One model down shouldn't take your app down

Your LLM provider will have an outage. The only question is whether your app goes down with it.

# Primary: the flagship
curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxxxxxxx" \
  -d '{"model":"deepseek-v4-pro","messages":[{"role":"user","content":"Summarize this meeting transcript."}]}'

# 503 / timeout? Same request, different model. That's your failover.
curl https://aibridge-api.com/v1/chat/completions \
  -H "Authorization: Bearer mb-xxxxxxxx" \
  -d '{"model":"glm-4-plus","messages":[{"role":"user","content":"Summarize this meeting transcript."}]}'

One endpoint. One API key. A whole fleet of models to fall back to when your first choice hiccups. That's the difference between "my AI feature is down" and "my AI feature is down for nobody."

The single-provider fragility problem

If you built your app on one model provider, your uptime is their uptime. When they degrade — and every provider degrades eventually — you're stuck watching a status page instead of shipping.

You could add a second provider yourself. That means a second SDK, a second auth flow, a second billing relationship, and a retry/fallback layer that treats two vendors as two entirely different systems.

Or you could point at an endpoint that already fronts 15 models across 4 vendors, and treat failover as a config change instead of an integration project.

Failover as a one-liner

Because every model sits behind the same OpenAI-compatible contract, your fallback logic collapses to a loop over a list of model names:

MODELS = ["deepseek-v4-pro", "glm-4-plus", "qwen3-235b-a22b", "kimi-k3"]

def chat(messages):
    for model in MODELS:
        try:
            return client.chat.completions.create(model=model, messages=messages)
        except (RateLimitError, APITimeoutError, ServiceUnavailableError):
            continue
    raise RuntimeError("All models exhausted — this almost never happens")

Four vendors means a single vendor's outage stops being your outage. When DeepSeek has a rough hour, glm-4-plus picks up the call. When GLM rate-limits, Qwen takes the next one. Your users never notice.

The same trick solves A/B testing

The loop above also answers a question every builder eventually asks: "which model is actually better for my use case?"

Send the same prompt to three models, diff the outputs, and pick a winner on your own quality bar — not on someone else's benchmark. One endpoint makes a proper bake-off a ten-line script instead of a week of integrations.

What's in the fleet

DeepSeek — deepseek-v4-pro, deepseek-v4-flash, deepseek-reasoner, deepseek-coder, deepseek-chat
Qwen — qwen3-235b-a22b, qwen-plus (131K), qwen-max
GLM — glm-4-plus, glm-4-air, glm-4-flash
Moonshot — kimi-k3 (1M context), moonshot-v1-128k / -32k / -8k