"Multi-vendor" means different things depending on how much engineering investment you're willing to put behind it — worth deciding explicitly before building anything, since the checklist below changes depending on which pattern you're after.
Decide which multi-vendor pattern you actually need
Three common patterns cover most teams: a cold failover, where a backup vendor sits unused until the primary has an outage or a sustained rate-limit problem; task-based routing, where different request types deliberately go to different vendors or model tiers based on difficulty and cost; and dynamic routing, where requests are placed in real time based on live cost, latency, or availability, usually through a routing layer built for it. Each is a meaningfully different amount of engineering work, so picking one deliberately — rather than drifting into whichever integration got built first — keeps the effort proportional to the actual need.
Check the pricing unit before you compare cost across vendors
Not every AI vendor bills per million tokens — some bill per GPU-second, per GPU-hour, per character, per megapixel, or as a flat subscription regardless of usage. Comparing a raw per-unit number across vendors without first confirming what that unit actually is produces a cost estimate that's wrong, not just imprecise, and it's an easy mistake to make when scanning a vendor's pricing page quickly. Normalizing every vendor under consideration to the same real-world workload — a specific monthly request volume and shape — before comparing is the only reliable way to get an honest number.
Rate limits and quotas don't transfer to a new vendor
A second vendor added as a real failover starts on its own default rate-limit tier, which may be far below what your traffic actually needs at the moment you'd need to fail over to it. If the backup is meant to absorb real load during an incident, ramping it under normal conditions ahead of time — rather than discovering its limits during the actual outage — is what makes it a usable fallback instead of a second outage.
Run the same eval suite against every model on the shortlist
The real switching cost is behavioral drift, not API integration — two models can accept the same prompt and produce meaningfully different quality, tone, or failure modes. Treating a vendor or model change like a dependency upgrade — evaluated against your own test set before it takes real traffic — catches that drift before customers do, rather than after.
Write the rollback plan down before the emergency
Deciding, in the moment, whether a spike in errors or latency justifies failing over to a second vendor is a worse decision than one made calmly in advance. Writing down the specific conditions that trigger a failover — a sustained rate of 429s, a latency threshold, a detected price or deprecation change on the primary vendor — turns an incident-time judgment call into a checklist someone can execute quickly under pressure.