Scaling AI Systems from 1M to 100M Users: Multi-Cloud Architecture, GPU Autoscaling, Massive RAG and Hyper-Scale Resilience

Q: How do you scale AI to 100 million users?

Key strategies: aggressive caching (semantic + response), model routing (simple queries → small models), edge inference for latency-sensitive endpoints, auto-scaling inference pods, request batching, and multi-region deployment with data locality.

Q: What is the architecture for hyper-scale AI?

Hyper-scale AI uses: global load balancing, multi-region model serving, tiered caching (L1 local, L2 distributed, L3 semantic), async processing for non-real-time tasks, and intelligent request routing based on complexity analysis.

Q: How do you manage AI costs at hyper-scale?

Cost management at scale: model cascade routing (cheapest model that can handle the request), aggressive semantic caching (60-80% hit rates), quantized models for edge, spot instances for batch processing, and cost-per-query budgets per feature.

Satyam Kumar

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Artificial Intelligence Architecture

Designing Hyper-Scale AI Systems for Performance, Cost, and Resilience (From 1 Million to 100 Million Users))

February 12, 20266 min read

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Satyam

AI & Cloud архитектор. Помогаю командам строить системы, масштабируемые до миллионов.

Designing Hyper-Scale AI Systems for Performance, Cost, and Resilience (From 1 Million to 100 Million Users))

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