Cost-Optimizing Serverless Architectures

Example

A photo-resize API running on Lambda + S3 reduced monthly costs dramatically by batching jobs, adding caching, and optimizing memory size—paying only for usage instead of running idle servers.

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• Grokking System Design Fundamentals
• Grokking the Coding Interview
• Mock Interviews with ex-FAANG engineers

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Why Is It Important

• Pay-per-use pricing means waste quickly adds up.
• Optimized design lowers invocation counts and execution time.
• Enables cost savings while still delivering scalable, resilient services.

Interview Tips

• Always mention key levers: invocation frequency, execution time, memory size.
• Talk about strategies: caching, batching, async queues, right storage tiers.
• Reference monitoring and budgets (FinOps), plus ARM/Graviton adoption.

Trade-offs (#tradeoffs)

• Pros: Auto-scaling, fine-grained billing, no idle costs.
• Cons: Cold starts, provider limits, reduced low-level control.
• Provisioned concurrency fixes latency but raises steady costs.

Pitfalls

• Forgetting data transfer fees.
• Over-provisioning memory/timeouts.
• Chatty micro-functions increasing invocation costs.
• Long DB connections in serverless functions.
• Skipping load tests, concurrency limits, and alarms.