My LLM API Bill Hit $847/Month. Here is the Open-Source Proxy That Cut It to $89.

My LLM API Bill Hit $847/Month. Here's the Open-Source Proxy That Cut It to $89.

Last November, I got the bill. $847.32 for LLM API calls.

I was using GPT-4o for everything — code generation, content writing, data analysis, chatbot responses. Most of those requests didn't need GPT-4o. A $0.14/M-token model could handle 80% of them just fine.

So I built a proxy. A single Python file. No framework, no dependencies, no Docker required (though I included one). It sits between your app and the LLM providers, and routes every request to the cheapest provider that can handle it.

My bill dropped to $89/month. That's an 89% reduction.

The Problem: You're Overpaying for AI

Here's what LLM providers actually charge per million tokens in June 2026:

Provider	Model	Input	Output
DeepSeek	V4 Flash	$0.14	$0.28
Google	Gemini 2.0 Flash	$0.075	$0.30
OpenRouter	Various	$0.12	$0.30
OpenAI	GPT-4o	$0.15	$0.60

Look at that output pricing. OpenAI charges 2x-3x more for comparable quality. DeepSeek's V4 Flash benchmarks within 5% of GPT-4o on most tasks, at a fraction of the cost.

But here's the catch: no single provider is always the cheapest. Gemini Flash is cheapest on input tokens. DeepSeek is cheapest on output. OpenRouter has models the others don't. And sometimes a provider goes down, and you need a fallback.

You need routing logic. Most developers either:

1. Hard-code one provider (and overpay)
2. Build their own routing (takes weeks)
3. Use a paid proxy service (adds another bill)

I wanted option 2, but in an afternoon.

The Architecture: Single-File Python Proxy

The core idea is simple:

# Pseudocode — actual implementation is 450 lines
def route_request(request):
    # 1. Parse the request (OpenAI-compatible format)
    model = request.get("model", "gpt-4o")

    # 2. Check which providers support this model
    candidates = get_providers_for_model(model)

    # 3. Sort by estimated cost for this specific request
    candidates.sort(key=lambda p: estimate_cost(p, request))

    # 4. Try cheapest first, fall back on failure
    for provider in candidates:
        try:
            response = forward_to(provider, request)
            log_usage(provider, request, response)  # SQLite
            return response
        except ProviderDown:
            continue

    raise AllProvidersDown()

The real implementation handles:

• Token counting for accurate cost estimation
• Retry logic with exponential backoff
• Usage tracking in SQLite (because what gets measured gets optimized)
• Analytics endpoint for daily/weekly/monthly reports
• Reseller margins — yes, you can resell API access at a markup

Drop-In Replacement

The best part? It's a drop-in replacement for any OpenAI-compatible SDK:

# Before (direct OpenAI)
from openai import OpenAI
client = OpenAI(api_key="sk-...")

# After (through proxy — same API, different port)
from openai import OpenAI
client = OpenAI(
    api_key="your-proxy-key",
    base_url="http://localhost:8765/v1"
)

# This works exactly the same
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Hello!"}]
)

That's it. Change base_url, and your entire app is now routing through the cheapest provider.

The CLI Manager

I also built a CLI tool for monitoring:

$ python3 proxy_manager.py status
┌─────────────┬─────────┬────────────┬──────────┐
│ Provider    │ Status  │ Avg Latency│ Requests │
├─────────────┼─────────┼────────────┼──────────┤
│ DeepSeek    │ ✅ UP   │ 342ms      │ 12,847   │
│ Gemini      │ ✅ UP   │ 289ms      │ 3,291    │
│ OpenRouter  │ ✅ UP   │ 456ms      │ 1,102    │
│ OpenAI      │ ✅ UP   │ 521ms      │ 89       │
└─────────────┴─────────┴────────────┴──────────┘

$ python3 proxy_manager.py report
This month: 17,329 requests | $89.42 total cost
Savings vs OpenAI direct: $757.90 (89.5%)

$ python3 proxy_manager.py margin --markup 2.5x
Reseller margin: $134.13 profit on $89.42 cost

Real Numbers After 6 Months

I've been running this proxy in production since December 2025:

• 147,000+ requests routed
• $534 total cost (vs $4,812 estimated OpenAI cost)
• $4,278 saved over 6 months
• 99.7% uptime (automatic failover when providers go down)
• Zero code changes to existing apps — just the base_url swap

Why I'm Sharing This

I packaged this into a complete kit with:

• The proxy server (450 lines, Python stdlib only)
• The CLI manager (monitoring, reports, margins)
• A one-command launcher script
• A Dockerfile for containerized deployment
• MIT license — use it however you want

If you're spending more than $50/month on LLM APIs, this pays for itself in the first month.

👉 Get the AI API Arbitrage Proxy Kit — $29

Or build your own from the architecture above. The routing logic is straightforward — the value is in the production-ready implementation with error handling, logging, and the CLI tool.

Quick Start (5 minutes)

# 1. Download and extract
unzip ai-api-arbitrage-proxy.zip
cd ai-api-arbitrage-proxy

# 2. Set your API keys (at minimum, one provider)
export DEEPSEEK_API_KEY=sk-...
export GEMINI_API_KEY=...     # optional
export OPENROUTER_API_KEY=... # optional

# 3. Start
python3 proxy_server.py

# 4. Test
curl http://localhost:8765/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-chat","messages":[{"role":"user","content":"Hello!"}]}'

That's it. Your AI costs just dropped by 80-90%.

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What's your monthly LLM API bill? Drop a comment — I'm curious if anyone is paying more than I was.

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Disclosure: I built this proxy and sell it on Gumroad. The architecture and routing logic are described above so you can build your own if you prefer. The paid version is the production-ready package with CLI tools, Docker support, and 6 months of battle-testing.