14.8k stars, 2.2k forks, 16 free LLM providers. FreeLLMAPI just productized the free-tier stack.
Open-source LLM proxy FreeLLMAPI hit 14.8k GitHub stars in six weeks and shipped a paid Premium tier ($19 per year) for a live 161-model catalog. The free tier exposes 82 models on a 30-day delay. The architecture stacks 16 free-tier providers behind one OpenAI-compatible endpoint, with smart routing, automatic failover, streaming, vision, and per-key rate-limit tracking. MIT licensed. Self-hosted.
The decision it puts on your desk
If you spend more than $1K per month on LLM APIs, spend 2 hours this week stacking free tiers behind FreeLLMAPI and route your dev or staging traffic through it. Re-baseline your model cost against actual free-tier performance, not vendor list price. If 30 percent of your tokens move to free tiers without quality loss, extend the pattern to non-customer-facing production by end of quarter. The free-tier floor is no longer a hack. It is a category.
FreeLLMAPI crossed 14.8k GitHub stars and 2.2k forks in roughly six weeks. Last week the project launched a paid Premium tier at $19 per year, unlocking a live model catalog that ships 161 models the same day they drop. The free tier gives you 82 models on a 30-day delay. The architecture stacks 16 free-tier providers behind a single OpenAI-compatible endpoint: Gemini, Groq, Cerebras, Mistral, Cloudflare, OpenRouter, NVIDIA, plus nine more. The aggregate monthly free quota is around 1.7 billion tokens. Smart routing, automatic failover, streaming, vision, tool calling, per-key rate-limit tracking. MIT licensed. Self-hosted. Brings-your-own-keys.
The cost angle is the lead
Most engineering teams spend $1K to $50K per month on LLM APIs. Stacking free tiers does not eliminate that bill. It sets a floor. If your workload can route 30 to 50 percent of total tokens to free-tier models, the typical share for non-frontier work like classification, extraction, summarization, transformation, and bulk generation, the savings compound.
A founder running $5K per month on OpenAI and Anthropic with a moderate workload can credibly cut that to $3.5K within a quarter by routing lower-stakes traffic to free tiers through a proxy. The numbers sharpen at higher spend. At $20K per month, a 30 percent reduction is $6K per month, $72K per year, on infra your competitors may not have on the spreadsheet yet. Free-tier quotas at frontier labs grew roughly 40 percent year over year in 2025. They are growing again in 2026.
The architecture angle is what makes it stick
FreeLLMAPI is not just a price play. It is a routing and failover layer your engineering team was going to build anyway. The proxy scores available models on a convex combination of reliability, speed, and intelligence, sampled from a Beta posterior over recent performance. It rotates keys within a model to avoid per-key rate limits. It falls back to the next model in your chain when one provider rate-limits, errors, or returns a malformed response, up to 20 attempts in a 45-second budget. It sticks each session to one model for 30 minutes to prevent mid-conversation quality swings.
For an engineering team running a multi-model product, this is the routing layer they were going to build anyway. FreeLLMAPI ships it as open source. The proxy also speaks the OpenAI wire format, so adoption is a base-URL change in any client that already targets OpenAI. No SDK swap. No rewrite. The full stack runs on your own machine or in your own VPC. Nothing leaves your perimeter. API keys are encrypted at rest with AES-256-GCM. Model catalog updates are signed with Ed25519 against a pinned public key.
The catch
Three things this does not solve.
First, you still need free-tier accounts. Sixteen providers means up to sixteen sign-up flows, identity verifications, and rate-limit windows. Budget 2 to 3 hours of setup time. The proxy does not bypass onboarding.
Second, the free-tier pricing shifts weekly. Providers launch, retire, and change quotas without notice. FreeLLMAPI ships a signed model catalog and refreshes it twice per day. Your model choice from January will not be your model choice from April. Treat any free-tier strategy as a moving target.
Third, free tiers carry terms-of-service constraints. Most providers restrict free-tier usage to personal experimentation, not commercial production. The risk of account suspension on a high-traffic commercial workload is real. The proxy will not save you if a provider decides you are not playing by their rules. Run it in dev and staging. Run it for non-customer-facing batch work. Read the fine print before you route paying customers through it.
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