If you have been building with Claude Code, Cursor, or autonomous agentic workflows over the past few months, you've probably felt the sting of runaway Anthropic API bills.
A few days of multi-turn coding sessions can easily run up hundreds of dollars in token usage.
Today, I'm open-sourcing ClaudeCut — a lightweight local reverse proxy that automatically reduces Anthropic Claude API expenses by 40% to 70% without sacrificing code quality or model intelligence.
Here is the exact engineering behind how it works.
⚡ The Root Problem: Why Claude Gets Expensive
When you use Claude in an IDE or CLI, your client sends:
- The entire repository structure & opened files
- The developer rules / system instructions
- The full tool definition schemas
- The entire multi-turn conversation history
Every single time you send a message, your tool re-sends that entire 30,000+ token context from scratch.
By default, Anthropic bills input tokens at standard full rates:
- Claude 3.7 / 3.5 Sonnet: $3.00 per million input tokens
- Claude Opus: $15.00 per million input tokens
The "Hidden" 90% Discount
Anthropic offers Prompt Caching, which discounts cached input tokens by 90% ($0.30/M on Sonnet).
The catch: To get this discount, requests must contain explicit cache_control: {"type": "ephemeral"} breakpoints. In standard setups, developers don't configure these breakpoints manually, so they pay full price every turn.
💡 How ClaudeCut Solves This
ClaudeCut is a drop-in local proxy that sits seamlessly between your IDE / CLI and Anthropic:
1️⃣ Automated Ephemeral Prompt Caching (90% Off)
ClaudeCut dynamically intercepts incoming payload requests and places up to 4 cache breakpoints (Anthropic's maximum limit):
- System instructions
- Tool definition schemas
- Multi-turn chat checkpoints
Claude recognizes the cache bookmarks and automatically applies the 90% discount on all repeat context.
2️⃣ Dynamic Task-Based Model Switching
Why pay flagship rates to write a 1-line git commit message or fix a typo?
ClaudeCut inspects task heuristics in real-time:
- Tier 1 (Lightweight / 90% cheaper): Formats, typo fixes, git commit messages, regex, syntax checks ➔ Routed to Claude 3.5 Haiku
- Tier 2 (Standard Workhorse): Feature coding, refactoring, multi-file edits, unit tests ➔ Claude 3.7 Sonnet
- Tier 3 (Deep Reasoning): Architecture design, formal verification, security audits ➔ Claude Opus
3️⃣ Context & Whitespace Minification
Strips redundant delimiters, blank lines, and trailing whitespace bloat before tokenization (saving 10%–25% tokens without breaking syntax).
4️⃣ Real-Time Financial Dashboard
Runs a local dashboard at http://localhost:8000/dashboard displaying live dollars saved, actual API spend, cache hit ratios, and model tier distribution.
📊 The Math: 10-Turn Benchmark
Here is a real comparison on a 10-turn coding session with 35,000 context tokens:
| Setup | Input Tokens | Cache Reads (90% OFF) | Output Tokens | Total Cost |
|---|---|---|---|---|
| Standard Claude (Direct) | 360,000 | 0 | 6,000 | $1.170 |
| With ClaudeCut Optimizer | 10,000 | 315,000 | 6,000 | $0.346 |
| Net Cost Reduction | — | — | — | 🔥 70.38% SAVED |
🚀 Quickstart in 60 Seconds
1. Clone & Run ClaudeCut
git clone https://github.com/rahulagarwal18/ClaudeCut.git
cd ClaudeCut
pip install -r requirements.txt
python -m claudecut.cli start
2. Connect Claude Code CLI
export ANTHROPIC_BASE_URL="http://127.0.0.1:8000"
claude
3. Connect Python SDK / Cursor
import anthropic
client = anthropic.Anthropic(
base_url="http://127.0.0.1:8000"
)
🔗 Try It Out & Star the Repo!
The project is live on GitHub:
👉 github.com/rahulagarwal18/ClaudeCut
If this saved you money on your Claude bills, consider dropping a ⭐ on GitHub!
What are your favorite techniques for optimizing LLM inference costs? Let's discuss in the comments below! 👇
Top comments (1)
You can find the full source code and setup guide on GitHub: github.com/rahulagarwal18/ClaudeCut
⭐ Feel free to star the repo if you find it helpful!