Every LLM provider has outages. OpenAI has had multi-hour incidents that took down products built entirely on GPT-4. Anthropic has hit rate limits during traffic spikes that returned 429s to paying customers. If your app calls a single provider directly, their outage is your outage — and your users don’t care whose fault it was.
Why single-provider apps break
A typical integration looks like this: pick a model, call its API directly, handle the response. It works fine until the provider has a bad day. Then every request fails, and the only fix is waiting for their status page to turn green, because your code has no path to try something else.
The fix isn’t “pick a more reliable provider” — even the best providers have incidents. The fix is having a fallback path that doesn’t require a deploy to activate.
How model fallbacks work
Aniron lets you specify a fallback chain in a single request: a primary model, and one or more backups tried in order if the primary errors, times out, or rate-limits.
import OpenAI from 'openai';
const client = new OpenAI({
baseURL: 'https://api.aniron.ai/v1',
apiKey: process.env.ANIRON_KEY,
});
const response = await client.chat.completions.create({
model: 'anthropic/claude-sonnet-4',
fallback_models: ['openai/gpt-4o', 'meta-llama/llama-3.1-70b'],
messages: [{ role: 'user', content: 'Draft a refund policy summary.' }],
});If Claude Sonnet 4 returns a 429 or 5xx, Aniron retries the same request against GPT-4o automatically, then Llama 3.1 70B if that also fails — all within the same HTTP call, no client-side retry logic required.
What to put in your fallback chain
Match capability first. A fallback that can’t do tool use isn’t a real fallback for a tool-calling request. Check each model’s capability list before chaining it.
Order by cost, not just capability. A common pattern: primary model for quality, a similarly-capable model from a different provider as the outage fallback, and a cheap high-throughput model as a last resort for degraded-but-available service.
Test the failure path. A fallback chain you’ve never actually triggered is a guess, not a safety net. Point a request at a deliberately invalid model string to confirm your chain activates and the response shape stays consistent for your downstream code.
When fallbacks aren’t enough
Fallbacks handle provider-level failures, not application-level ones. If a model produces a bad answer but returns a 200, no fallback logic fires — that’s a prompt or evaluation problem, not a reliability problem. Fallbacks also don’t help with cost: routing every request through three providers for redundancy on every call is wasteful. Reserve multi-model chains for requests where availability actually matters, and let cheaper, single-model calls stay simple.
Related resources
Model Fallbacks feature page — Full configuration reference for fallback chains and retry behavior.
Prepaid vs Metered Billing — How per-key budgets limit the blast radius of a runaway fallback chain.
GPT-4o vs Claude Sonnet 4 — Pick a matched pair of models for a fallback chain.