Token consumption, model pricing, efficiency scores and budget alerts — all in one dashboard. One line to install.
# Before from openai import OpenAI # After — only 2 lines changed import vantage from vantage.proxy.openai_proxy import OpenAI vantage.init(api_key="vnt_your_key") client = OpenAI(api_key="sk-...") # Everything else is identical — Vantage wraps transparently response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello!"}], ) # ✓ Tokens: 12 in, 8 out ✓ Cost: $0.000110 ✓ Latency: 423ms # ✓ Cheapest alternative: gemini-1.5-flash — save 94%
// Before import OpenAI from "openai"; // After — only 2 lines changed import { init, createOpenAIProxy } from "vantage-ai"; import OpenAI from "openai"; init({ apiKey: "vnt_your_key" }); const openai = createOpenAIProxy(new OpenAI()); // Identical API — Vantage wraps every call automatically const response = await openai.chat.completions.create({ model: "gpt-4o", messages: [{ role: "user", content: "Hello!" }], }); // ✓ Captured: tokens, cost, latency, cheapest alternative
// ~/.cursor/mcp.json (or windsurf / claude-code equivalent) { "mcpServers": { "vantage": { "command": "npx", "args": ["-y", "vantage-ai-mcp"], "env": { "VANTAGE_API_KEY": "vnt_your_key", "VANTAGE_ORG_ID": "your_org_id" } } } } // Then ask Cursor / Claude Code / Windsurf in chat: // "How much did I spend on AI this week?" // "Which model is cheapest for my summarisation workflow?" // "Show requests wasting the most tokens"
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