How to Reduce LLM API Costs at Scale

When you're sending thousands or millions of requests a day, small inefficiencies in how you call an LLM turn into real money fast. Reducing LLM API costs at scale isn't about finding a magic cheaper provider — it's about cutting wasted tokens, avoiding redundant calls, and matching the right model to the right task.

This article covers the techniques that actually move the needle: prompt engineering for token efficiency, caching, batching, model tiering, and visibility into where your spend is actually going. Most teams can cut 30-60% off their LLM bill without touching output quality, just by fixing how requests are constructed and routed.

Start by Measuring Where the Money Goes

You can't optimize what you can't see. Before changing anything, break down your spend by:

• Endpoint or feature — which part of your product drives the most calls?
• Input vs output tokens — output tokens are typically priced higher; long generations cost more than long prompts.
• Model tier — are you using your most expensive model for tasks a cheaper one could handle?
• Retry and error rate — failed requests that get retried silently double their cost.

If you're using a gateway like SubToAPI, this is easier because every request carries usage metadata (input tokens, output tokens, model, latency) in the response, and the dashboard aggregates it per API key. That means you can attribute cost to a specific feature or even a specific customer if you issue per-team keys. See /docs for details on what's returned with each response.

Cut Input Tokens With Better Prompts

The fastest win is almost always trimming what you send, not what the model generates.

• Remove boilerplate instructions you don't need for every call. If a system prompt is 800 tokens and only 200 of those actually change model behavior, cut the rest.
• Stop re-sending full conversation history when you only need the last few turns. Summarize older turns instead of replaying them verbatim.
• Avoid few-shot examples you don't need. Test with zero-shot first; only add examples if output quality actually requires them, and keep them short.
• Strip unnecessary context from RAG pipelines. Retrieved chunks are often 2-3x larger than what the model needs — trim to the relevant sentences, not whole documents.

A quick audit: log the token count of every prompt for a week, then look at the top 10% by size. Those are usually the ones with the most waste.

Control Output Length Explicitly

Output tokens cost more than input tokens on most models, and unconstrained generations run long by default.

• Set max_tokens deliberately instead of leaving it at a high default.
• Ask for structured output (JSON, bullet points) when you need structured data — it's shorter and easier to parse than prose.
• For summarization or extraction tasks, specify a target length ("in 2-3 sentences," "under 100 words") rather than letting the model decide.

This one change — capping and directing output length — often has a bigger cost impact than input trimming, especially for chat and summarization features.

Route Requests to the Right Model

Not every request needs your most capable (and most expensive) model. A common pattern:

1. Classification, extraction, simple Q&A → smaller/faster model
2. Multi-step reasoning, code generation, complex analysis → larger model
3. Escalation path: try the cheap model first, and only fall back to the expensive one if confidence is low or the response fails validation

This tiering alone can cut costs by 40-70% for products where most requests are simple but a minority genuinely need heavy reasoning.

async function routeRequest(prompt, complexity) {
  const model = complexity === "simple"
    ? "claude-haiku"
    : "claude-sonnet";

  const res = await fetch("https://api.subtoapi.app/v1/messages", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model,
      max_tokens: 300,
      messages: [{ role: "user", content: prompt }]
    })
  });

  return res.json();
}

Since SubToAPI exposes application API keys (sub_live_...) per app rather than per developer, you can route different features to different keys and see cost per feature directly in the dashboard — useful when deciding which parts of your product justify the more expensive model. Check /pricing for how seats and usage map to plan tiers.

Cache Aggressively

Caching is the highest-leverage cost reducer for any product with repeated or predictable queries.

• Exact-match caching: if the same prompt is sent repeatedly (FAQ bots, common queries), cache the response and skip the API call entirely.
• Semantic caching: for near-duplicate queries, use embeddings to detect similarity and reuse a cached answer instead of a fresh call.
• Static system prompts: if your system prompt and tool definitions don't change between requests, some providers support prompt caching that discounts repeated prefix tokens — check your provider's docs for this feature.

Even a 20% cache hit rate on a high-volume endpoint is a direct 20% cost reduction on that path.

Batch Where Latency Isn't Critical

For non-interactive workloads — nightly summarization jobs, bulk classification, report generation — batch processing is usually cheaper than real-time calls, and it also smooths out rate limit pressure. If your workload doesn't need a sub-second response, queue requests and process them in batches during off-peak windows.

Watch for Retry and Error Waste

A subtle but common cost leak: retries on failed or malformed requests. If your error handling blindly retries on any non-200 response, you can end up paying for the same generation multiple times. Add:

• Exponential backoff with a retry cap (2-3 attempts, not unlimited)
• Validation before retry — don't retry on a 400 (bad request), only on transient errors like 429 or 5xx
• Idempotency checks so a slow response doesn't get retried while still in flight

Standardize Access Across Teams

At scale, cost overruns often come from inconsistent access — different teams calling the LLM provider directly with their own patterns, no shared caching, no visibility into aggregate spend. Centralizing access through a single API layer, with per-team keys and shared usage dashboards, makes it much easier to enforce the practices above consistently. SubToAPI's Solo (€9), Team (€19/seat), and Scale (€49/seat) plans are built around this — one dashboard, streaming, and usage metadata across every key issued to your teams. You can try it with a free trial at /signup.

Questions

Does switching to a cheaper model always reduce quality? Not necessarily. For simple tasks like classification, formatting, or short extraction, smaller models often match larger ones on accuracy while costing a fraction as much. Test both on your actual data before assuming you need the top-tier model everywhere.

Is prompt caching worth setting up for a small app? It's worth it once you have repeated or near-duplicate queries — even a modest volume. If every request is genuinely unique, caching won't help much and you're better off focusing on prompt and output trimming first.

How do I know if my cost problem is input or output tokens? Check your usage metadata per request. Output tokens are usually the bigger cost driver in chat and generation use cases; input tokens dominate in RAG or long-context summarization. SubToAPI's /docs/messages response includes both, so you can see the split per call.