Most provider fallback systems start with a model list:
primary model → backup model → another backup model.
That is useful, but it is not enough.
The hard part is deciding which failures should actually move traffic to the next route.
A quota-exhausted account, a temporary upstream overload, an invalid API key, a malformed request, and a timeout after partial streamed output are not the same event. Treating all of them as “retry and fall back” creates duplicate calls, hides configuration mistakes, and makes usage records difficult to explain.
A practical routing layer needs a small normalized outcome for every attempt:
- provider and model
- protocol path
- attempt identity
- error class
- whether output reached the client
- whether the request is safe to retry
- retry-after or reset information when available
That makes policy explicit.
For example:
- Invalid credentials should fail fast. Trying more providers usually only hides a configuration error.
- A quota reset at a known time should create a cooldown, not an immediate chain of retries.
- A transient overload can justify a bounded fallback attempt.
- A timeout after partial streaming needs downstream-state awareness. Retrying blindly can duplicate output, tool execution, or billing.
- A request rejected for an unsupported parameter is a capability mismatch. The fix is request shaping, not another identical retry.
The useful metric is not “how many fallbacks succeeded?” It is whether the system completed the workflow without creating unclear retries, duplicated side effects, or untraceable cost.
I’m building Your Model around the same practical question: how do you compare provider and model paths through a real workflow without assuming that every route has identical semantics?
How does your stack distinguish quota exhaustion, temporary overload, and partial-stream failure today?
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