No, it’s not about switching JSON → TOON to save a few characters. For sure that could be good in some cases, but the point here is different.

The first wave of shipping “AI Features” is over. Now, the corporate credit card bill has arrived. And for many startups, the math just isn’t adding up.

I’m often called in to solve complex scaling issues. But recently, the biggest bottleneck I’ve found doesn’t require a system rewrite. It just requires getting back to basics.

Some weeks ago, I audited a startup that was spending a monthly fortune on the OpenAI API. Their CTO believed the high cost was the inevitable price of innovation and user volume.

He was wrong.

The problem wasn’t traffic, and it wasn’t complex architecture. The problem was elementary implementation errors.

The “Cash-Burning” Pattern

I found a code pattern in their backend that is the digital equivalent of leaving the faucet running while brushing your teeth.

Take a look at this simplified example (which might be running in your repo right now):

import { z } from "zod";
import { openai } from "./lib/ai";
import { zodResponseFormat } from "openai/helpers/zod";

const ALL_CATEGORY_CATALOG = [
  { slug: "electronics", description: "Gadgets e produtos de tecnologia avançada, incluindo smartphones, notebooks, e dispositivos smart home." },
  { slug: "home_garden", description: "Itens para decoração de casa, jardinagem, ferramentas de reparo e vida ao ar livre." },
  { slug: "fashion", description: "Vestuário, calçados e acessórios de moda, incluindo itens masculinos e femininos." },
  // more 47 items...
];

const ALL_CATEGORY_SLUGS = ALL_CATEGORY_CATALOG.map(item => item.slug);

export const classifyProduct = async (productName: string, storeContext: any) => {

  const contextBloat = `
    Store Niche: ${storeContext.niche}
    Active Integrations: ${storeContext.integrations.map(i => i.name).join(", ")}
    Active Campaign Tags: ${storeContext.marketingTags.join(", ")}
  `;

  const response = await openai.chat.completions.create({
    model: "gpt-4o", 
    messages: [
      {
        role: "system",
        content: `You are a helpful inventory assistant expert in e-commerce. Your goal is to classify products based on the provided list.
                  
             ALLOWED CATEGORIES LIST: 
            ${ALL_CATEGORY_CATALOG.map(item => `slug: ${item.slug}, description: ${item.description}`).join('\n')}
                  
             ADDITIONAL CONTEXT: 
             ${contextBloat}

             STRICT RULES:
             1. You must return a valid JSON object.
             2. Do not include markdown formatting like \`\`\`json.
             3. Use ONLY the categories provided in the list above. Do not invent new tags.
             4. If you are unsure, choose the closest match.`
      },
      { role: "user", content: `Product Name: ${productName}` }
    ],
    response_format: zodResponseFormat(
      z.object({
        categories: z.array(z.enum(ALL_CATEGORY_TAGS))
      }),
      "category_classification"
    ),
  });

  return response.choices[0].message.parsed;
};

For a developer in a rush, this works. The code runs. But for your Finance team, this is a disaster.

Can you spot the 3 major leaks here? Before reading further, look closely at the snippet above. Are you able to identify the errors and how you would fix them? 👇 Drop a comment below with your analysis before checking the solution.

The Real-World Results

Here is the actual diff from the production logs after deploying this change:

1. 📉 Total Tokens: Dropped ~33.6% from 4,157 to 2,759 per request.
2. 💰 Bottom Line: For every $3,000 spent on these requests, the cost drops to ~$2,100. That is ~$900 of pure margin.

This codebase still has room for more advanced optimizations, but I wanted to highlight the fundamental errors first. There is a common misconception among developers that cost reduction begins and ends with model selection.

While right-sizing your model is crucial, bad implementation taxes your budget regardless of the model. Even if you stick with GPT-4o, fixing how you construct your requests is the easiest money you will ever save.

Do you want to see the solution?

I documented exactly which errors were draining the budget in this case and how simple the fixes were. If you are a CTO, PM, Tech Lead, Developer, or Vibe Coder and want to start reducing your AI costs, start applying this hygiene to your codebase today: Drop your email below.

I’ll immediately send you the PDF with the Full “Before vs. After” Code Comparison + The 3-Step Fix Breakdown.

> P.S. If your company is already spending over $1,000/month on OpenAI/Anthropic and you want me to personally audit your code to stop the bleeding (without rewriting your architecture), reply to the email I’m about to send you.