FreeLLM vs LiteLLM

LiteLLM is a Python SDK that translates between 100+ provider APIs. FreeLLM is a language-agnostic HTTP gateway focused on routing across free tiers with automatic failover. Both solve the multi-provider problem, but they take different approaches and fit different setups.

Side-by-side comparison

When to use LiteLLM instead

LiteLLM is the better fit when:

• Your codebase is Python-only and you want SDK-level control over every request.
• You need 100+ provider integrations including paid models like Claude, GPT-4o, or Gemini Ultra.
• You want to call providers directly from Python code without an HTTP layer in between.
• You are not hitting rate limits and do not need automatic failover.

When FreeLLM makes sense

FreeLLM fits better when:

• Your stack is not Python. FreeLLM exposes a standard HTTP API that any OpenAI SDK can talk to, regardless of language.
• You want failover and a dashboard without configuring them separately. Both are included out of the box.
• You are working with a free-tier-only budget. FreeLLM is built around that constraint.
• You want virtual sub-keys with per-key rate and token limits to isolate different apps or team members.

What the code looks like

LiteLLM is called as a Python library. FreeLLM is called through a base URL change on any OpenAI SDK.

import litellm

response = litellm.completion(
    model="groq/llama3-70b-8192",
    messages=[{"role": "user", "content": "Hello"}]
)

from openai import OpenAI

client = OpenAI(
    base_url="https://your-freellm-instance/v1",  # only change
    api_key="your-freellm-key"
)

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Hello"}]
)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://your-freellm-instance/v1",  // only change
  apiKey: "your-freellm-key",
});