The Cost of Intelligence

If you've built an AI agent recently, you've probably felt the sting of a surprise OpenAI or Anthropic bill. You build a cool project, it gets a little traction, and suddenly you are paying $50/day in API costs just to keep the lights on.

What most developers don't realize is that there is a massive amount of free LLM capacity scattered across the internet:

• OpenRouter: Free tier models like Llama 3 and Mistral.
• Google AI Studio: Generous free tier for Gemini 1.5 Flash and Pro.
• NVIDIA NIM: Free credits and endpoints for powerful open-source models.
• Groq: Blazing fast inference with a generous free tier.
• Cerebras & Mistral: Free endpoints for high-throughput testing.

The problem? They are fragile. Free tiers have strict rate limits. If you hardcode your app to use Groq's free tier, it will break the moment you hit 30 RPM.

The Solution: FreeLM

I built freelm to solve this exact problem.

freelm is an open-source gateway (available for Python via PyPI and Node.js/TypeScript via npm) that pools all these free providers behind a single, fault-tolerant call.

Instead of relying on one free API key, you feed freelm all your free API keys. It handles the rest.

How it Works

When you make a request:

1. freelm attempts to call your highest-priority provider (e.g., OpenRouter).
2. If OpenRouter returns a 429 Rate Limited error, freelm intercepts it.
3. It instantly trips a circuit breaker for that key and retries the request using your next provider (e.g., Gemini).
4. Your application receives the LLM response without crashing, completely unaware that a failover just happened.

Getting Started

Building a zero-cost stack takes two minutes.

In Node.js:

npm install freelm

In Python:

pip install freelm

Set your environment variables:

export OPENROUTER_API_KEY="sk-or-..."
export GEMINI_API_KEY="AIza..."
export GROQ_API_KEY="gsk_..."

And drop it into your code:

from freelm.compat import OpenAI

# It automatically acts as a drop-in shim for the OpenAI SDK!
client = OpenAI() 

response = client.chat.completions.create(
    model="auto", # Automatically picks the best free model available
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Stop Paying for Dev Environments

By using freelm, I've entirely eliminated LLM costs for my development, testing, and staging environments, and even for low-traffic production side-projects.

You no longer have to choose between going broke and shutting down your app. Build the zero-cost stack.