Caching Strategies for ML Systems
Designing multi-tier caching architectures to reduce inference latency, DB load, and compute costs across ML applications.
Caching in ML Systems intercepts redundant compute and data requests across every tier of the machine learning lifecycle. Caching spans Feature Store Caching (low-latency Redis key-value feature lookups), Model Prediction Caching (caching exact/semantic model outputs for frequent inputs), Prompt Caching (reusing pre-computed KV Cache tensors for static LLM prefixes), and Embedding Caching. Effective caching requires selecting optimal eviction policies (LRU, LFU, TTL) and invalidation triggers to prevent stale data.
The 4-Tier ML Caching Architecture
USER REQUEST / INFERENCE QUERY
│
┌────────────────────────────────┼────────────────────────────────┐
▼ ▼ ▼
[ PREDICTION CACHE ] [ FEATURE STORE CACHE ] [ PROMPT / EMBEDDING CACHE ]
- Exact Match (Hash) - Low-latency Redis - Vector Embedding Cache
- Semantic Match (CosSim) - Online User Features - LLM Prompt KV Cache
- Sub-5ms response - Sub-10ms feature lookup - Sub-50ms TTFT latency
Caching Strategy Matrix
| Cache Tier | Cached Asset | Primary Benefit | Eviction / Invalidation Policy |
|---|---|---|---|
| Prediction Cache (Exact) | Hash(input) -> Model Output | Eliminates 100% of model compute for duplicate requests | TTL (24h) + Manual API invalidation |
| Semantic Cache | Vector(prompt) -> Response | Recognizes paraphrased user queries | Similarity threshold ($\tau \ge 0.95$) + TTL |
| Feature Store Online Cache | user_id -> Real-time Features | Low-latency feature fetching for real-time GBDT/RecSys | Event-driven CDC (Change Data Capture) / TTL |
| Prompt KV Cache | Prefix Tokens -> K, V Tensors | Skips heavy $O(N^2)$ LLM prefill phase | LRU Radix Tree page eviction |
| Embedding Cache | Text String -> Dense Vector | Avoids re-embedding frequent queries in RAG | Long TTL / Immutable store |
Cache Invalidation Anti-Patterns
- Unbounded TTL for Dynamic Data: Caching user credit scores or fraud risk for 30 days. Fix: Use short TTLs (5m) or pub-sub cache invalidation on feature updates.
- Missing Metadata Scoping: Caching responses globally across multi-tenant applications. Fix: Scope cache keys by
(tenant_id, user_role, input_hash).
Say this out loud
"Caching in ML systems spans Prediction Caching (Exact and Semantic), Feature Store Caching (Redis), Embedding Caching, and LLM Prompt KV Caching. Caching reduces end-to-end latency from seconds to sub-10ms and slashes GPU compute costs. We use TTLs, LRU eviction, and event-driven invalidation to prevent stale prediction risks."
Follow-ups to expect
- What is Cache Stampede (Thundering Herd)? When a popular cached key expires, thousands of concurrent user requests hit the un-cached backend simultaneously, crashing GPUs. Mitigate using Mutex Locks (single-flight execution) or probabilistic early expiration.
- How do you measure Cache Efficiency in ML serving? Track Cache Hit Ratio ($\frac{\text{Hits}}{\text{Hits} + \text{Misses}}$), Average Latency Reduction ($\Delta t$), and Total Dollar Savings from bypassed GPU compute.
Check yourself
Question 1 of 3
Where can caching be applied in an end-to-end Machine Learning inference system to reduce end-to-end latency?