SuperPenguin provides AI spend intelligence that aggregates and visualizes costs across all major AI providers in a single dashboard. It attributes spend to teams, customers, features, and even individual pull requests, offering reconciliation against invoices, spend alerts via Slack/email/Discord, and per‑request observability without code changes. The platform integrates with over 100 providers through LiteLLM and supports direct connections to tools like Cursor and GitHub.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations using generative AI services struggle to monitor and attribute the costs of dozens of providers across multiple teams, customers, and product features, leading to hidden charges, billing errors, and difficulty assessing ROI.
Solution
SuperPenguin offers an AI spend intelligence platform that aggregates usage data from over 100 generative AI providers—including OpenAI, Anthropic, Gemini, and AWS Bedrock—into a single dashboard. The system reconciles tracked spend with actual invoices, flags billing anomalies, and provides real‑time alerts via Slack, email, or Discord. By attaching metadata to each request through lightweight Python or TypeScript SDKs, SuperPenguin attributes costs to specific customers, features, teams, and even individual pull requests in Cursor or GitHub, delivering per‑request cost visibility. Users can slice spend by provider, model, project, or engineer, forecast future expenses, and set custom spend thresholds or spike detectors. The platform requires no code changes to the underlying provider clients, enabling immediate deployment for finance, engineering, and product leaders.
Target Audience
Primary customers are finance and engineering teams in enterprises that consume generative AI APIs, as well as product managers and engineering leaders who need detailed cost attribution for features and projects.
Features
- Unified dashboard consolidating usage and spend from 100+ AI providers in one view
- Automatic reconciliation of tracked spend against provider invoices to identify hidden charges
- Real‑time spend alerts with configurable thresholds sent to Slack, email, or Discord
- Per‑request cost attribution via Python and TypeScript SDKs, supporting metadata for customer, feature, team, and environment
- Pull‑request level cost mapping for Cursor and GitHub, showing dollar cost per merged PR and per engineer
- Forecasting and trend analytics for total spend, token volume, and provider‑specific usage
- No‑code integration: SDK wraps existing provider clients without changing base URLs