Cut LLM API costs by attacking tokens in five steps: measure spend by input versus output and by feature, enable prompt caching so repeated context bills at the lower cached rate, compress prompts and retrieve only needed context to shrink input tokens, cap and shape output since output tokens cost more, and route simple requests to cheaper models. Most of the saving comes from sending fewer tokens, not from switching providers. Test each change against a quality benchmark so you keep only the reductions that hold quality steady. These moves routinely cut a token bill meaningfully without degrading the product.
Last updated: June 2026
LLM API cost is token cost: you pay per input and output token, so the bill is driven by how many tokens each call sends and returns. Caching reuses a stable portion of the prompt at a reduced rate, and prompt compression reduces the tokens you send in the first place. Together with output limits and model routing, they target the four levers that actually move a token bill. The premise is simple: most LLM spend is tokens that added no value, and removing them lowers cost without lowering quality, as long as you measure each change.
This article is part of our AI, GPU and ML cluster. For the full picture start with the complete guide to AI and GPU cost optimization, the pillar this piece links up to. If you are still deciding whether an API is the right home for the workload at all, read how to choose between self-hosted and API LLMs on cost.
Step 1: How do I measure token cost by call type?
Break your LLM spend down by input versus output tokens and by feature before changing anything, because you cannot cut what you cannot see. Pull token counts per call type from your provider's usage data or your own instrumentation, and attribute them to the features that generate them. This shows where the tokens and the dollars actually go, which is almost never where teams assume. Output-heavy features and any feature that resends large context on every call are the usual offenders. This measurement is the See step and the baseline you will judge every later change against.
Step 2: How does prompt caching lower my bill?
Prompt caching stores a stable prefix of the prompt, such as a long system prompt, instructions, or shared document context, so on repeat calls those tokens bill at a reduced cached rate instead of full input price. For applications that resend the same large context on every request, this is often the single biggest saving, because the expensive prefix is paid for once and reused across many calls. Structure prompts so the stable part comes first and the variable part last, which maximizes what can be cached. Caching changes nothing about the output, so it is the safest cut to make first.
Step 3: How do I compress prompts and trim context?
Prompt compression reduces input tokens without losing the information the model needs. Remove redundant instructions and boilerplate, summarize long conversation history instead of resending it verbatim, and for retrieval-augmented features fetch only the passages a request actually needs rather than stuffing the whole context window. Every input token you avoid is billed at zero. For retrieval-augmented generation specifically, the retrieval layer itself has a cost, which is why this pairs with how to reduce vector database costs for RAG workloads. Compress aggressively but test against quality, since over-trimming context can degrade answers.
LLM API bill rising faster than usage justifies?
Our cost audit profiles your token spend by feature, applies caching, compression, output limits, and model routing, and measures the saving against a quality benchmark so the bill drops without the product getting worse. On the performance model, you pay only from realized savings. No savings, no fee.
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Cap and shape output because output tokens usually cost more than input tokens, often several times more, which makes them the highest-leverage line to control. Set a maximum output length appropriate to each feature, request concise formats such as structured fields instead of prose, and stop generation as soon as the needed answer is produced rather than letting the model run on. Much output is generated and then truncated or ignored by the application, which is pure waste you are paying premium rates for. Shaping output is frequently the fastest large saving after caching.
Step 5: How do I route requests to the cheapest capable model?
Send each request to the smallest model that can handle it, and reserve the largest, most expensive model for the tasks that genuinely need it. Most applications run a mix of easy and hard requests, yet route everything to one premium model by default. Classify requests by difficulty and route simple ones, classification, extraction, short answers, to a smaller or cheaper model, escalating to the strong model only when needed. This preserves quality where it matters while cutting cost on the bulk of traffic. Routing decisions and their savings belong in your AI cost scope, covered in how to build an AI cost FinOps scope for 2026.
The AI and GPU Cost Control Guide includes our LLM token-reduction checklist with the caching, compression, and routing playbook. It is the downloadable companion to this article.
Frequently asked questions
How does prompt caching cut LLM API costs?
Prompt caching stores a stable portion of the prompt, such as a long system prompt or shared context, so on repeat calls those tokens are billed at a reduced cached rate instead of full input price. For applications that resend the same large context on every request, caching can cut input token cost substantially because the expensive prefix is paid for once and reused.
What is prompt compression?
Prompt compression is the practice of reducing the number of input tokens a request sends without losing the information the model needs. It includes removing redundant instructions, summarizing long conversation history, and retrieving only the relevant context rather than stuffing everything in. Fewer input tokens means a lower bill on every call, since you pay per token.
Do output tokens cost more than input tokens?
Yes, on most LLM APIs output tokens are priced higher than input tokens, often several times higher. That makes capping and shaping output one of the highest-leverage cost moves: setting a max output length, requesting concise formats, and stopping generation early all directly reduce the most expensive part of the bill.
Will cutting costs hurt response quality?
Not if you measure it. Caching changes nothing about the output. Compression and output caps can affect quality if taken too far, so you test each change against a quality benchmark and keep only the reductions that hold quality steady. Model routing preserves quality by sending hard tasks to the strong model and only easy ones to the cheap model.
The short version
Cut LLM API costs by measuring token spend by feature, caching repeated context, compressing prompts, capping output, and routing simple requests to cheaper models. The saving comes from sending fewer tokens, not from switching providers, and each change should be tested against a quality benchmark. When you want that token-reduction work done and the saving proven against quality, that is exactly what our FinOps implementation service delivers.
Written by Morten Andersen and reviewed by Fredrik Filipsson, applying the See, Cut, Lock, Run method. Independent and vendor neutral.
Cloud pricing and service behavior change frequently. Verify the specifics in this guide against the providers’ own current documentation and the FinOps Foundation: FinOps Foundation Framework ↗ and FinOps Rate Optimization capability ↗. This article also reflects Cloud Cost Room’s hands-on, vendor-neutral engagement experience.