Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
/ quick answer
Measure cost per task first, then apply caching, smaller models and prompt trimming — each change validated against an eval set. A step-by-step method to reduce model spend without degrading output quality.
- 01Log tokens and cost per feature, not just the total bill.
- 02Build a 50–100 example eval set for the most expensive feature.
- 03Reorder prompts so stable content comes first and enable prompt caching.
- 04Add a semantic cache for repeated questions.
- 05Trim context: retrieve fewer, better chunks instead of stuffing documents.
- 06Try a smaller or non-reasoning model on the easy share of requests.
- 07Batch non-urgent jobs and set per-feature budgets with alerts.
- 08Re-run evals after every change and keep only the wins.
/ frequently asked
What does the Cut LLM Inference Costs workflow do?
Measure cost per task first, then apply caching, smaller models and prompt trimming — each change validated against an eval set.
What problem does Cut LLM Inference Costs solve?
AI features scale with usage, and the monthly model bill grows faster than revenue.
How many steps does Cut LLM Inference Costs take?
8 steps. It starts with log tokens and cost per feature, not just the total bill. and ends with re-run evals after every change and keep only the wins..
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Prompt Caching
Reusing the processed form of a repeated prompt prefix so later requests are cheaper and faster.
- →Semantic Cache
A cache that returns a stored LLM answer when a new question means the same thing as an earlier one, even if worded differently.
- →Knowledge Distillation
Training a small model to imitate the outputs of a large one, keeping most of the quality on a target task at a fraction of the cost.
- →Agent Cost Control
Agent cost control is the practice of budgeting tokens, steps and model tiers per task so autonomous systems stay economically viable at scale.
Related workflows
Turn this into a repeatable process.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
- →Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
- →Red Team an AI App
A repeatable process for attacking your own AI product before launch.
Related tool stacks
The tools that run it in production.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →Local AI Stack
Open-source tools to run chat, retrieval and agents entirely on your own hardware.
Related prompts
Reusable prompts for this job.
- →Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
Related use cases
How people apply it, and what came out.
- →SaaS Cuts AI Bill With Routing
Illustrative example: routing easy requests to a small model reduces spend while holding quality steady.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.