How would you scale AI systems from 10k to 10M users?

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### Signal to interviewer

I can scale AI platforms with phased architecture and operational discipline instead of reactive firefighting.

### Clarify

I would clarify growth forecast, regional mix, workload complexity, and non-functional SLO requirements.

### Approach

Use a stage-gate blueprint: capacity milestones, reliability controls, cost optimization checkpoints, and support scalability planning.

### Metrics & instrumentation

Primary metric: successful request volume at SLO during peak conditions. Secondary metrics: autoscale efficiency, support-to-user ratio, and onboarding throughput quality. Guardrails: cost blowout, severe incident frequency, and latency degradation under burst.

### Tradeoffs

Early heavy investment improves resilience but can slow feature delivery. Lean infrastructure accelerates growth but risks instability at inflection points.

### Risks & mitigations

Risk: sudden traffic spikes overwhelm services; mitigate with queue buffering and admission control. Risk: rising unit economics; mitigate with route optimization. Risk: support bottlenecks; mitigate with in-product recovery and tooling.

### Example

A consumer writing app scales by splitting free versus paid traffic classes, adding regional replicas, and introducing adaptive model routing as demand grows.

### 90-second version

Scale AI systems with stage-gated infrastructure and product operations. Align each growth step with SLO, cost, and incident controls so expansion remains stable and sustainable.

FOLLOW-UPS

Clarification

Which SLO must remain stable as user count scales most aggressively?
What growth milestone should trigger the next infrastructure stage?

Depth

How would you design traffic segmentation for paid versus free users?
What forecasting and load-testing loop supports proactive capacity planning?

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