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Monitoring

DebuggAI provides comprehensive monitoring capabilities for your AI applications, helping you track performance, costs, and behavior in real-time.

What is AI Monitoring?​

AI monitoring is the process of tracking and analyzing the performance and behavior of AI models and applications. Unlike traditional application monitoring, AI monitoring focuses on the unique aspects of AI systems, such as:

  • Prompt Quality: How well your prompts are designed to elicit the desired responses
  • Response Quality: How accurate, relevant, and useful the model's responses are
  • Model Performance: How quickly the model responds and how efficiently it uses tokens
  • Cost Efficiency: How much you're spending on AI API calls and whether you're getting good value

Key Monitoring Features​

DebuggAI offers several key monitoring features:

Request Monitoring​

Track every request to your AI models, including:

  • Prompts: The input provided to the model
  • Responses: The output generated by the model
  • Metadata: Additional information about the request, such as user ID, session ID, etc.
  • Performance Metrics: Latency, token usage, and other performance indicators
  • Cost: The cost of each request based on the model and token usage

Real-Time Dashboards​

View real-time dashboards that provide insights into your AI application's performance:

  • Request Volume: Track the number of requests over time
  • Latency: Monitor how quickly your models are responding
  • Token Usage: Track how many tokens are being used for prompts and responses
  • Cost: Monitor the cost of your AI API calls
  • Error Rates: Track how often your requests are failing

Alerts and Notifications​

Set up alerts and notifications to be notified when important events occur:

  • Error Rate Spikes: Get notified when your error rate exceeds a threshold
  • Latency Issues: Get notified when your models are responding slowly
  • Cost Thresholds: Get notified when you exceed a cost threshold
  • Unusual Behavior: Get notified when your models exhibit unusual behavior

Setting Up Monitoring​

Setting up monitoring with DebuggAI is easy:

  1. Install the DebuggAI SDK for your programming language
  2. Initialize the SDK with your API key
  3. Wrap your AI client with the DebuggAI monitoring wrapper
  4. Start making requests to your AI models

For detailed instructions, see the Quick Start Guide.

Best Practices​

Here are some best practices for monitoring your AI applications:

  • Add Context: Add metadata to your requests to provide context for debugging
  • Set Up Alerts: Set up alerts for important metrics to be notified of issues
  • Monitor Costs: Keep an eye on your costs to avoid unexpected bills
  • Track User Feedback: Correlate user feedback with model performance to identify areas for improvement
  • Review Regularly: Regularly review your monitoring data to identify trends and patterns

Next Steps​

Now that you understand how monitoring works in DebuggAI, you can:

  • Learn about Debugging to see how to identify and fix issues in your AI applications
  • Explore Analytics to see how to analyze your AI application's performance and behavior
  • Check out Request Tracing to learn more about tracking requests in detail