Enterprise LLM cost control is usually framed as a model selection problem. Pick a cheaper model, negotiate better token pricing, or reduce usage. Those levers matter, but they miss the layer where most production waste accumulates: serving policy.
A Neo Cloud marketplace path can keep prompts, retrieval traces, and telemetry inside the enterprise boundary while still improving cost per useful answer. The key is to optimize the path between each request and the GPU.
Start with request classification
Not every prompt needs the same model, context window, latency target, or generation budget. Classify traffic by workflow type, risk, freshness, and user-facing latency before it reaches model execution.
Common classes include live assistant responses, internal knowledge retrieval, batch enrichment, coding automation, compliance review, and agentic tool use. Each class should have its own routing and serving policy.
Make caching a first-class serving decision
Semantic caching returns known answers before a GPU is touched. Prompt caching reuses long system, policy, retrieval, and context prefixes across similar requests.
Together, these controls reduce duplicate inference, stabilize latency, and make repeated enterprise workflows less expensive without changing the user-facing product experience.
Route by capability, not habit
Routing should send each request to the least expensive model that can satisfy the task, policy, and latency requirement. A frontier model can remain available for hard cases while smaller or specialized models handle routine work.
The routing policy should account for model quality, tool requirements, data sensitivity, cached context, queue depth, and the cost of retrying a poor answer.
Use batching and scheduling to improve GPU utilization
GPU waste often comes from idle gaps, incompatible request shapes, and latency policies that treat every task as urgent. Batching compatible requests and scheduling them by latency budget can raise occupancy while preserving service quality.
The goal is not maximum batching at any cost. The goal is to match queue behavior to business priority, response-time expectations, and hardware fit.
Key takeaways
- Serving policy is where private LLM economics become measurable.
- Caching should be evaluated beside routing, batching, quantization, and scheduling.
- The best cost metric is not raw token price. It is useful workflow output per dollar.