35,000 Developers Are Stacking 34 Free AI Tiers Behind One API Key
FreeLLMAPI aggregates free tiers from 34 providers into a single endpoint, giving developers 7.4 billion tokens a month without paying.
Tools · Source: GitHub (FreeLLMAPI repo)
What happened
Every AI lab now offers a free tier. They give you a few million tokens a month and a few thousand requests a day. On its own, each tier is just a toy. A new open-source project called FreeLLMAPI changes the math entirely. It stacks 34 different free tiers into a single OpenAI-compatible endpoint. The repository already has over 36,000 stars on GitHub.
The router handles the heavy lifting locally. It connects to 635 free model endpoints across 474 model families. Supported providers include Google, Groq, NVIDIA, Cerebras, and HuggingFace. You get access to models like Qwen3, DeepSeek V4, GLM-5, and Llama. It supports chat, embeddings, image generation, and audio transcription. When one provider hits a rate limit, the system automatically falls over to the next available provider. This gives developers up to 7.4 billion tokens a month of working inference capacity.
The tool integrates directly with popular coding agents. You can plug it into Cursor, Claude Code, Aider, and Gemini CLI. Keys are stored encrypted on your local machine. The core router is free and open-source under an MIT license. Free users get monthly catalog updates, while a premium version offers same-day model updates for nineteen dollars a year.
Key facts
- 7.4 billion — Tokens per month of working inference capacity
- 34 — Free LLM providers supported
- 635 — Free model endpoints available
- 36.2k — GitHub stars for the FreeLLMAPI repository
- $19 — Yearly cost for the premium live catalog updates
Why it matters
Stacking free tiers manually is a nightmare. You have to manage thirty-four different SDKs. You have to track thirty-four different rate limits. You have thirty-four different places a request can fail. This router removes the friction of provider fragmentation. It speaks multiple wire formats, including the Anthropic Messages API and native Gemini surfaces. Builders can run massive local experimentation and power coding agents without burning cash on API credits. You just point your client library at a local server and it routes everything transparently.
The second-order effect is a massive shift in how developers consume AI. Labs offer free tiers as loss leaders. They want to lock developers into their specific ecosystems and proprietary SDKs. FreeLLMAPI commoditizes these loss leaders. Developers can now extract the maximum free value across the entire industry without ever committing to a single provider. The landscape shifts weekly as providers launch or retire models, but the router tracks all of that automatically.
For builders
Run coding agents for free
Point Cursor, Aider, or Claude Code at your local FreeLLMAPI endpoint. The router handles the rate limits and failovers across multiple providers. You can generate code all day without paying API costs.
Synthesize answers across models
Use the virtual fusion model feature to fan out a single prompt. The router sends it to a panel of diverse free models in parallel. A judge model then synthesizes the drafts into one final, high-quality answer.
Keep your API keys local
The router runs locally on your machine. It stores your provider keys encrypted and never exposes them to a public proxy. You get the benefits of an aggregator without sacrificing your security.
My take
AI labs thought free tiers would build loyalty. Instead, developers built a router to strip-mine them all at once. If you are paying for API credits just to experiment or run local coding agents, you are doing it wrong. Free is the smartest move when you have the right infrastructure.
Original reporting: GitHub (FreeLLMAPI repo). This is my rewrite and opinion.