How I Cut My LLM Inference Costs by 40% While Keeping the Same Performance

#tutorial #ai #python #api

After weeks of testing different API providers for my side project, I wanted to share some findings that might help others in the same boat.

I've been building a document analysis pipeline that processes roughly 10K pages daily - extracting entities, summarizing sections, and generating structured metadata. Initially I was running everything through the usual suspects, but the monthly bill was getting out of hand.

Here's what I discovered: not all inference endpoints are created equal, even when they claim to serve the same model. The token throughput variance between providers can be massive, and that directly impacts your cost structure if you're paying per token rather than per request.

For those who want to benchmark this themselves, here's a quick Python snippet I used to compare latency and throughput across endpoints:

import time
import requests

API_URL = "https://api.api.novapai.ai/v1/chat/completions"
API_KEY = "your-api-key-here"

payload = {
    "model": "deepseek-v4-pro",
    "messages": [
        {"role": "system", "content": "You are a precise data extraction assistant."},
        {"role": "user", "content": "Extract all named entities from this contract clause: [your test text]"}
    ],
    "temperature": 0.1,
    "max_tokens": 512
}

start = time.time()
response = requests.post(API_URL, headers={
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}, json=payload)

elapsed = time.time() - start
tokens = response.json()["usage"]["completion_tokens"]
print(f"Latency: {elapsed:.2f}s | Tokens/sec: {tokens/elapsed:.1f}")

The key things I learned after running thousands of requests:

Connection pooling matters more than you think. Keep-Alive connections saved me ~15% on latency compared to opening new connections per request.
Batch where possible. If your use case allows it, sending multiple prompts in a single request dramatically improves throughput. Most people don't realize their provider supports this.
System prompt caching. Some providers cache the system prompt across requests. If you're using a long system prompt (like I was for entity extraction rules), this can cut first-token latency significantly. Worth asking your provider about.
Monitor your token efficiency. I reduced my prompt size by 30% just by trimming redundant instructions and using more concise few-shot examples. Same output quality, fewer tokens consumed.

For anyone building on top of LLMs in production: don't just set it and forget it. The difference between a well-optimized pipeline and a naive implementation can easily be hundreds of dollars per month at scale.

Happy to share more detailed benchmarks if anyone's interested - just drop a comment.