The 270-Second Rule: How to Cut Claude Code API Costs by 90% with Smart

Source: Dev.to

Key Takeaways

• Anthropic’s prompt cache has a 5‑minute TTL.
• Orchestrator loops running faster than 270 seconds pay ~10 % of full input token costs.

What Changed — The Cache TTL You’re Probably Ignoring

Anthropic’s prompt caching has a 5‑minute TTL (Time To Live). After 5 minutes (300 seconds) the cache entry expires and your next Claude API request pays the full input‑token cost to re‑process the entire context.

For Claude Code users building multi‑agent systems or orchestration loops, this changes everything:

• > 300 seconds – every iteration pays full context cost
• < 300 seconds – you stay inside the cache window, paying ~10 % of base input cost
• ≈ 300 seconds – worst case – unpredictable cache behavior

Critical update: In March 2026, Anthropic changed the default cache TTL from 1 hour to 5 minutes. If you configured caching before March 6, your assumptions may be wrong. Disabling telemetry also disables the 1‑hour TTL entirely.

Why 270 Seconds Specifically

The math is simple but crucial:

• 5 minutes = 300 seconds
• Subtract ~30 seconds for processing time, context assembly, and clock skew

270 seconds gives a reliable buffer so every orchestrator tick arrives inside the cache window and pays cached input rates.

In the source system this saved $0.50–$1.20 /day on ~391 K tokens/day of orchestrator calls. The savings compound across parallel agents and scale with usage.

How To Apply This To Your Claude Code Workflows

1. Check Your Current Cache Behavior

# Add this to your Claude API calls to verify caching
response = client.messages.create(...)
print(f"Cache read tokens: {response.usage.cache_read_input_tokens}")
print(f"Cache creation tokens: {response.usage.cache_creation_input_tokens}")

If cache_read_input_tokens is 0 on your second call within 5 minutes, the cache is broken or you’re hitting the TTL boundary.

2. Adjust Your Orchestrator Loop

import time

TICK_INTERVAL = 270  # seconds — matches Anthropic cache TTL with buffer

def orchestrator_tick():
    # Your Claude Code orchestration logic here:
    # 1. Check agent statuses
    # 2. Process completed tasks
    # 3. Dispatch new work
    # 4. Update state
    pass

while True:
    orchestrator_tick()
    time.sleep(TICK_INTERVAL)

3. Structure Your Context for Caching

The cache works on identical prompts. Structure your orchestrator context so it changes minimally between ticks:

• Keep static instructions in system prompts
• Separate dynamic state into specific message roles
• Use consistent formatting for agent status reports

4. When NOT to Use 270‑Second Ticks

Applicable

• Multi‑agent orchestration systems
• Periodic status‑checking loops
• Background monitoring agents

Not applicable

• Interactive Claude Code sessions
• Real‑time coding assistance
• Latency‑sensitive workflows

The Broader Principle

The 270‑second tick exemplifies a critical principle: orchestration cadence should be derived from infrastructure constraints, not arbitrary responsiveness goals.

An initial instinct to tick every 60 seconds (“responsive enough”) leads to paying ~4.5× more for the orchestrator context window when agents take minutes to complete work.

What This Means for Your Claude Code Projects

• Audit existing loops: Identify any periodic Claude calls.
• Add cache monitoring: Incorporate the verification check into your logging.
• Consider agent granularity: Fewer, longer‑running agents may be more cost‑effective than many quick‑checking ones.
• Document TTL assumptions: Ensure the team knows the current cache behavior, especially after infrastructure changes.

The free resources mentioned in the original article (e.g., the whoffagents.com architecture and GitHub quickstart) provide concrete implementation patterns for multi‑agent systems that can benefit from this optimization.

Originally published on gentic.news.