Symptom
Each turn of a multi-turn conversation bills the same large system prompt as new input tokens. With a 10,000-token system prompt and a 20-turn conversation, the agent pays for 200,000 system prompt tokens when it should pay for ~10,000 (first turn) plus ~200,000 × 0.1 (cache hits). At scale, this is the single largest avoidable cost in most agent deployments.
Observable signs:
- Input token count in API responses is nearly constant each turn (matches system prompt size)
- Cost per conversation grows linearly with turn count instead of sublinearly
cache_read_input_tokensin API response is always 0usage.cache_creation_input_tokensnever appears in logs
# BROKEN: full system prompt sent every single turn
def chat(history: list[dict], user_message: str) -> str:
history.append({"role": "user", "content": user_message})
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=LARGE_SYSTEM_PROMPT, # 10,000 tokens re-sent every call
messages=history,
)
reply = response.content[0].text
history.append({"role": "assistant", "content": reply})
return reply
# Turn 1: 10,000 system + 20 user = 10,020 input tokens
# Turn 20: 10,000 system + 8,000 history + 50 user = 18,050 input tokens
# Total: ~280,000 input tokens for the system prompt alone
Root Cause
The Anthropic API requires system to be sent on every request — it is not persisted server-side between calls. Without prompt caching, the full text is tokenized and billed as new input tokens each time.
Prompt caching solves this: mark a system prompt block with cache_control: {type: ephemeral} and Anthropic will cache the KV state of that block for 5 minutes (extended to 1 hour on subsequent hits). Cache hits cost 10% of normal input token price.
The catch: caching requires the cached content to appear at consistent positions in the request, with identical byte content. Any mutation — even a trailing space — busts the cache.
Fix
Option 1 — Single cache_control Block on System Prompt
The minimal change: add cache_control to the system prompt so it’s cached after the first turn.
import anthropic
client = anthropic.Anthropic()
# Large system prompt — represents tools, instructions, examples, etc.
SYSTEM_PROMPT = """You are an expert software engineering assistant for AcmeCorp.
## Company Context
AcmeCorp builds distributed data infrastructure. Our stack: Python, Go, Kubernetes,
PostgreSQL, Redis, Kafka. We follow trunk-based development with feature flags.
All code must pass mypy strict, ruff, and our custom linter rules.
## Engineering Standards
[... imagine 8,000 more tokens of context: coding standards, architecture docs,
API references, team conventions, example code patterns, etc. ...]
## Response Format
- Lead with the answer, not the explanation
- Include runnable code examples when relevant
- Reference specific file paths when discussing our codebase
- Flag security implications explicitly
"""
# Structure system as a list with cache_control — NOT as a plain string
CACHED_SYSTEM = [
{
"type": "text",
"text": SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"}, # cache this block
}
]
def chat_with_cache(history: list[dict], user_message: str) -> tuple[str, dict]:
"""Chat with prompt caching enabled. Returns reply and usage stats."""
history.append({"role": "user", "content": user_message})
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=CACHED_SYSTEM, # identical structure every call → cache hit after turn 1
messages=history,
betas=["prompt-caching-2024-07-31"], # required for explicit cache control
)
reply = response.content[0].text
history.append({"role": "assistant", "content": reply})
usage = {
"input_tokens": response.usage.input_tokens,
"output_tokens": response.usage.output_tokens,
"cache_creation_tokens": getattr(response.usage, "cache_creation_input_tokens", 0),
"cache_read_tokens": getattr(response.usage, "cache_read_input_tokens", 0),
}
return reply, usage
# Demonstrate cache behavior across turns
history = []
topics = [
"What's the best way to handle database migrations in our stack?",
"How should I structure error handling in our Go services?",
"What's our approach to feature flags?",
"How do we handle secrets management?",
]
print("Turn | Input Tokens | Cache Creation | Cache Read | Effective Cost%")
print("-" * 70)
for i, topic in enumerate(topics):
reply, usage = chat_with_cache(history, topic)
cache_pct = (usage["cache_read_tokens"] / max(usage["input_tokens"], 1)) * 100
effective_cost = usage["input_tokens"] - usage["cache_read_tokens"] * 0.9 # 10% of cache hits
print(
f" {i+1:2d} | {usage['input_tokens']:12,d} | {usage['cache_creation_tokens']:14,d} | "
f"{usage['cache_read_tokens']:10,d} | {cache_pct:.0f}% cached"
)
# Expected output:
# 1 | 10,020 | 10,000 (cache created) | 0 | 0% cached
# 2 | 8,120 | 0 | 10,000 | 86% cached ← massive saving
# 3 | 9,340 | 0 | 10,000 | 73% cached
# 4 | 10,890 | 0 | 10,000 | 68% cached
Expected Token Savings: 85-90% reduction on system prompt tokens from turn 2 onward. A 10,000-token system prompt cached for 20 turns saves ~171,000 input tokens vs. full re-send.
Environment: Python 3.9+, anthropic>=0.40.0. Prompt caching is available on Claude Haiku 4.5, Sonnet 4.6, and Opus 4.6. The betas parameter enables explicit cache control.
Option 2 — Multi-Block Caching for System + Tool Definitions
Cache both the system prompt and tool definitions separately to maximize cache hits when tools change less often than user conversation.
import anthropic
import json
client = anthropic.Anthropic()
BASE_INSTRUCTIONS = """You are a data analysis assistant. Help users query, transform,
and visualize datasets. Always validate inputs before executing queries.
[... 5,000 tokens of base instructions ...]"""
TOOL_REFERENCE = """## Available Tools Reference
### query_database
Executes SQL against the analytics warehouse. Supports SELECT only.
Parameters: sql (string), timeout_seconds (int, default 30)
Returns: {rows: [...], column_names: [...], row_count: int}
### create_visualization
Generates a chart from tabular data.
Parameters: data (array), chart_type (bar|line|pie|scatter), title (string)
Returns: {chart_url: string, alt_text: string}
[... 3,000 tokens of tool docs ...]"""
# Two cached blocks — base instructions cached longer, tools cached separately
MULTI_BLOCK_SYSTEM = [
{
"type": "text",
"text": BASE_INSTRUCTIONS,
"cache_control": {"type": "ephemeral"}, # 5-min TTL, extended on hits
},
{
"type": "text",
"text": TOOL_REFERENCE,
"cache_control": {"type": "ephemeral"}, # separate cache entry
},
]
TOOLS = [
{
"name": "query_database",
"description": "Execute a SQL SELECT query against the analytics warehouse.",
"input_schema": {
"type": "object",
"properties": {
"sql": {"type": "string", "description": "SELECT statement to execute"},
"timeout_seconds": {"type": "integer", "default": 30},
},
"required": ["sql"]
}
},
{
"name": "create_visualization",
"description": "Create a chart from data.",
"input_schema": {
"type": "object",
"properties": {
"data": {"type": "array"},
"chart_type": {"type": "string", "enum": ["bar", "line", "pie", "scatter"]},
"title": {"type": "string"},
},
"required": ["data", "chart_type", "title"]
}
}
]
def run_analysis_session(questions: list[str]) -> list[dict]:
"""Run multiple analysis questions in one session, caching system + tools."""
history = []
session_usage = []
for question in questions:
history.append({"role": "user", "content": question})
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=MULTI_BLOCK_SYSTEM, # both blocks cached from turn 2 onward
tools=TOOLS,
messages=history,
betas=["prompt-caching-2024-07-31"],
)
# Handle tool use or text response
if response.stop_reason == "tool_use":
# In real code: execute tool and continue conversation
history.append({"role": "assistant", "content": response.content})
else:
history.append({"role": "assistant", "content": response.content[0].text})
session_usage.append({
"question": question[:50],
"input": response.usage.input_tokens,
"cache_created": getattr(response.usage, "cache_creation_input_tokens", 0),
"cache_read": getattr(response.usage, "cache_read_input_tokens", 0),
})
return session_usage
results = run_analysis_session([
"Show me total revenue by region for Q4 2024",
"Which product category had the highest growth?",
"Create a bar chart of the top 10 customers by spend",
"What's the week-over-week trend for active users?",
])
total_input = sum(r["input"] for r in results)
total_cache_read = sum(r["cache_read"] for r in results)
print(f"Total input tokens: {total_input:,}")
print(f"Served from cache: {total_cache_read:,} ({total_cache_read/total_input:.0%})")
print(f"Estimated savings: ~{int(total_cache_read * 0.9):,} tokens (90% discount on cache reads)")
Expected Token Savings: Caching both system (5,000 tokens) and tool reference (3,000 tokens) saves ~14,400 tokens per turn from turn 2 onward. 10-turn session: ~130,000 tokens saved.
Environment: Python 3.9+, anthropic>=0.40.0 with prompt caching beta.
Option 3 — Async Multi-Session Cache Warming
Pre-warm the cache at session start so the first real user turn already hits the cache.
import anthropic
import asyncio
client = anthropic.AsyncAnthropic()
LARGE_SYSTEM_PROMPT = """[System prompt with 8,000+ tokens of context...]"""
CACHED_SYSTEM = [
{
"type": "text",
"text": LARGE_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}
]
async def warm_cache() -> dict:
"""
Send a minimal message to prime the cache before the real conversation starts.
The cache TTL is 5 minutes, extended on each hit — warm on session start.
"""
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1, # minimal output — we only want to cache the system prompt
system=CACHED_SYSTEM,
messages=[{"role": "user", "content": "ready"}],
betas=["prompt-caching-2024-07-31"],
)
usage = {
"cache_created": getattr(response.usage, "cache_creation_input_tokens", 0),
"cache_read": getattr(response.usage, "cache_read_input_tokens", 0),
}
return usage
async def chat_turn(history: list[dict], user_message: str) -> tuple[str, dict]:
"""Single conversation turn — cache should already be warm."""
history.append({"role": "user", "content": user_message})
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=CACHED_SYSTEM,
messages=history,
betas=["prompt-caching-2024-07-31"],
)
reply = response.content[0].text
history.append({"role": "assistant", "content": reply})
return reply, {
"input": response.usage.input_tokens,
"cache_read": getattr(response.usage, "cache_read_input_tokens", 0),
}
async def run_warmed_session(user_messages: list[str]) -> None:
"""Warm cache at session start, then process messages."""
# Warm cache concurrently with session setup (e.g., loading user profile)
print("Warming prompt cache...")
warm_task = asyncio.create_task(warm_cache())
# Simulate concurrent session setup work
await asyncio.sleep(0.1)
warm_usage = await warm_task
print(f"Cache warmed: {warm_usage['cache_created']:,} tokens stored")
history = []
for msg in user_messages:
reply, usage = await chat_turn(history, msg)
saved = usage["cache_read"]
print(f"Turn: {usage['input']:,} input tokens, {saved:,} from cache ({saved/max(usage['input'],1):.0%})")
asyncio.run(run_warmed_session([
"Summarize our Q3 performance",
"What are the main risks for Q4?",
"Draft an executive summary",
]))
Expected Token Savings: Cache warming costs ~8,000 tokens once; saves ~7,200 tokens (90% discount) on every subsequent turn including the first.
Environment: Python 3.9+, asyncio, anthropic>=0.40.0 with prompt caching beta.
Option 4 — Cache-Aware Conversation Manager
Track cache efficiency per session and alert when cache is busted (e.g., by accidental system prompt mutation).
import anthropic
import hashlib
import time
from dataclasses import dataclass, field
client = anthropic.Anthropic()
@dataclass
class CacheStats:
turns: int = 0
total_input_tokens: int = 0
total_cache_created: int = 0
total_cache_read: int = 0
cache_busts: int = 0
last_system_hash: str = ""
@property
def cache_hit_rate(self) -> float:
if self.total_input_tokens == 0:
return 0.0
return self.total_cache_read / self.total_input_tokens
@property
def estimated_savings_tokens(self) -> int:
"""Tokens saved vs. paying full price on every turn."""
return int(self.total_cache_read * 0.9) # 90% discount on cache reads
def report(self) -> str:
return (
f"Cache stats: {self.turns} turns | "
f"{self.total_input_tokens:,} total input tokens | "
f"{self.cache_hit_rate:.0%} cache hit rate | "
f"~{self.estimated_savings_tokens:,} tokens saved | "
f"{self.cache_busts} cache busts"
)
class CacheAwareAgent:
"""
Conversation manager that tracks prompt cache efficiency
and warns when the system prompt is accidentally mutated.
"""
CACHE_TTL_SECONDS = 300 # 5 minutes
def __init__(self, system_prompt: str):
self._base_system = system_prompt
self._cached_system = [
{
"type": "text",
"text": system_prompt,
"cache_control": {"type": "ephemeral"},
}
]
self._history: list[dict] = []
self._stats = CacheStats()
self._last_call_time: float = 0.0
self._system_hash = self._hash(system_prompt)
@staticmethod
def _hash(text: str) -> str:
return hashlib.md5(text.encode()).hexdigest()[:8]
def _check_cache_validity(self):
"""Warn if system prompt changed or cache likely expired."""
current_hash = self._hash(self._base_system)
if current_hash != self._system_hash:
print(f" ⚠️ System prompt changed! Cache busted. ({self._system_hash} → {current_hash})")
self._stats.cache_busts += 1
self._system_hash = current_hash
if self._last_call_time > 0:
elapsed = time.monotonic() - self._last_call_time
if elapsed > self.CACHE_TTL_SECONDS:
print(f" ⚠️ Cache likely expired ({elapsed:.0f}s since last call > {self.CACHE_TTL_SECONDS}s TTL)")
self._stats.cache_busts += 1
def chat(self, user_message: str) -> str:
self._check_cache_validity()
self._history.append({"role": "user", "content": user_message})
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=512,
system=self._cached_system,
messages=self._history,
betas=["prompt-caching-2024-07-31"],
)
reply = response.content[0].text
self._history.append({"role": "assistant", "content": reply})
# Update stats
self._stats.turns += 1
self._stats.total_input_tokens += response.usage.input_tokens
self._stats.total_cache_created += getattr(response.usage, "cache_creation_input_tokens", 0)
cache_read = getattr(response.usage, "cache_read_input_tokens", 0)
self._stats.total_cache_read += cache_read
self._last_call_time = time.monotonic()
print(f" Turn {self._stats.turns}: {response.usage.input_tokens} input, {cache_read} from cache")
return reply
def print_stats(self):
print(f"\n{self._stats.report()}")
# Usage
SYSTEM = "You are a helpful assistant.\n" + ("Context: " + "x" * 9000) # ~10k token system
agent = CacheAwareAgent(SYSTEM)
messages = [
"Hello, what can you help me with?",
"Tell me more about option 2",
"Can you compare all the options?",
"Which would you recommend?",
]
for msg in messages:
reply = agent.chat(msg)
agent.print_stats()
# Expected: 75%+ cache hit rate from turn 2 onward
Expected Token Savings: 80-90% on system prompt from turn 2. Cache bust detection prevents silent cache misses that would otherwise go unnoticed.
Environment: Python 3.9+, anthropic>=0.40.0 with prompt caching beta.
Option 5 — Shared Cache Across Concurrent Sessions
Multiple agent sessions running simultaneously can share a warmed cache if they use the exact same system prompt.
import anthropic
import asyncio
import time
from typing import Optional
client = anthropic.AsyncAnthropic()
SHARED_SYSTEM_PROMPT = """You are a customer support agent for CloudApp.
[... 8,000 tokens of product documentation, FAQ, policies ...]"""
CACHED_SYSTEM = [
{
"type": "text",
"text": SHARED_SYSTEM_PROMPT,
"cache_control": {"type": "ephemeral"},
}
]
_cache_warm_time: Optional[float] = None
_cache_warm_lock = asyncio.Lock()
async def ensure_cache_warm() -> bool:
"""
Ensure the shared cache is warm. Only one coroutine warms at a time.
Returns True if cache was just created, False if already warm.
"""
global _cache_warm_time
async with _cache_warm_lock:
now = time.monotonic()
# Re-warm if never warmed or if TTL likely expired
if _cache_warm_time is None or (now - _cache_warm_time) > 240:
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1,
system=CACHED_SYSTEM,
messages=[{"role": "user", "content": "ping"}],
betas=["prompt-caching-2024-07-31"],
)
_cache_warm_time = now
created = getattr(response.usage, "cache_creation_input_tokens", 0)
print(f" Cache warmed: {created:,} tokens")
return True
return False
async def handle_session(session_id: int, user_messages: list[str]) -> dict:
"""Handle a single user session, benefiting from shared cache."""
await ensure_cache_warm()
history = []
total_input = 0
total_cache_read = 0
for msg in user_messages:
history.append({"role": "user", "content": msg})
response = await client.messages.create(
model="claude-sonnet-4-6",
max_tokens=256,
system=CACHED_SYSTEM,
messages=history,
betas=["prompt-caching-2024-07-31"],
)
reply = response.content[0].text
history.append({"role": "assistant", "content": reply})
total_input += response.usage.input_tokens
total_cache_read += getattr(response.usage, "cache_read_input_tokens", 0)
return {
"session_id": session_id,
"turns": len(user_messages),
"total_input_tokens": total_input,
"total_cache_read": total_cache_read,
"cache_hit_rate": total_cache_read / max(total_input, 1),
}
async def simulate_concurrent_sessions():
"""Simulate 10 concurrent user sessions sharing a warmed cache."""
sessions = [
handle_session(i, [
"How do I reset my password?",
"What's included in the Pro plan?",
])
for i in range(10)
]
results = await asyncio.gather(*sessions)
total_input = sum(r["total_input_tokens"] for r in results)
total_cache_read = sum(r["total_cache_read"] for r in results)
print(f"\n10 concurrent sessions:")
print(f" Total input tokens: {total_input:,}")
print(f" Served from cache: {total_cache_read:,} ({total_cache_read/total_input:.0%})")
print(f" Savings: ~{int(total_cache_read * 0.9):,} tokens")
asyncio.run(simulate_concurrent_sessions())
# All 10 sessions share the same cached system prompt
# Expected: 85%+ cache hit rate across all sessions
Expected Token Savings: With 10 concurrent sessions × 2 turns × 8,000-token system prompt = 160,000 tokens. Cache read discount saves ~144,000 tokens vs. full pricing.
Environment: Python 3.9+, asyncio, anthropic>=0.40.0 with prompt caching beta.
Option 6 — Dynamic System Prompt with Static Prefix Caching
When the system prompt has both static (cacheable) and dynamic (per-user) sections, structure them so the static prefix is always cached.
import anthropic
client = anthropic.Anthropic()
STATIC_PREFIX = """You are an expert coding assistant for DevHub.
## Platform Capabilities
[... 7,000 tokens of static documentation: language support, IDE integrations,
code review standards, security policies, API references ...]
## Core Behavior
- Provide runnable code examples
- Flag security issues explicitly
- Suggest tests for every code change
- Reference documentation when relevant
"""
def build_system_prompt(
user_name: str,
user_role: str,
active_project: str,
user_preferences: dict,
) -> list[dict]:
"""
Build a two-part system prompt:
- Part 1: Large static prefix (cached) — identical across all users
- Part 2: Small dynamic suffix (not cached) — personalized per user
"""
dynamic_suffix = f"""
## Current Session Context
User: {user_name} ({user_role})
Active project: {active_project}
Preferred language: {user_preferences.get('language', 'Python')}
Code style: {user_preferences.get('style', 'PEP 8')}
Verbosity: {user_preferences.get('verbosity', 'concise')}
"""
return [
{
"type": "text",
"text": STATIC_PREFIX,
"cache_control": {"type": "ephemeral"}, # cached — same for all users
},
{
"type": "text",
"text": dynamic_suffix,
# No cache_control — this changes per user, not worth caching
},
]
def code_assistant_chat(
history: list[dict],
user_message: str,
user_context: dict,
) -> tuple[str, dict]:
"""
Chat with static prefix cached, dynamic suffix freshly sent.
"""
system = build_system_prompt(
user_name=user_context["name"],
user_role=user_context["role"],
active_project=user_context["project"],
user_preferences=user_context.get("preferences", {}),
)
history.append({"role": "user", "content": user_message})
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
system=system,
messages=history,
betas=["prompt-caching-2024-07-31"],
)
reply = response.content[0].text
history.append({"role": "assistant", "content": reply})
usage = {
"input": response.usage.input_tokens,
"cache_created": getattr(response.usage, "cache_creation_input_tokens", 0),
"cache_read": getattr(response.usage, "cache_read_input_tokens", 0),
"dynamic_tokens": len(system[1]["text"].split()), # approximate
}
return reply, usage
# Two different users — both benefit from the same cached static prefix
users = [
{"name": "Alice", "role": "Senior Engineer", "project": "auth-service",
"preferences": {"language": "Python", "style": "PEP 8", "verbosity": "concise"}},
{"name": "Bob", "role": "Junior Engineer", "project": "frontend-app",
"preferences": {"language": "TypeScript", "style": "Airbnb", "verbosity": "detailed"}},
]
for user in users:
print(f"\nSession for {user['name']}:")
history = []
questions = [
"How do I write a context manager?",
"What's the best way to handle errors here?",
]
for q in questions:
reply, usage = code_assistant_chat(history, q, user)
print(f" Input: {usage['input']:,} | Cache read: {usage['cache_read']:,} | Created: {usage['cache_created']:,}")
# Key insight: Alice and Bob share the cached static prefix (7,000 tokens)
# Their dynamic suffix (~50 tokens each) is sent fresh — tiny cost
# Result: 7,000-token cache hit for EVERY turn of EVERY user session
Expected Token Savings: Static prefix (7,000 tokens) cached and shared across all users. 100 users × 5 turns each = 3,500,000 tokens; with caching: 7,000 (creation) + 3,493,000 × 10% = ~356,300 effective tokens. 90% reduction on the static portion.
Environment: Python 3.9+, anthropic>=0.40.0 with prompt caching beta. Cache is shared across all requests with identical static prefix content.
Comparison
| Option | Approach | Setup Effort | Sessions | Dynamic Content |
|---|---|---|---|---|
| 1 — Single Block | Add cache_control to system | Minimal | Single | No |
| 2 — Multi-Block | Cache system + tools separately | Low | Single | No |
| 3 — Cache Warming | Pre-warm before session starts | Low | Single | No |
| 4 — Cache Monitor | Track hit rate, detect busts | Medium | Single | No |
| 5 — Shared Cache | One warm serves N concurrent sessions | Medium | Multi | No |
| 6 — Static Prefix | Cache static part, send dynamic fresh | Medium | Multi | Yes |
Start with Option 1 for any existing agent — it’s a one-line change to the system prompt structure. Add Option 6 (static prefix) when different users get personalized system prompts. Use Option 5 (shared cache warming) for high-concurrency deployments to ensure the first user in each 5-minute window doesn’t pay full price.
Key constraint: Cache busts happen when system prompt content changes even by one character. Never format, sort, or template-fill the cached portion dynamically — treat it as a compile-time constant.
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