TokenOps by Lovie

Key Features

• •Vendor wrappers support Anthropic, OpenAI, Bedrock, Google AI, Vercel AI, and Azure with one-line code integration, capturing LLM usage events while maintaining direct vendor connectivity for optimal performance and security.
• •Invoice reconciliation engine diffs monthly vendor invoices against captured usage events line by line, revealing effective rates, identifying pricing drift, and detecting quietly applied volume discounts that affect per-customer cost calculations.
• •MCP catalog provides thirty specialized tools for customers, events, vendors, finance operations, and system management, enabling AI agents to answer complex financial questions through natural language queries integrated with Claude Desktop and other clients.
• •Per-customer cost attribution uses customerId parameters passed with each LLM call to join usage data with specific accounts, providing granular visibility into which customers are driving AI infrastructure expenses and their corresponding profitability.
• •Workflow tools include build_pnl for generating profit and loss statements, reconcile for invoice verification, anomaly detection for spending pattern monitoring, and price_change simulations for forecasting revenue impact of pricing adjustments.
• •Real-time cost dashboard displays LLM spending across customers within seconds of usage events, with monthly reconciliation against vendor invoices to ensure accuracy and provide continuous visibility into unit economics.
• •TypeScript and Python SDKs support edge runtime environments with simple installation processes, allowing quick integration into existing AI applications without requiring architectural changes or performance compromises.

Use Cases

• •Monthly board reporting where finance teams generate per-customer P&L statements by joining captured LLM costs with revenue data, providing accurate gross margin calculations for strategic decision-making and investor updates.
• •Customer success management monitors account profitability metrics to identify at-risk customers with negative margins, enabling proactive retention efforts and strategic pricing adjustments to improve customer lifetime value.
• •Engineering teams detect anomalous spending patterns through automated monitoring that alerts when customer usage deviates significantly from baselines, preventing runaway LLM loops before they generate unexpected invoice surprises.
• •Product management simulates pricing changes against historical usage data to forecast revenue impact and identify customers who might churn under new pricing models, supporting data-driven monetization strategy development.
• •Vendor management reconciles monthly invoices from multiple AI providers to verify billing accuracy, identify cost savings opportunities, and maintain transparency across complex multi-vendor AI infrastructure environments.
• •Financial planning analyzes per-customer unit economics to optimize resource allocation, identify profitable customer segments, and make informed decisions about product development and market focus areas.

Who is this for?

TokenOps targets AI-native companies building products with LLM integration, SaaS businesses incorporating AI features into their platforms, and enterprise teams developing internal AI applications. Specifically designed for finance leaders, product managers, engineering teams, and customer success managers in organizations that need to track the profitability of AI usage across their customer base and manage multi-vendor AI infrastructure costs effectively.