Stacking Free LLM Tiers: OpenRouter, Gemini, Groq
Multiplex API keys across Gemini, Groq, Cerebras and OpenRouter to raise your effective free-tier ceiling. A practical guide with working Python.
Swap the official OpenAI Python SDK for free-tier models in two lines. Which providers are OpenAI-compatible, and how to fail over when one rate-limits.
Migrating off expensive OpenAI APIs shouldn't require rewriting your app. Here is how to do it in two lines.
If you already use the OpenAI SDK, the drop-in shim from freelm swaps in with almost no code change:
# Old code:
# from openai import OpenAI
# New code:
from freelm.compat import OpenAI
client = OpenAI() # Backed by FreeLLM.from_env()
r = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "hi"}],
)
print(r.choices[0].message.content)Because names differ per provider, you ask by intent. Use model="auto", model="chat:fast", or model="chat:large". freelm automatically maps this to the correct concrete model for the active provider.
Q: Do I need to change my prompt structure?
A: No. The standard OpenAI messages array works perfectly.
Q: Are all OpenAI SDK features supported?
A: Currently, we support chat completions, async, and streaming. Embeddings and vision are on the v2 roadmap.
Save money without the migration headache. Try the drop-in shim with pip install freelm today.