Optimizing Large Language Model Inference Costs for Enterprise Deployments

Measure before you optimise

Enterprise LLM programmes rarely fail on capability; they fail on unit economics discovered after launch. The first step is a cost model expressed per unit of business value: cost per resolved support ticket, per document summarised, per query answered. That framing exposes which workloads deserve engineering attention and which are already cheap enough to leave alone.

Instrument input and output token counts, latency percentiles and cache hit rates per feature and per tenant. Most organisations find that a small number of prompt templates account for the majority of spend, usually because of oversized context windows rather than request volume.

The highest-leverage levers

Output tokens generally dominate cost, and context length dominates latency and memory. Optimisation therefore starts with the prompt and the retrieval strategy before it touches infrastructure.

• Model routing: send easy requests to a small model and escalate only on low confidence.
• Prompt compression: trim boilerplate, deduplicate retrieved chunks, cap context by relevance score.
• Caching: exact-match and semantic caching for repeated queries; prefix and KV cache reuse for shared system prompts.
• Quantisation and distillation: serve int8 or 4-bit weights, or a distilled task model, where evaluation shows parity.
• Continuous batching and paged attention to raise GPU utilisation on self-hosted serving.
• Structured outputs and token limits to stop verbose generations you never use.

Self-hosting versus managed endpoints

Managed endpoints win at low and spiky volume because you pay nothing for idle capacity. Self-hosting wins once sustained utilisation is high enough to amortise accelerator cost, and it becomes attractive earlier when data residency or latency requirements are strict. The break-even is a utilisation calculation, not an ideological one, and it should be revisited quarterly as prices move.

A hybrid pattern is common and effective: self-host the small workhorse model that handles most traffic, and burst to a managed frontier model for the minority of requests that need it.

Guarding quality while cutting cost

Every optimisation is a potential regression, so each one needs an evaluation gate. Maintain a golden dataset with task-specific metrics, run candidate configurations against it, and require explicit sign-off on any quality delta. Pair that with online guardrails — sampled human review, refusal and hallucination rates, user-level satisfaction signals — so that silent degradation surfaces quickly.

The disciplined version of this work typically removes a large share of spend with no measurable quality loss, because most of the savings come from waste rather than from capability reduction.