How to Reduce LLM Cost: 9 Practical Tactics That Work

Reducing LLM cost comes down to three levers: sending fewer tokens, paying less per token, and eliminating redundant or wasted calls. Most teams overspend not because a single request is expensive, but because inefficiencies compound across thousands of requests a day — bloated prompts, repeated context, no caching, and infrastructure overhead that never gets audited.

This guide walks through concrete tactics you can apply this week, roughly in order of effort-to-savings ratio. None of them require switching your entire stack or rewriting your product.

1. Trim your prompts before anything else

The fastest win is almost always prompt hygiene. Common offenders:

• System prompts that repeat instructions the model already infers from examples
• Full conversation history sent on every turn instead of a summarized version
• Boilerplate JSON schemas or formatting instructions duplicated across every call
• Debug/logging text accidentally left in production prompts

A 30% reduction in average prompt length translates almost linearly into a 30% reduction in input token cost. Run a token count on your top 5 most frequent prompts and see what's actually necessary versus habitual.

2. Cap and control output length

Unconstrained generation is a silent cost driver. If you're summarizing, extracting structured data, or classifying, set explicit max_tokens limits and instruct the model to be concise. A response that could be 400 tokens but runs to 900 because the model padded with explanation is pure waste — especially at scale.

3. Cache repeated context and responses

Two caching patterns matter:

• Prompt caching — if your system prompt or reference documents are static across requests, cache them so you're not re-sending (and re-billing) the same tokens every call.
• Response caching — for deterministic or near-deterministic queries (FAQ answers, common classifications), store the result and skip the model call entirely on repeat requests.

Even a naive in-memory cache keyed on a hash of the input can cut call volume by 20-40% for support bots and internal tools with repetitive traffic.

4. Route requests to the right model

Not every task needs your most capable model. Use a cheaper, faster model for classification, routing, extraction, and short-form tasks, and reserve the expensive model for reasoning-heavy or high-stakes generation. A simple router — even a rule-based one checking input length or task type — can shift 60-80% of volume to lower-cost tiers without touching quality where it matters.

function pickModel(task) {
  if (task.type === "classify" || task.type === "extract") {
    return "fast-tier-model";
  }
  return "reasoning-tier-model";
}

5. Batch and deduplicate requests

If your application fires off multiple small calls that could be combined into one — say, three separate extraction calls on the same document — merge them. Batching reduces the fixed overhead (repeated system prompts, repeated context) that gets paid on every single call.

Also check for accidental duplicate calls: retries without backoff, double-fired frontend events, or webhook handlers processing the same event twice are common silent cost leaks.

6. Stream and cancel early when you can

For interactive use cases, streaming lets you cancel generation as soon as you have enough output — useful for autocomplete, live search, or draft previews where the user often stops reading before the full response finishes. This doesn't reduce cost on providers that bill for tokens already generated, but it reduces perceived latency, which often lets you use a smaller model without users noticing.

7. Move structured tasks to tool use instead of free text

Asking a model to return JSON by describing the format in the prompt often produces retries and malformed output that you re-request. Using proper tool/function calling gets structured output more reliably on the first try, which reduces retry-driven token waste. See /docs/tools if you're setting this up against a Claude-based API.

8. Consolidate access instead of paying per-integration overhead

If your team has multiple people or services each running their own Claude subscription or ad-hoc API setup, you're often paying for redundant access and losing visibility into who's spending what. Centralizing access through a single API layer with per-key usage metadata makes it easy to see which application, feature, or team member is driving cost — which is usually the first step toward actually reducing it, since you can't optimize what you can't measure.

This is one of the reasons teams use SubToAPI: it turns your existing Claude access into application API keys (sub_live_...) with streaming, tool use, and usage metadata per key, so you can spot the expensive integration instead of guessing. Plans start at €9 for solo use, with team and scale tiers for shared seats — see /pricing. You can generate a key and test it against a real endpoint in a few minutes via /docs/quickstart.

9. Audit dead weight regularly

Set a recurring monthly check:

• Which prompts have grown longer over time without anyone noticing?
• Which features call the model on every page load instead of on demand?
• Are there abandoned test integrations still burning a key?
• Is retry logic capped, or can a single failure loop and rebill?

Cost creep is gradual — a prompt that gains 50 extra tokens per release doesn't look alarming until six months later it's 10x the original size.

Putting it together

The highest-leverage sequence is usually: trim prompts → cap outputs → cache what repeats → route by task → measure per-key usage to find the actual waste. Teams that do all five typically see cost drop by more than half without any noticeable change in output quality, because most of the savings come from eliminating waste, not degrading the model.

If you're working directly against a Claude-based API, /docs/messages and /docs/streaming cover the request patterns that make caching and output control straightforward to implement.

Questions

Does switching to a cheaper model always reduce cost without hurting quality? Not always — it depends on the task. Cheaper models work well for classification, extraction, and short-form generation, but reasoning-heavy or long-context tasks often need a stronger model or you'll pay more in retries and correction than you saved on the base rate.

Is prompt caching worth setting up for a low-traffic app? It's worth it if your prompts share large static blocks like system instructions or reference documents, even at low volume, since the savings scale with how much repeated content you avoid re-sending. For fully dynamic prompts with little repetition, the setup effort may not pay off.

How do I find out which part of my app is actually driving LLM cost? Track usage per API key or per feature instead of looking at a single aggregate bill. Splitting keys by service, team, or environment — which platforms like SubToAPI support natively — makes it obvious which integration is expensive instead of requiring manual log analysis.