Large system prompts, tool schemas and repeated document context can make every LLM request more expensive. Context caching can help, but only when the same stable prefix is reused often enough.

Cache stable prefixes

Policies, tool definitions and static product instructions are better candidates than user-specific conversation history. Measure effective hit rate. A cache feature is useful only if requests actually reuse the cached segment. Track eligible tokens, cache hits, savings and latency separately.

Plan invalidation

Policy or tool changes should create a new cache version. Reusing stale instructions can be more damaging than paying for fresh context. Respect tenant boundaries.

Never allow private user or enterprise context to become a cross-tenant cache artifact. Caching must preserve the same isolation guarantees as the underlying application. Compare caching with routing. Sometimes a smaller model or better prompt architecture saves more than caching. Evaluate all three using cost per successful task.

Separate interactive and elastic capacity

Real-time traffic benefits from warm, predictable capacity, while background jobs can often use preemptible or delayed resources. Keeping at least some scheduling separation prevents batch work from consuming the headroom needed for user-facing requests. The boundary does not have to be fixed. Capacity can move between pools based on demand, but the transfer policy should preserve latency targets before maximizing utilization. Use queue signals before GPU saturation becomes visible. Queue age, pending tokens and admission delay often reveal overload earlier than raw GPU utilization. A device can appear busy but healthy, or moderately utilized while a long queue forms because request shapes are mismatched. Control loops should therefore use several signals and apply hysteresis so autoscaling or rejection policies do not oscillate during bursty traffic.

Plan for model and request heterogeneity. Different models, context lengths and generation limits consume very different amounts of memory and time. Schedulers should account for request shape instead of treating every job as one unit of work. Model routing and batching policies can improve efficiency, but they should preserve quality and latency requirements explicitly rather than using the cheapest path by default. Evaluate cost under realistic concurrency. Benchmarks with one request at a time do not reveal production behavior. Test burst traffic, mixed prompt lengths, long generations, model swaps and failure recovery. Cost and latency should be measured under the same concurrency pattern expected from real users.

Make capacity policy explicit

Infrastructure becomes easier to operate when the system can explain how work is prioritized. Define which requests may queue, which receive reserved capacity, which can route to alternate models and which should be rejected during overload. Explicit policy also improves multi-tenant fairness because large customers cannot accidentally consume all available headroom without a deliberate quota or scheduling decision. Optimize the critical path, not a single component. Inference latency includes queueing, model execution, transfer, token streaming and downstream processing. A faster kernel may have little user impact if requests spend most of their time waiting for admission or model loading. End-to-end tracing should identify where tail latency originates so engineering effort follows actual bottlenecks.

Use graceful degradation before catastrophic overload. When demand exceeds capacity, delay background work, disable optional enrichment, route eligible traffic to smaller models or reject before queues become unstable. Controlled degradation protects most users and shortens recovery after a spike.

Queue age and pending tokens can reveal overload earlier than raw GPU utilization.

Model economics under realistic concurrency

Warm pools, batching and routing all trade cost for latency or quality. Measure cost per successful request by traffic class under bursty, production-like concurrency rather than relying on single-request benchmarks. The best configuration is the one that satisfies product SLOs at sustainable cost, not simply the one with the highest utilization.

Schedule according to request shape. Prompt length, expected output and model size create very different memory and compute demand. Treating every request as one unit can produce unfair queues and poor batching.

Shape-aware scheduling and per-tenant limits make capacity more predictable under mixed workloads.

Test failure and recovery of the infrastructure layer

Model servers, caches and routing components will fail independently. Exercise model-load failure, regional loss, stale cache and capacity exhaustion so fallback paths are known before incidents. Reliability should be measured together with latency because frequent retries can hide behind acceptable average response times.

Bottom line. Prompt caching works best for large, stable and frequently reused context. Treat it as an economic optimization with explicit invalidation and privacy rules.