AI Token Cost Optimization: Cut Your LLM Bill

AI token cost optimization means reducing what you pay per AI request without lowering output quality. You pay by the token, so fewer tokens means lower cost.

A token is a small chunk of text, roughly a few characters. Every word you send and every word you get back has a price. Our guide to what AI tokens are covers the unit itself and how to read a per-token rate card.

This guide covers what drives token cost, the tactics that cut it, a worked before-and-after example, and how to measure the savings. It links to our ROI pillar so you can turn savings into a business case.

What Drives Token Cost

Token cost is driven by three things: how much text you send, how much you get back, and which model you use. Reduce any one and the bill drops.

Long prompts are the most common hidden cost. Teams paste in huge context "just in case," and pay for it on every single call.

Model choice matters just as much. The most powerful model can cost many times more per token than a smaller one that handles the task fine.

Tactics That Cut Cost

The best cost tactics remove waste and match each task to the cheapest model that can do it well. Most teams can combine several of these.

• Prompt trimming: cut boilerplate, repeated instructions, and unneeded context from every prompt.
• Caching: reuse answers for repeated or near-identical requests instead of paying again.
• Model tiering and routing: send easy tasks to a small cheap model and only hard tasks to a large one.
• Batching: group many small requests into fewer larger calls where the workflow allows it.
• Output caps: limit the maximum response length so the model cannot ramble on your dime.
• Retrieval instead of long context: fetch only the few relevant passages rather than stuffing whole documents into every prompt.

Model Tiering and Routing in Practice

Model routing sends each request to the cheapest model that can handle it well. It is the single biggest lever for most teams.

The idea is simple. Classify the task, then pick the model. A quick classification lookup costs far less than the answer it routes.

• Use a small model for classification, extraction, and simple replies.
• Reserve a large model for reasoning, nuance, and high-stakes answers.
• Add a fast rule or classifier to decide the tier before the main call.
• Review the split monthly and move tasks down a tier where quality holds.

Running the Model Yourself Removes the Per-Token Bill

Routing sends work to a cheaper model. Local inference goes one rung further and removes the per-token charge for the work it can handle. The model runs on hardware you own, so no request counts against a rate card.

Hardware and the power to run it replace the bill. A local setup needs a Linux PC with an NVIDIA RTX GPU carrying at least 24GB of VRAM, and 32GB is the cited recommendation. An NVIDIA DGX Spark is the packaged alternative, listed at $4,699. Verify current pricing on the DGX Spark product page before you budget against that figure. Electricity then runs for as long as the machine does.

A packaged product can add a subscription on top. Perplexity announced Portable Computer on 25 August 2026, a local-first build of its Perplexity Computer agent made with NVIDIA. It is not a separate purchase. It unlocks with a Pro, Max, Enterprise Pro or Enterprise Max subscription, and the current rate for each tier sits on Perplexity's pricing page.

The trade-off is capability. Models small enough to run on one machine trail frontier models on hard reasoning, and Perplexity has published figures that size the gap. On Terminal Bench 2.1, a coding benchmark, Portable Computer scored 59.6% running locally. The same setup scored 73.0% when it escalated to a cloud advisor model, at roughly $0.415 per task. Perplexity reported those numbers itself, so read them as vendor-reported figures rather than independent testing.

So local hardware absorbs a share of the queue, and the hard cases still escalate to a paid model. That lowers the bill without removing it. Three kinds of workload justify the hardware:

• High-volume repetitive calls: classification, extraction, and tagging that run the same shape thousands of times a day.
• Work on data that cannot leave the building, such as client files, medical records, or documents under a confidentiality clause.
• Workloads that run constantly enough for the hardware to pay back. A GPU idle five days a week costs more than the API calls it replaced.

A Worked Before-and-After Example

A simple redesign can cut a workflow's token cost by more than half. Here is an illustrative example for a support-reply assistant.

Before: every request pastes the full knowledge base into the prompt and always uses the largest model. Prompts are long and expensive.

After: retrieval fetches only the three relevant passages, a small model handles routine questions, and output length is capped. The prompt shrinks and cheaper models do most of the work.

• Before: ~8,000 input tokens per request, large model on every call.
• After: ~1,200 input tokens per request, small model on the majority of calls.
• Result in this example: roughly a 50 to 70 percent drop in cost per request, with quality checked on an eval set.

Measuring the Savings

Measure cost per request before and after each change, and check quality did not drop. Savings you cannot measure are savings you cannot defend.

Track a few numbers over time. Compare them week to week so you can prove a change worked.

• Cost per request: the core number to drive down.
• Input and output tokens per request: shows where the waste is.
• Model mix: the share of calls going to cheap versus expensive models.
• Quality score: confirms cost cuts did not hurt output.

Turning Savings into ROI

Token savings become real ROI when you multiply the per-request cut by your monthly volume. A small saving per call adds up fast at scale.

Frame the result in dollars per month, not tokens. That is the language leadership uses to approve more AI work.

Pair the savings with the value the workflow creates. Our ROI pillar helps you build that full business case.