llm-cost-watch

llm-cost-watch

The Model Context Protocol (MCP) server for LLM spend & latency intelligence.
"Ask Claude why our LLM bill jumped."

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📺 90-Second Explainer Video Walkthrough

▶️ Click here to download / watch the full 1080p MP4 walkthrough (with voiceover)
Explaining why multi-agent swarms face compounding token spikes, how local-first MCP telemetry works, and how to cut LLM spend by 94% with automated micro-model routing.

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What is llm-cost-watch?

Instead of clicking through complex web dashboards, llm-cost-watch lets you ask Claude directly inside Claude Desktop, Claude Code, or Antigravity:

You: "Claude, why did our LLM spend jump 40% this week, and where is our latency bottleneck?"

Claude: [Calls llm-cost-watch: cost_by_feature & slow_calls]
"Your 'knowledge_base_rag' pipeline accounts for 66.1% of total spend ($0.082 across 3 test runs) 
and is driving your p95 latency up to 3,120ms due to uncompressed 7k token context windows.

Meanwhile, 'customer_intent_classifier' is currently routed through GPT-4o for 14-token classification. 
Routing it to GPT-4o-mini will cut that feature's spend by 94% and reduce p95 latency to 320ms."

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3 Core MCP Tools

1. cost_by_feature(timeframe="7d"): Identifies which feature or workflow consumes the largest share of your budget.
2. slow_calls(limit=5, min_latency_ms=1200): Pinpoints high-latency outliers (p50, p95, p99) and diagnoses the bottleneck (unbounded prompt payload, recursive tool loops, reasoning stalls).
3. routing_suggestions(savings_threshold_pct=20): Analyzes short structured prompts running on expensive frontier models and calculates exact dollar savings from moving to Claude 3.5 Haiku or GPT-4o-mini.

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Quick Setup (Under 5 Minutes)

1. Install & Clone

git clone https://github.com/rohith0707/llm-cost-watch.git
cd llm-cost-watch
pip install -r requirements.txt

2. Connect to Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "llm-cost-watch": {
      "command": "python",
      "args": ["-m", "tools.llm-cost-watch.server"]
    }
  }
}

3. Log Calls in Your Code (One Line)

from logger import watch_llm

@watch_llm(feature="customer_intent_classifier", task_type="classification", model="gpt-4o")
def classify_intent(message: str) -> str:
    # Your normal OpenAI / Anthropic call
    return client.chat.completions.create(...)

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Test Run

python test_server.py

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Commercial Licensing & Architecture Audits

• Free Core: Open-source under MIT.
• Acquisition & IP Rights: Listed on TrustMRR: AgentControl & Decision Plane ($25,000 asking price).
• Self-Hosted Engine: Get the production LeadHunter & Concurrency Starter Kit on Gumroad (Flash Offer: $19).
• Enterprise SLA & 14-Day Concurrency / Cost Audit ($2,500): Contact Rohith Balsa (balsarohith5@gmail.com | LinkedIn).