Reduce LLM inference cost systematically: measuring cost per outcome, context reduction, caching, model routing, output limits, and the trade-offs each involves.
LLM inference cost optimization usually starts after a bill arrives that nobody predicted. The cost drivers are consistent across systems, and most can be addressed without reducing quality, sometimes while improving it.
Begin by measuring, because intuition about where tokens go is reliably wrong.
Measure cost per outcome
Track cost per completed business outcome, not per API call. A workflow averaging four calls but occasionally requiring sixty has an economics problem that per-call averages conceal.
Attribute cost by feature, workflow, and tenant. Without attribution you cannot tell which part of the product is unprofitable, and blanket reductions damage features that were fine.
Log input and output tokens per call alongside the outcome. Retries and failed attempts belong in the total, since they are frequently excluded from estimates and often significant.
Context is usually the largest lever
Input tokens dominate cost in most retrieval-based systems, and much of that input contributes nothing.
Retrieve broadly then rerank and cut hard. Sending three well-ranked passages instead of twenty typically reduces cost substantially and improves answer quality, because distractors degrade reasoning.
Compact conversation history into structured facts rather than carrying raw turns. Long sessions otherwise grow linearly in cost while adding little.
Trim system prompts. Instructions accumulate over time as edge cases are patched, and they are paid for on every request. Audit them periodically against evaluation results.
Caching
Prompt caching, where a provider offers it, cuts the cost of repeated prefix content substantially. Structure prompts so stable content comes first and variable content last, and avoid reordering stable sections between calls, which defeats caching.
Cache complete responses for identical inputs where the task is deterministic enough to allow it, such as classification of repeated content.
Cache retrieval results and embeddings. Re-embedding unchanged documents is pure waste and is common in poorly instrumented pipelines.
Model routing
Not every request needs the strongest model. Route classification, routing, extraction, and formatting to smaller models, reserving larger ones for genuine reasoning.
Cascade where quality matters: a small model answers, and a larger one reviews only the uncertain or high-stakes cases.
Measure the routing distribution. A router that sends most traffic to the expensive model is not delivering its intended saving, and this is a common outcome when confidence thresholds are set conservatively and never revisited.
Output control
Output tokens usually cost more per token than input. Constrain generation length explicitly rather than relying on instructions, which models follow inconsistently.
Request structured output where you will parse it anyway, since it is more compact than prose and easier to validate.
Avoid asking for extended reasoning in the response when you only need the conclusion, unless the reasoning demonstrably improves accuracy on your evaluation set.
Architectural choices
Batch where latency permits, since batched throughput is considerably more efficient.
Set hard budgets per task with safe termination, which prevents the runaway loops that produce the worst cost incidents.
Consider self-hosting only at steady high volume, where utilization justifies it. At variable or modest volume, managed endpoints are typically cheaper once operating cost is counted honestly.
Frequently asked questions
What reduction is realistic?
Systems that have never been optimized commonly see substantial reductions from context trimming, caching, and routing alone, often without quality loss.
Does cutting context hurt quality?
Cutting irrelevant context usually improves it. Cutting relevant context hurts, which is why reranking matters more than raw retrieval volume.
Should we fine-tune to reduce cost?
Sometimes, where a smaller fine-tuned model replaces a larger prompted one on a narrow task. Weigh the ongoing maintenance and re-training burden.
What is the first thing to check?
Cost attribution by feature, followed by the size of the context being sent on your highest-volume path.
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Written by
Moueen Togarvi
Founder & CEO at Voquarn Code, focused on product engineering, search growth, and practical AI systems.