How Prompt Caching Cuts LLM Costs for Front‑End Teams

Why token bills explode in a front‑end pipeline

When you stitch together research, summarisation, and content generation agents, each step repeats the same system prompt and a few dozen instructions. Multiply that by dozens of topics and you quickly hit millions of tokens. In my recent Claude run I logged 7.3 M tokens for a single batch, which at list price would be $8 – a modest amount, but the hidden cost was the repeated prompt payload that never changed.

What prompt caching actually does

Prompt caching stores the model's internal representation of a static prompt on the server side. When you send the same prompt again, the model re‑uses the cached representation and only processes the new user‑provided tokens. The result is a drastic drop in prompt tokens while completion tokens stay the same.

Detecting cache‑able prompts in your code

Look for any system or assistant messages that are identical across calls. In a TypeScript orchestrator you can extract them into a constant and pass a cache_key if the provider supports it. Here’s a minimal wrapper that adds a hash‑based cache key:

import crypto from "crypto";
import { ClaudeClient, Message } from "@anthropic/sdk";

function cacheKey(messages: Message[]): string {
  const system = messages.find(m => m.role === "system")?.content || "";
  return crypto.createHash("sha256").update(system).digest("hex");
}

export async function cachedCompletion(messages: Message[]) {
  const key = cacheKey(messages);
  return ClaudeClient.complete({messages, cache_key: key});
}

Every call that reuses the same system content now hits the provider's cache.

Measuring the impact

Run the same pipeline twice: once with raw prompts, once with the wrapper above. Compare the usage.prompt_tokens field in the API response. In my test, a 10‑topic batch dropped from 3.2 M to 0.9 M prompt tokens – a 72% reduction. The dollar impact at $0.015 per 1 K prompt tokens is roughly $35 saved per batch.

Trade‑offs and gotchas

• Cache invalidation: If you tweak the system prompt, the hash changes and the cache is bypassed – exactly what you want, but remember to version your prompts.
• Provider support: Not all APIs expose a cache key. OpenAI’s logit_bias trick can emulate caching, but it’s slower than native support.
• Latency: The first hit still incurs full compute cost; subsequent hits are faster, but the benefit is token‑wise, not time‑wise.

Putting it into a real front‑end workflow

Suppose you have a Next.js page that calls an internal API to generate SEO meta tags on demand. Instead of sending the full prompt each time, store the static part in a server‑side module and reuse the cache key. The front‑end sees no difference – you just pay less behind the scenes.

// pages/api/seo.ts
import { cachedCompletion } from "../../lib/aiCache";

export default async function handler(req, res) {
  const { topic } = req.query;
  const messages = [
    {role: "system", content: "You are an SEO expert. Generate meta title and description for the given topic."},
    {role: "user", content: `Topic: ${topic}`}
  ];
  const result = await cachedCompletion(messages);
  res.json(result);
}

Now every request for the same topic reuses the cached system prompt, keeping your token bill flat even as traffic spikes.