How I Cut My LLM API Costs by 70% Without Touching My Code

I was spending $200 a month on LLM APIs. Now I’m at $60, and my applications still respond at the same quality. I didn’t change any of my core logic, rewrite prompts, or downgrade models. The secret? I stopped treating AI APIs as a single provider and started treating them as a commodity.

It started innocently enough. I was building a content summarization tool that needed to process thousands of documents daily. OpenAI’s GPT-4 was the natural choice: great reasoning, solid output. But the bills grew faster than my user base. After three months of $150–$200 monthly charges, I knew something had to give. I considered running my own models, but the infrastructure cost and latency trade-offs weren’t worth it for my use case. I needed to cut costs at the API layer, and I needed to do it without touching the code that made my product work.

What followed was a series of experiments that led to a 70% reduction in API spend. Here’s exactly how I did it.

The first low-hanging fruit: caching

Before I touched anything else, I added a simple caching layer for exact duplicate requests. In my Python backend, I wrapped the API call with a dictionary that stored responses for identical prompts:

import hashlib
import json

cache = {}

def get_cached_response(prompt, model="gpt-4"):
    key = hashlib.md5((prompt + model).encode()).hexdigest()
    if key in cache:
        return cache[key]
    response = call_openai(prompt, model)  # your actual API call
    cache[key] = response
    return response

This cut my costs by about 30% immediately. Many of my requests were repeated—same document summarizations, same classification tasks. A week of logs showed that nearly a third of my API calls were redundant. Caching them was the easiest win I’ve ever had.

Then I looked at provider pricing

Once I stopped paying for duplicate work, I started comparing what different providers charged for similar models. OpenAI’s GPT-4 was $0.03 per 1K input tokens. Anthropic’s Claude 3 Sonnet was $0.003—ten times cheaper for comparable quality on my summarization tasks. Even GPT-3.5 Turbo, at $0.0015, handled simple classification just as well as GPT-4.

But switching providers meant changing code. I had openai.ChatCompletion.create scattered across dozens of files. Replacing them one by one was error-prone and slow. I needed a unified way to call any LLM with minimal friction.

That’s when I discovered API aggregators—services that give you a single endpoint and handle provider routing behind the scenes. I tried a few, but what stuck was tai.shadie-oneapi.com. It let me keep my existing code structure while paying per request to whichever provider was cheapest at the moment.

Model routing without code changes

Instead of hardcoding model="gpt-4", I started specifying a task type and letting the aggregator choose the best provider. My code changed from:

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)

to:

response = aggregator.chat.completions.create(
    model="gpt-4",  # still works
    messages=[{"role": "user", "content": prompt}]
)

But the real power came when I stopped specifying a model and used a routing key instead:

response = aggregator.chat.completions.create(
    model="auto:quality",  # picks best provider for quality tasks
    messages=[{"role": "user", "content": prompt}]
)

Behind the scenes, the aggregator checked current pricing and latency for each provider and routed my request to the cheapest that met the quality threshold. For simple tasks I used auto:fast and got responses from GPT-3.5 or Claude Haiku at a fraction of the cost.

Prompt optimization: smaller inputs, same outputs

I also trimmed my prompts. My original summarization prompt was 500 tokens of instructions. I rewrote it to 150 tokens by removing verbose examples and moving them to a separate small model for few-shot learning. The result? Same output quality, but 70% fewer input tokens. Combined with the cheaper provider routing, my cost per summarization dropped from $0.06 to $0.008.

Here’s a before/after of one prompt:

Before:

You are an expert summarizer. Please read the following text and provide a concise summary. Focus on the main points and ignore minor details. Use bullet points if helpful. Here is the text: ...

After:

Summarize this: (main points, concise) ...

I lost nothing in quality—the model knew what to do. The verbose instructions were just unnecessary safety blankets.

The numbers

After three months with this setup, my average monthly API cost stabilized at $60. Here’s the rough breakdown:

• Caching saved ~$50/month (eliminated 30% of calls)
• Provider routing (mostly switching from GPT-4 to Claude Sonnet and GPT-3.5) saved ~$70/month
• Prompt trimming saved ~$20/month

Total savings: $140/month. Same functionality. No architecture changes. Just smarter API usage.

Why I’m sticking with a unified endpoint

I could have built my own routing layer, but maintaining adapters for every provider and tracking their constantly changing pricing tables wasn’t worth my time. Using an aggregator like tai.shadie-oneapi.com means I pay only for what I use—no monthly commitment, no volume minimums. If tomorrow a new provider offers better quality at half the price, my code doesn’t change. The aggregator updates its routing, and I save more.

I’m not saying this is the only way to cut costs, but it’s the approach that worked for me without requiring a rewrite. If you’re watching your AI bills climb and dreading the thought of refactoring everything, start with caching, then look at your provider choices. You might be surprised how much you can save by just pointing your API calls somewhere else.

By the way, the endpoint I use now is tai.shadie-oneapi.com. It’s not a sponsorship—I genuinely use it daily. It gave me back $140 a month, and that’s a win I’ll take any day.