Conclusion
- No-card APIs are best for prototype validation, demos, and smoke tests.
- Vercel AI SDK migration is easiest when the provider supports OpenAI-compatible baseURL settings.
- Test streaming, tool calls, JSON output, and rate limits before shipping.
- Production chatbots need server-side keys, spend limits, logs, and fallback routes.
What to do next
- Create a test key from a no-card or trial-credit provider.
- Set the provider baseURL, apiKey, and model in server-side environment variables, not client code.
- Run a one-message chatbot smoke test, then a streaming response test.
- Test your longest real prompt and any tool/function calling path.
- Add OpenLLMAPI or another gateway when you need one production endpoint with fallback and budget attribution.
Recommended paths
| Provider | Free / credits | Best for |
|---|---|---|
| OpenRouter | Free model routes vary | Fast no-card chatbot prototype |
| Qwen DashScope | Signup credits vary | China-friendly compatible-mode Next.js tests |
| Zhipu GLM | Signup tokens vary | Domestic GLM smoke tests and fallback |
| Groq | Developer limits vary | Fast lightweight streaming tests |
| OpenLLMAPI | Trial varies | One compatible endpoint with logs, budgets, and fallback |
Global developer checklist
- Confirm whether signup, billing, and API keys work from your country before writing production code.
- Prefer OpenAI-compatible endpoints when you may need to switch models, regions, or providers later.
- Test free credits with a real smoke prompt and record latency, error shape, streaming behavior, and quota burn.
- Keep at least one fallback route for provider outages, model deprecations, and regional access changes.
Production handoff
Ship the prototype, then stabilize the route
Keep your Vercel app OpenAI-compatible while adding provider fallback, spend logs, and budget caps behind one production key. Signup is UTM-tagged for Vercel/no-card intent.
FAQ
Can Vercel AI SDK use OpenAI-compatible APIs?
Yes when you configure a compatible baseURL, key, and model through the OpenAI-style provider path. Still verify streaming and tool behavior.
Is no-card enough for production?
Usually no. It is enough for demos and smoke tests, but production needs billing, logs, budget caps, and a fallback route.
Where should I store the API key?
Only in server-side environment variables or a secure backend. Never expose prototype keys in browser code.
What breaks most often?
Incorrect baseURL, model names, streaming chunk format, JSON/tool-call behavior, and rate-limit handling.