I’ve been building with LLM APIs for a while, and one thing that keeps getting annoying is not the models themselves — it’s managing all the different providers.
OpenAI for one use case, Claude for another, Gemini for long-context tasks, DeepSeek or Qwen for cost-sensitive workflows… and suddenly you’re dealing with different API keys, dashboards, pricing pages, rate limits, billing systems, and slightly different integration patterns.
At some point, the “AI part” becomes less of the problem. The infrastructure around it starts eating time.
That’s why I build TokenBay, a unified API platform that lets you access multiple AI models through one API key:
The idea is simple: instead of wiring your app to each model provider separately, you use one OpenAI-compatible API layer and switch between models depending on the task.
For example:
- use stronger models for reasoning-heavy tasks
- use cheaper models for summaries, classification, or simple chat
- test GPT, Claude, Gemini, DeepSeek, Qwen, GLM, etc. without rebuilding your integration every time
- manage credits and usage in one place instead of jumping across dashboards
I don’t think everyone needs a unified API gateway. If your app only uses one model provider, direct API access is probably cleaner.
But once you start comparing multiple models, optimizing cost, or building fallback into production workflows, having one API layer starts to make a lot more sense.
There are also some launch benefits available right now:
15% off most models
500 free credits
Invite a friend → both get 200 credits
I’m curious how other builders are handling this.
Are you still integrating directly with each provider, or are you using a unified API gateway for multiple LLMs?
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