API Usage
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API Usage is an innovative open-source, self-hostable proxy solution designed to empower organizations and individual developers with unparalleled transparency into their Large Language Model (LLM) API consumption. It meticulously tracks and analyzes API calls, primarily for OpenAI and other LLMs, providing granular insights into usage patterns, associated costs, and model performance. By offering full control over data and infrastructure, API Usage ensures privacy, compliance, and significant optimization of AI spending, making it an indispensable tool for efficient LLM integration and management.
Why was this tool discontinued?
Automatically marked inactive after 7 consecutive failed health checks (last error: DNS resolution failed)
What It Does
The tool functions as an intermediary proxy, intercepting and logging all API requests and responses to various LLM providers. It then processes this data to generate detailed analytics on token consumption, latency, error rates, and costs, broken down by project, user, and specific models. This self-hostable architecture ensures that all sensitive API usage data remains within the user's controlled environment.
Pricing
Pricing Plans
API Usage is entirely free and open-source, providing all features without any cost for self-hosting.
- Track usage by project, model, and time
- Cost visualization with charts
- Set spending limits and alerts
- Self-hostable
- Multi-user and multi-team support
- +2 more
Core Value Propositions
Cost Optimization & Control
Gain clear visibility into API spending, identify cost drivers, and implement limits to manage budgets effectively and prevent overruns.
Enhanced Data Privacy
Keep all sensitive API usage data within your own infrastructure, ensuring compliance with data governance policies and reducing third-party risk.
Operational Transparency
Understand exactly how, when, and by whom AI APIs are being utilized, fostering better resource allocation and accountability within teams.
Simplified Multi-Provider Management
Centralize tracking for various LLM providers through a single proxy, streamlining monitoring and reducing integration complexity.
Use Cases
Monitor Production Application Costs
Track real-time API usage and costs for LLM-powered applications in production, allowing for immediate identification of cost-inefficient models or prompts.
Budget Management for AI Projects
Set and enforce spending limits for different AI projects or teams, ensuring adherence to financial budgets and preventing unexpected overspending.
Departmental Cost Allocation
Allocate LLM API costs accurately to specific departments or teams based on their actual usage, facilitating internal chargebacks and financial planning.
Performance Debugging & Optimization
Utilize detailed request/response logs and latency metrics to debug problematic API calls and optimize prompt engineering for better performance and cost-efficiency.
Identify Usage Trends
Analyze historical usage data to identify peak usage times, popular models, and long-term consumption trends, informing capacity planning and strategy.
Evaluate Model Efficiency
Compare the token usage and cost-effectiveness of different LLM models for similar tasks, aiding in the selection of the most efficient models for various applications.
Technical Features & Integration
Comprehensive Usage Tracking
Monitors API calls, token consumption, and costs across OpenAI, Azure OpenAI, Anthropic, and Google Gemini (via LiteLLM) for detailed oversight.
Cost Visualization & Analysis
Generates interactive charts and dashboards to visualize expenses by project, model, and time, facilitating budget management and optimization.
Spending Limits & Alerts
Allows setting custom spending thresholds with real-time notifications for usage spikes, helping prevent unexpected costs.
Self-Hostable Architecture
Users can host the tool on their own infrastructure, ensuring complete data privacy and control over sensitive API usage information.
Multi-User & Team Support
Supports multiple users and teams, enabling granular tracking and management of API usage across different organizational units or projects.
Detailed Request Logging
Logs full request and response bodies, along with metadata like tokens and latency, providing deep insights for debugging and optimization.
Provider Agnostic Proxy
Functions as a unified proxy for various LLM providers, simplifying integration and offering a consistent interface for usage monitoring.
PostgreSQL Data Storage
Stores all collected usage data in a PostgreSQL database, providing a reliable and scalable backend for analytics and custom reporting.
Target Audience
This tool is ideal for developers, engineering teams, product managers, and finance departments within companies utilizing large language models. It caters to organizations that need to meticulously track, analyze, and control their OpenAI and other LLM API expenditures, especially those prioritizing data privacy and self-hosting capabilities. Startups, enterprises, and individual developers seeking cost optimization and usage transparency will find it highly beneficial.
Frequently Asked Questions
Yes, API Usage is completely free to use. Available plans include: Open Source.
The tool functions as an intermediary proxy, intercepting and logging all API requests and responses to various LLM providers. It then processes this data to generate detailed analytics on token consumption, latency, error rates, and costs, broken down by project, user, and specific models. This self-hostable architecture ensures that all sensitive API usage data remains within the user's controlled environment.
Key features of API Usage include: Comprehensive Usage Tracking: Monitors API calls, token consumption, and costs across OpenAI, Azure OpenAI, Anthropic, and Google Gemini (via LiteLLM) for detailed oversight.. Cost Visualization & Analysis: Generates interactive charts and dashboards to visualize expenses by project, model, and time, facilitating budget management and optimization.. Spending Limits & Alerts: Allows setting custom spending thresholds with real-time notifications for usage spikes, helping prevent unexpected costs.. Self-Hostable Architecture: Users can host the tool on their own infrastructure, ensuring complete data privacy and control over sensitive API usage information.. Multi-User & Team Support: Supports multiple users and teams, enabling granular tracking and management of API usage across different organizational units or projects.. Detailed Request Logging: Logs full request and response bodies, along with metadata like tokens and latency, providing deep insights for debugging and optimization.. Provider Agnostic Proxy: Functions as a unified proxy for various LLM providers, simplifying integration and offering a consistent interface for usage monitoring.. PostgreSQL Data Storage: Stores all collected usage data in a PostgreSQL database, providing a reliable and scalable backend for analytics and custom reporting..
API Usage is best suited for This tool is ideal for developers, engineering teams, product managers, and finance departments within companies utilizing large language models. It caters to organizations that need to meticulously track, analyze, and control their OpenAI and other LLM API expenditures, especially those prioritizing data privacy and self-hosting capabilities. Startups, enterprises, and individual developers seeking cost optimization and usage transparency will find it highly beneficial..
Gain clear visibility into API spending, identify cost drivers, and implement limits to manage budgets effectively and prevent overruns.
Keep all sensitive API usage data within your own infrastructure, ensuring compliance with data governance policies and reducing third-party risk.
Understand exactly how, when, and by whom AI APIs are being utilized, fostering better resource allocation and accountability within teams.
Centralize tracking for various LLM providers through a single proxy, streamlining monitoring and reducing integration complexity.
Track real-time API usage and costs for LLM-powered applications in production, allowing for immediate identification of cost-inefficient models or prompts.
Set and enforce spending limits for different AI projects or teams, ensuring adherence to financial budgets and preventing unexpected overspending.
Allocate LLM API costs accurately to specific departments or teams based on their actual usage, facilitating internal chargebacks and financial planning.
Utilize detailed request/response logs and latency metrics to debug problematic API calls and optimize prompt engineering for better performance and cost-efficiency.
Analyze historical usage data to identify peak usage times, popular models, and long-term consumption trends, informing capacity planning and strategy.
Compare the token usage and cost-effectiveness of different LLM models for similar tasks, aiding in the selection of the most efficient models for various applications.
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