Keep AI Features Running When a Provider Goes Offline

Provider outages should trigger a qualified failover path, not a late-night rewrite. ProofMap helps teams prove backups before they need them.

Why Choose ProofMap

Test alternate runtimes against the same objectives your production model must satisfy.

See which tasks pass on the backup model, which need fallback, and which should stay degraded until the primary returns.

Use approved runtime mappings during an outage instead of debating model behavior while users are waiting.

Decision area	Ad hoc workflow	ProofMap
Model or provider change	Teams compare demos, skim logs, and make a judgment call under pressure.	Run baseline-versus-challenger evaluations and see pass/fail evidence before a change ships.
Cost and performance tradeoff	Savings, latency, and quality are discussed separately, usually without a shared source of truth.	Compare quality evidence with cost, runtime, and fallback options in the same qualification workflow.
Production approval	Prompts and model choices move through informal review or one-off scripts.	Only qualified prompt packages and runtime mappings are promoted for production use.
Incident readiness	Fallbacks are invented after prices change, providers fail, or behavior drifts.	Backup models, prompt mappings, and fallback policies are qualified before they are needed.

How does ProofMap help with provider outages?

It lets you qualify backup providers ahead of time and keep evidence for which prompts and tasks can safely fail over.

Can every workflow fail over to another provider?

Usually not. ProofMap makes partial failover explicit so teams can keep safe capabilities online and disable or fallback the risky ones.

Who is this for?

Teams building AI agents or LLM-backed workflows that need evidence before changing prompts, models, providers, or fallback policies.

What does ProofMap produce?

A qualification trail: objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.

Create an evidence-backed failover path before the next AI provider incident.