Stack Free LLM Tiers for Unlimited-ish AI
# Each LLM provider meters its free tier separately, so routing across Gemini, Groq, OpenRouter, NIM, Cerebras and Mistral multiplies your free capacity.
To get near-unlimited free LLM usage, stack multiple providers' free tiers behind one router. Each provider meters its free quota independently, so combining Google Gemini, Groq, OpenRouter, NVIDIA NIM, Cerebras, and Mistral gives you the sum of all their free limits — and when one hits a rate limit, you fail over to the next instead of paying.
Why stacking works
A single free tier is capped and bursty. Google AI Studio gives generous daily requests; Groq is blazing but rate-limited per minute; Cerebras is token-limited per day; Mistral's free tier is low-RPM. None alone is reliable for production. But their limits are independent counters, so routing one request to Gemini, the next to Groq, and overflow to OpenRouter's :free models effectively adds the tiers together. The catch is operational: you need per-key rate tracking and automatic failover, or you spend your time babysitting 429s.
The 2026 free-tier landscape
These are the providers worth stacking, with their free limits as of June 2026. Treat them as defaults — providers change quotas often.
| Provider | Free limit | Strengths |
|---|---|---|
| Google AI Studio (Gemini) | ~1,500 req/day, 1M context | Huge context, no card |
| Groq | 30 RPM, 14,400 req/day | ~700 tok/s, fastest |
| Cerebras | ~30 RPM, 1M tokens/day | Fast, big daily tokens |
| OpenRouter | ~50–1,000 req/day | Many :free model variants |
| NVIDIA NIM | Metered by build credits | Broad model catalog |
| Mistral | 2 RPM, 1B tokens/month | Full model lineup free |
Stacked, that is thousands of free requests per day across complementary strengths — fast short calls on Groq, long-context on Gemini, variety on OpenRouter.
Don't put failover in your app code
The wrong fix is more retry logic scattered through your application. The right fix is a gateway layer that absorbs 429s, rotates keys, fails over to backup providers, and tracks per-key usage — so your app just asks for a completion. That separation keeps business logic clean and makes the reliability behavior testable in one place.
Stacking with freelm
freelm is an open-source Python gateway built for exactly this. You give it the keys you have; it pools all six providers behind one OpenAI-compatible call, paces each key with a requests-per-minute bucket plus a daily counter, and fails over automatically.
pip install freelm
from freelm import FreeLLM
# reads OPENROUTER_API_KEY, GEMINI_API_KEY, GROQ_API_KEY,
# CEREBRAS_API_KEY, MISTRAL_API_KEY, NVIDIA_API_KEY from env
llm = FreeLLM.from_env(strategy="quota_aware")
print(llm.text("Summarize the CAP theorem in 2 sentences."))
The quota_aware strategy routes to the provider with the most headroom right now and skips any key that is cooling down, which naturally spreads load across the stack. Switch to round_robin to distribute evenly, or priority to prefer your favorite provider and only spill over when it is exhausted.
Respecting each tier automatically
Stacking only works if you stay under each tier's limits. freelm tracks requests per minute and per day per key, so a key predicted to be exhausted is skipped before you waste a call — no surprise bans. When everything is briefly rate-limited, wait=True will pause until a key frees up instead of failing:
llm = FreeLLM.from_env(strategy="quota_aware", wait=True, max_wait=20)
Frequently Asked Questions
Does stacking free tiers violate provider terms? Use each provider within its own free terms — one account, normal usage. Stacking means routing across providers you each signed up for legitimately, not creating fake accounts.
How much free capacity can I get? It depends on which providers you add, but combining the six above yields thousands of requests per day with complementary strengths (fast, long-context, high-variety). Add more keys to raise it further.
What happens when every provider is rate-limited at once?
freelm either raises a clear NoProvidersAvailable error or, with wait=True, briefly sleeps until a key recovers — bounded by max_wait.
Which strategy should I use?
quota_aware for best free-capacity use, round_robin to spread evenly, priority to prefer a primary provider. All four are one constructor argument.
Is this production-ready? For free-tier workloads, prototypes, side projects, and bursty internal tools, yes. For high-volume production, stack free tiers as a cheap fallback behind a paid primary.
Written by Shihab Shahriar Antor — AI Engineer & Founder of Shahriar Labs. The gateway in this post is freelm — pip install freelm (PyPI).