Llm.report: An Analytics Dashboard for OpenAI API

Llm.report is an analytics dashboard designed to monitor and analyze the costs associated with using the OpenAI API. It offers valuable insights into the usage and expenses of different OpenAI models, providing features such as cost breakdown, live API logs, caching, cost forecasting, and an alerting system.

Llm.report Features

🔍 Cost Breakdown by Model: Analyze and track the cost contribution of each AI model.
📊 Live API Logs & Analytics: Search and analyze historical prompts and completions in real-time.
🔁 Built-in Caching: Reduce costs by eliminating duplicate API calls.
📈 Cost Forecasting: Predict future spend based on historical data.
🔔 Alerting System: Receive instant Slack and email notifications about API usage and cost changes.

Use Cases

👀 Monitoring and optimizing OpenAI API costs: Keep track of expenses and identify areas for cost reduction.
📅 Analyzing historical API usage patterns and trends: Gain insights into usage patterns to make informed decisions.
💰 Forecasting future spend for budget planning: Plan and allocate resources based on predicted costs.

Conclusion

Llm.report offers a user-friendly interface and powerful features to help users optimize their OpenAI API usage, reduce costs, and make data-driven decisions for their AI projects. With its cost breakdown, live API logs, caching, cost forecasting, and alerting system, Llm.report provides valuable insights and tools for effective cost management.

FAQ

Q: How can Llm.report help in reducing OpenAI API costs?

A: Llm.report offers a cost breakdown feature that allows users to analyze and track the cost contribution of each AI model, helping identify areas for cost reduction.

Q: Can I analyze historical API usage patterns with Llm.report?

A: Yes, Llm.report provides live API logs and analytics, allowing users to search and analyze historical prompts and completions in real-time.

Q: Does Llm.report offer cost forecasting capabilities?

A: Absolutely! Llm.report can predict future spend based on historical data, enabling users to plan and allocate resources effectively.

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