ML Model Optimization • Model Caching (Embedding Cache, Result Cache)Medium⏱️ ~2 min
Cost Savings and Observability: Measuring Cache Impact
Caching is a cost and latency optimization. Quantifying the impact guides tuning decisions and justifies infrastructure investment. The metrics split into performance and financial categories.
Performance metrics start with hit ratio by tier. Track exact cache hits, semantic cache hits, and overall misses separately. A well tuned system serving enterprise workflows might see 25 percent exact hits, 18 percent semantic hits, and 57 percent misses, for a 43 percent total hit rate. Watch hit ratio trends. A declining trend signals cache size is too small, Time To Live (TTL) is too short, or traffic patterns shifted. Next measure latency savings. An exact cache hit at 2 milliseconds versus 1.8 seconds for full model inference saves 1,798 milliseconds. Semantic cache at 15 milliseconds saves 1,785 milliseconds. Compute weighted average savings across all hits. For the example above, 43 percent of requests save roughly 1.79 seconds, yielding an overall p95 latency reduction of 30 to 40 percent.
Financial metrics quantify cost avoidance. If your model costs 5 dollars per million tokens and average response is 100 tokens, each avoided call saves 0.0005 dollars. At 10 million requests per month with 43 percent hit rate, you avoid 4.3 million model calls, saving 2,150 dollars monthly in API costs. For self hosted models, measure GPU hours saved. A model requiring 2 A100 GPUs at 3 dollars per GPU hour costs 6 dollars per hour. If caching reduces load by 40 percent, you can scale from 10 to 6 GPUs, saving 4 GPUs times 3 dollars times 730 hours, roughly 8,760 dollars monthly.
Observability requires tracking false positive rates, cache memory occupancy, eviction rates, and stampede events. False positives are measured by running a verifier model on semantic cache hits and counting disagreements. Target under 2 to 5 percent. Eviction storms indicate undersized cache or poor eviction policy. Stampedes show up as request queue depth spikes correlated with cache misses. Use dashboards that correlate cache metrics with downstream model latency and cost to close the feedback loop.
💡 Key Takeaways
•Hit ratio by tier distinguishes exact cache (20 to 30 percent typical), semantic cache (10 to 25 percent additional), and misses. Total hit rates of 40 to 50 percent are achievable in closed domain enterprise systems with proper tuning.
•Latency savings per hit quantify user experience impact. Exact cache at 2ms versus 1.8 second model call saves 1,798ms. With 43 percent hit rate, overall p95 latency drops 30 to 40 percent.
•Cost savings depend on pricing model. API based systems save per token costs. At 5 dollars per million tokens, 100 token responses, and 10M requests monthly, a 40 percent hit rate saves $2,000 monthly in API fees.
•Self hosted GPU cost savings scale with cache hit rate. A 40 percent cache hit rate reducing load from 10 to 6 A100 GPUs at 3 dollars per GPU hour saves roughly $8,760 monthly in compute costs.
•False positive rates measured by verifier disagreements should stay under 2 to 5 percent. Higher rates indicate similarity thresholds are too loose or metadata alignment is insufficient.
•Cache memory occupancy and eviction rates indicate sizing. Frequent evictions of recently added entries suggest cache is undersized. Use frequency based or segmented Least Recently Used (LRU) policies to prioritize hot content.
📌 Examples
Netflix tracks cache hit rates across personalization and recommendation systems. A 35 percent result cache hit rate combined with embedding caching reduces p99 serving latency from 450ms to 180ms and cuts GPU costs by $120K monthly across the recommendation stack.
An enterprise RAG system serving 8 million queries monthly with average 120 token responses at 10 dollars per million tokens sees 38 percent combined cache hit rate. Monthly savings: 0.0012 dollars per response times 3.04 million cached responses equals $3,648 in API cost avoidance.
A support chatbot monitors false positive rates by running a small BERT verifier on 5 percent of semantic cache hits. When false positive rate climbs from 3 to 9 percent after a threshold change from 0.85 to 0.75, they revert and add product category metadata to improve precision.