Akshay Kale

OpenAI

key ideas

• ▸LLMs are served through distributed inference clusters
• ▸Requests are batched to maximize GPU utilization
• ▸Scheduling layers prioritize latency-sensitive workloads
• ▸Horizontal scaling handles traffic spikes

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These patterns apply to any compute-heavy backend where batching and scheduling improve efficiency — not just AI.

Stripe

key ideas

• ▸APIs must be safe to retry in distributed systems
• ▸Clients send an idempotency key with each request
• ▸The backend stores the request and response for that key
• ▸Retries return the cached response instead of re-executing

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This pattern prevents duplicate operations like payments or order creation — critical for any financial system.

Netflix

key ideas

• ▸Microservices communicate through resilient service meshes
• ▸Circuit breakers prevent cascading failures
• ▸Chaos engineering validates system reliability
• ▸Services degrade gracefully instead of failing completely

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Fault tolerance is essential when operating globally distributed systems — design for failure, not against it.

Uber

key ideas

• ▸Real-time location streams processed via distributed event pipelines
• ▸Kafka handles high-throughput event ingestion
• ▸Dispatch decisions rely on low-latency geospatial computations
• ▸Systems balance throughput with real-time responsiveness

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Event-driven architecture is essential for real-time systems where milliseconds determine user experience.

Cloudflare

key ideas

• ▸Edge networks run workloads close to the user
• ▸Global request routing improves latency and reliability
• ▸Stateless services allow horizontal scaling
• ▸Edge workers enable lightweight serverless compute

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Moving computation closer to users drastically reduces latency — a core principle of modern distributed systems.

Google

key ideas

• ▸Slow requests at the tail significantly impact overall user experience
• ▸Even rare slow responses can delay an entire request chain
• ▸Request hedging and replication reduce tail latency
• ▸Optimize for worst-case latency, not just average

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At scale, reducing tail latency is critical to consistency. A p99 problem affects millions of users daily.