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ML Model Optimization • Model Caching (Embedding Cache, Result Cache)Medium⏱️ ~2 min

Cost Savings and Observability: Measuring Cache Impact

QUANTIFYING CACHE VALUE
Cache value = (hit rate) × (cost per inference). If your model costs $0.01 per inference and cache achieves 80% hit rate on 1M daily queries, you save 800K × $0.01 = $8,000 daily. This calculation justifies cache infrastructure investment and guides capacity planning.
Include latency savings in ROI calculation. Cache hit at 2ms versus model inference at 200ms improves p50 latency by 198ms for the 80% of requests hitting cache. User experience improvement drives business metrics—conversion rate, engagement, retention. Faster responses compound into meaningful revenue impact.
ESSENTIAL CACHE METRICS
Hit rate: Percentage of requests served from cache. Track overall and by query segment. Popular queries should have higher hit rate than long-tail. If popular queries have low hit rate, cache sizing or eviction policy is wrong.
Miss penalty: Latency and cost of cache misses (full model inference). High miss penalty means cache value is high. Track p50, p95, p99 of miss latency. Target optimization at high-penalty query segments first.
Freshness metrics: Average cache age, max cache age, percentage of results served beyond target freshness. Staleness affects result quality. Monitor correlation between cache age and downstream metrics like click-through rate to find the right TTL.
CACHE OBSERVABILITY DASHBOARD
Build real-time dashboard showing: current hit rate trend, cache size and eviction rate, latency distribution (hits vs misses), cost savings accumulator, staleness distribution, error rate by cache layer. Set alert thresholds for each metric.
Debug capability: ability to check specific input against cache. Is it cached? What key? When cached? What result? This is essential for investigating user-reported issues and validating cache behavior after configuration changes.
CACHE OPTIMIZATION WORKFLOW
Regular optimization cycle: identify low-hit-rate query segments, analyze root cause (cache key too specific? TTL too short? eviction too aggressive?), adjust parameters, A/B test changes, measure impact on hit rate and downstream metrics. Iterate monthly.
💡 Key Insight: Track cache efficiency = (bytes served from cache) / (bytes stored). Low efficiency means storing entries that rarely get hit. Target: serve at least 10x the bytes you store to justify memory cost.

💡 Key Takeaways

✓Cache value = hit rate × cost per inference. Quantify savings in dollars.

✓Track hit rate overall and by segment to find optimization opportunities

✓Monitor freshness: cache age affects result quality and downstream metrics

✓Cache efficiency = bytes served / bytes stored. Target 10x+ to justify memory cost

📌 Interview Tips

1Interview Tip: Calculate concrete savings—80% hit rate on 1M queries at $0.01/inference = $8K daily. This justifies cache infrastructure cost.

2Interview Tip: Describe the optimization workflow—identify low-hit segments, diagnose root cause, test changes, measure downstream impact.

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