📝 Originally published (in Japanese) at forge.workstyle.tech.
Introduction
As the utilization of Large Language Models (LLMs) advances, there is a growing need for "reducing API costs" and "easily trying out multiple models" in development and prototyping. However, many commercial API services require credit card registration, which can be a barrier.
In this article, we have compiled a list of LLM API providers that offer free tiers without requiring credit card registration, as well as practical knowledge on how to combine them to build robust systems.
Note: The free tiers and models offered by each service are subject to change frequently, so please check the official website of each company for the latest information before introduction.
[!warning] Update on 2026-09-07: Some parts of this article are currently incorrect.
After re-verifying all providers two months after publication, we found that three of the providers listed below are no longer usable.
- Cerebras … The free tier has ended and returns
402 Payment required.- GitHub Models … The service itself has ended (
410 Gone).- SambaNova … Six out of seven models return
402. Only one model remains free (with a 12-second response time).On the other hand, Groq, Gemini, and OpenRouter still have their free tiers intact, and the issues were due to model name expiration or key expiration.
The full records of the verification (10 different "ways of failure" and recovery procedures) are summarized here:
Re-testing the "Free LLM API Summary" I wrote two months ago, half of them were dead
Major Providers with Free Tiers and No Credit Card Requirement
Below is a summary of providers with relatively easy-to-use free tiers.
| Provider | Acquisition Method | Free Tier Estimate | Characteristics |
|---|---|---|---|
| Google Gemini | AI Studio | Model-dependent RPM/day | High-quality models are available. Note that prompts may be used for learning. |
| Groq | Console | Model-dependent RPM/RPD limits | Super-fast inference using LPU is the biggest feature. |
| Cloud | ⚠ As of 2026-09, the free tier has ended (402 Payment required) | ||
| OpenRouter | Website |
:free model 20 RPM / 50-1000 RPD |
Multiple models can be switched with a single API key. |
| NVIDIA NIM | Build | Free credits/tier | Many open models can be executed quickly. |
| — | — | ⚠ As of 2026-09, the service has ended (410 Gone) | |
| Cohere | Dashboard | Trial tier (around 20 RPM) | Models suitable for trial purposes. |
| SambaNova | Cloud | ⚠ As of 2026-09, six out of seven models return 402. Only one model remains free (with a 12-second response time) | |
| Mistral | Console | Experiment tier (large capacity) | Large-scale token usage is possible (with authentication conditions). |
Important Design Guidelines for Practical Use
Free-tier APIs are very powerful, but when incorporating them into commercial applications, there are specific considerations to keep in mind.
1. Building a Fallback Strategy
The biggest challenge when using free-tier APIs is service shutdown due to API limits (Rate Limit) or daily quotas. To avoid this, it is essential to manage multiple providers as a single "pool" and automatically switch to another provider when one reaches its limit.
For example, using multi-provider routers like LiteLLM to group models allows maintaining application availability even if a specific provider goes down.
2. Considerations for Agent Use: Model Compatibility
When building AI agents, attention is necessary for the output format of models. Recently, models that output reasoning_content (thinking process) have emerged, but if the output format differs between models, it can cause context breakdown during multi-turn conversations. When using free tiers for agent purposes, it is essential to choose models with unified behavior.
3. Security and Privacy
When using free tiers, many services come with the condition that "input data may be used for model learning." Therefore, it is essential to avoid including confidential or personal information in prompts. For applications involving sensitive information, it is recommended to use paid plans or enterprise contracts that guarantee data is not used for learning.
Conclusion
Free-tier API providers are extremely powerful tools for prototyping and small-scale automation tasks. Rather than using them individually, combining multiple providers to optimize "availability" and "speed" is the key to practical use.
- Prototyping and one-time generation: Free tiers are sufficient.
- Conversational agents and high-frequency communication: Consider using paid subscriptions from the perspective of capacity, speed, and privacy.
- Design points: Implementing fallbacks and considering data handling (use for learning).
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