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Design a multi-region inference deployment: capacity per region, routing, failover, and getting the weights everywhere.

Remove the largest region from a concrete capacity plan. Check the surviving budget, tenant eligibility, cold prefixes and retry semantics before claiming the service can fail over.

Updated Sep 2026 · Learn how AI infrastructure works through explanations, worked examples and diagrams, then practise applying it to interview questions.

Remove the largest region from a concrete capacity plan. Check the surviving budget, tenant eligibility, cold prefixes and retry semantics before claiming the service can fail over.

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The concepts behind this question

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📐 AI Systems Design

Multi-Region Serving and FailoverMulti-region serving can improve geographic reach and survive a regional outage. It requires enough remaining capacity and a clear plan for state, routing and degraded service.

🚀 Inference & Serving🔒 Premium

Reasoning Budgets and Answer LatencyReasoning tokens consume generation time and retained state before an answer becomes usable. Separate the first generated token from the first answer token, reserve output capacity, and evaluate reasoning limits against task success.

📐 AI Systems Design🔒 Premium

Request Routing and Load Balancing for LLMsAn LLM router chooses a compatible replica, weighing queue time against reusable prompt state. Cached work helps only when the extra wait does not erase the saving.

📐 AI Systems Design🔒 Premium

Model Routing and CascadesChoose a model against measured task quality, latency and cost. Distinguish routing before generation from cascades that pay for a first answer before escalating.

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UP NEXT ON YOUR JOURNEY

Next in this trackDesign the playground behind an LLM API: streaming, sessions, quotas and abuse, for a million free-tier users.Next in this trackDesign the checkpoint store for a lab running several large training jobs: write bursts, retention, resharding and lineage.Next in this trackDesign observability for an inference fleet: per-request spans, GPU metrics, and cost attribution per tenant.

FEDITOR'S NOTE

Scored on explicit capacity assumptions, correct state and identity boundaries, failure handling and a justified design choice.

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