reduction for tasks where the smaller model works just as well.

3. Write Shorter, Better Prompts

Tokens cost money. Every unnecessary word in your prompt is wasted spend. But this doesn't mean you should sacrifice clarity—it means you should be intentional.

Before (verbose):

You are a helpful assistant that summarizes documents.
Please read the following document carefully and provide
a comprehensive summary that captures all the main points.
The summary should be clear, concise, and well-organized.
Make sure to include the key takeaways and any important
details that the reader should know about.

Document to summarize:
{document}

After (concise):

Summarize the key points from this document in 3-5 bullets:

{document}

The second prompt is clearer AND cheaper. Common prompt bloat patterns:

• Redundant instructions: "Please" and "make sure to" add tokens without value
• Over-explanation: If the model understands, don't explain further
• Unused context: Don't include information the model doesn't need
• Verbose system prompts: These are sent with every request

4. Cache Repeated Requests

If users ask similar questions, you're paying for the same computation repeatedly. Caching can dramatically reduce costs for certain use cases.

Good candidates for caching:

• FAQ-style questions
• Static content generation (product descriptions, etc.)
• Classification tasks with limited input variation
• Anything where slight input changes don't require new responses

Bad candidates for caching:

• Conversations (context changes constantly)
• User-specific content
• Time-sensitive information
• Tasks requiring real-time data

// Simple semantic caching example
const cache = new Map();

async function getCachedCompletion(prompt) {
  // Create a cache key from the prompt
  const key = hashPrompt(prompt);

  // Check cache first
  if (cache.has(key)) {
    return cache.get(key);
  }

  // Call API if not cached
  const response = await openai.chat.completions.create({...});

  // Store in cache (with TTL in production)
  cache.set(key, response);

  return response;
}

5. Set Guardrails and Alerts

Even with all the optimizations above, costs can spike unexpectedly. Maybe a feature goes viral. Maybe there's a bug causing infinite loops. You need guardrails.

Essential guardrails:

• Daily spend limits: Alert when daily spend exceeds a threshold
• Per-user rate limits: Prevent any single user from running up costs
• Request size limits: Cap maximum input length
• Error monitoring: Failed requests still cost tokens (for the input)

Set alerts at multiple thresholds: 50%, 75%, and 90% of your budget. The earlier you catch a problem, the less it costs.

Quick Wins vs. Long-Term Gains

Start with tracking and guardrails—they're low effort and high impact. Then move to model selection and prompt optimization for your highest-cost features.