How I Slashed AI API Costs 60% as a Cloud Architect

Short summary

A cloud architect details how they reduced LLM API costs by 60% across three regions using model tiering (premium/mid-range/budget models) and regional routing. They shifted 70% of traffic from GPT-4o to DeepSeek V4 Pro while maintaining p99 latency under 2 seconds. The approach uses caching (40% hit rate), connection pooling, and selective model selection based on query complexity.

• •Implement tiered model strategy: GPT-4o (10% traffic), DeepSeek V4 Pro (bulk), budget models (classification/routing)
• •Reduced $80k/month to $31k/month on document summarization by routing 70% traffic to cheaper models
• •Latency optimization: connection pooling, streaming, regional failover, and semantic caching (40% hit rate) for deterministic prompts

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