Prompt caching in production: the 4 patterns that cut my Anthropic bill (and when not to bother)

The first month I ran Career-OS in production, the Anthropic bill was bigger
than my coffee budget. After I wired prompt caching properly into the scorer,
the drafter, and the digest, it dropped under it. Same calls. Same model.
Same outputs. Roughly an 80% cost reduction in one afternoon.

Prompt caching is the single highest-leverage knob in the Claude SDK. It's
also the one I see misconfigured most often in client code — usually because
people read the docs, slap cache_control on something, and assume they're
caching when they're not.

Here are the 4 patterns I ship in production, with the cost math, and the 4
cases where caching genuinely does not help so you don't waste a day on it.

What prompt caching actually does

The mechanics, in three lines, because you need to know this to use it right:

A cached block (added with "cache_control": { "type": "ephemeral" }) is stored on Anthropic's side after the first call. Subsequent calls with an identical cached block hit the cache instead of re-processing.
First call to a cache block costs 1.25× the base input price (cache write). Every subsequent call within the TTL costs 0.1× the base price (cache read). The break-even is at the second call.
Default TTL is 5 minutes. A 1-hour TTL is available at 2× write cost. Plan for the TTL — it shapes the entire pattern.

If your workload calls the same prompt twice within 5 minutes, caching pays
off. If you call it once an hour with no warmup, you're paying the write
penalty for nothing.

Pattern 1 — Cache the system block

The pattern everyone reaches for first, and the one that gives the biggest
win in 90% of cases.

// app/api/agent/route.ts
import Anthropic from "@anthropic-ai/sdk";

const claude = new Anthropic();

export async function POST(req: Request) {
  const { question } = await req.json();

  const reply = await claude.messages.create({
    model: "claude-sonnet-4-6",
    max_tokens: 1024,
    system: [
      {
        type: "text",
        text: SYSTEM_PROMPT,                    // 2,400 tokens of context
        cache_control: { type: "ephemeral" },   // ← the magic
      },
    ],
    messages: [{ role: "user", content: question }],
  });

  return Response.json({ answer: reply.content });
}

The math, for a 2,400-token system prompt called 100 times in 5 minutes (the
realistic shape of a busy support endpoint):

Without caching: 100 × 2,400 × $3/M input = $0.72
With caching: 1 × 2,400 × $3.75/M (write) + 99 × 2,400 × $0.30/M (read) = $0.08
Savings: ~89%.

The break-even is between the 1st and 2nd call. After call 2 you're already
ahead. After call 100 you've collapsed an 89% chunk of your bill into
operating expense.

Cache hits are silent. The API returns cache_creation_input_tokens and
cache_read_input_tokens in the usage block. Log them. If you're not seeing
reads, you're not caching:

console.log({
  cache_write: reply.usage.cache_creation_input_tokens,
  cache_read:  reply.usage.cache_read_input_tokens,
  uncached:    reply.usage.input_tokens,
});

A single dashboard tile showing cache_read / (cache_read + uncached) tells
you whether your caching is working. Mine sits at 94% for the Career-OS
scorer during morning crawl runs.

Pattern 2 — Cache long documents (the RAG-adjacent pattern)

The pattern that actually changes which architectures are economically viable.

Say you have a 30,000-token product manual, customer policy document, or
codebase. Without caching, every customer question costs you ~$0.09 in input
tokens alone. With caching, your first question of the day costs you
~$0.11, and every subsequent question costs $0.01.

# document_qa.py

reply = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=600,
    system=[
        {
            "type": "text",
            "text": LIGHT_INSTRUCTIONS,         # 200 tokens, uncached
        },
        {
            "type": "text",
            "text": SHOP_POLICY_DOCUMENT,       # 30,000 tokens, CACHED
            "cache_control": {"type": "ephemeral"},
        },
    ],
    messages=[{"role": "user", "content": user_question}],
)

What this kills: most of the use cases people built RAG for. If your
"retrieval over a fixed corpus" use case fits inside Claude's 200K context,
caching the full document is often cheaper and always more accurate than
embedding-based retrieval. No chunking. No top-k tuning. No vector DB
operational burden.

The catch: the corpus has to be relatively stable. If your "document" is
yesterday's database dump, you're paying the cache write fee every single
day. Use cache for things that change weekly, not hourly.

Pattern 3 — Cache tool definitions

Tool use blocks are tokens. They count. And they're identical across every
call to the same agent.

TOOLS = [
    {"name": "search_orders", "description": "...", "input_schema": {...}},
    {"name": "issue_refund",  "description": "...", "input_schema": {...}},
    {"name": "lookup_user",   "description": "...", "input_schema": {...}},
    # … 12 tools in total, ~3,500 tokens of schema
]

reply = client.messages.create(
    model="claude-sonnet-4-6",
    tools=TOOLS,
    system=[
        {
            "type": "text",
            "text": SYSTEM_PROMPT,
            "cache_control": {"type": "ephemeral"},
        },
    ],
    messages=[...],
)

When you cache the system block, tool definitions get cached too if
they're declared in the same call. They become part of the cached prefix.
You don't need a separate cache_control on the tools array — the cache
boundary extends through everything in the system block and the tools.

This is a 3,500-token win you get for free when you're already caching the
system block. Most of the time it's already happening and you don't realize
it. Worth confirming with the cache_creation_input_tokens log line.

Pattern 4 — Conversation prefix caching for multi-turn agents

The pattern that makes long-running agentic loops affordable.

Multi-turn agents — the ones that loop through assistant → tool_use → tool_result → assistant → tool_use → … — re-send the entire conversation
history on every call. By turn 8, you're sending 12,000+ tokens of history,
most of which is unchanged from turn 7.

Cache the prefix.

def agent_loop(initial_message: str) -> str:
    messages = [{"role": "user", "content": initial_message}]

    for turn in range(max_turns := 10):
        # Cache everything up to the last assistant turn
        cached_messages = mark_last_message_cached(messages)

        reply = client.messages.create(
            model="claude-sonnet-4-6",
            tools=TOOLS,
            system=[{
                "type": "text", "text": SYSTEM_PROMPT,
                "cache_control": {"type": "ephemeral"}
            }],
            messages=cached_messages,
        )

        if reply.stop_reason == "end_turn":
            return reply.content[0].text

        messages.append({"role": "assistant", "content": reply.content})
        messages.append({"role": "user", "content": run_tools(reply)})

def mark_last_message_cached(messages: list) -> list:
    """Add cache_control to the last user message so the whole prefix caches."""
    out = list(messages)
    if out:
        last = out[-1].copy()
        if isinstance(last["content"], str):
            last["content"] = [{"type": "text", "text": last["content"]}]
        last["content"][-1]["cache_control"] = {"type": "ephemeral"}
        out[-1] = last
    return out

Each new turn extends the cached prefix by the previous turn's content. By
turn 10, ~95% of your input tokens hit cache reads. An agent loop that would
cost $0.40 to run uncached costs $0.05 with this pattern.

The 4 cases where caching does NOT help

This is where I see clients waste afternoons. Be honest about whether your
workload fits.

1. Your prompts vary too much. If each call has a different system
prompt (you're concatenating user-specific data into it, or A/B-testing
prompt variants), there's no shared cache prefix to hit. Either restructure
to push the variation into the messages block (keeping the system stable),
or accept that caching isn't your lever.

2. Your volume is low. If you call the model 5 times an hour spread
evenly, the 5-minute TTL means you almost never hit a warm cache. The
1-hour TTL helps but doubles the write cost. At extremely low volumes the
math sometimes works out to "uncached is cheaper."

3. Your prompts are short. Below ~1,024 tokens of cacheable content (the
Anthropic minimum), caching just doesn't activate. The write cost is paid;
no cache is created. Quietly. Check the usage block.

4. Your content is per-user and short-lived. If the cached content is
specific to one user and they only make one or two calls, you're paying the
write penalty without ever hitting the cache. Aggregation across users or
sessions doesn't apply.

Operational hygiene

The three things to wire up before you ship cached calls:

Log cache_creation_input_tokens and cache_read_input_tokens for every call. Without this, you have no idea if caching is working.
Alert on cache hit rate dropping. If your dashboard shows 90% hits on Monday and 12% on Tuesday, something changed in your prompt structure. Tuesday's bill will reflect it.
Don't put PII or per-user secrets in the cached block. Cached content is reused. Anything you put in there is shared across every call that hits the same cache key. Put per-user context in the user messages block where it belongs.

What this is worth, dollars and time

For Career-OS, the four patterns above collapsed the morning crawl-and-score
run from "noticeable on the bill" to "rounding error." Setup time: one
afternoon. Ongoing maintenance: the three log lines + one dashboard tile.

For an inbound support agent handling 20,000 queries a month: easily
$200–$400/month saved versus uncached, every month, forever, with the same
quality of output.

For a documentation-QA endpoint over a stable corpus: the difference between
"too expensive to ship to all users" and "an obvious feature." I've watched
this single decision unblock entire roadmap items.

When to call

If you have a Claude-powered feature in production today and you do not have
a dashboard tile showing cache hit rate, that's the bug. Cache misses are
silent and your bill is paying for them.

This is a 1–3 day scoped audit + fix that I take on:
the shape is on the hire-me page.

For the full context where these patterns ship, see the
Career-OS architecture walkthrough.
For the upstream patterns — where to bolt the Claude call onto your stack
in the first place — see the
5 places to bolt AI onto Laravel
and the
PrestaShop 5-file pattern.
And before any of this ships to production, the
eval harness post
is the discipline that catches the regressions caching alone can't.

Originally published on bak-dev.com. Find more build-in-public posts at bak-dev.com/blog.

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