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Each row represents a unique request, identified by an ID and version number. Use these to track specific invocations of your endpoint.
Indicates the origin of the request (e.g., “Replicate App”). This helps you identify which integrations or applications are using your endpoint.
Shows the current state of each request. “Complete” indicates successful processing. Monitor this column for any non-complete statuses that may require attention.
  • Total Time Taken: The overall processing duration for each request. - LLM Total Token: The number of tokens processed by the language model. - LLM Time Taken: Time specifically spent on language model operations. Use these metrics to identify performance bottlenecks or unexpected behavior.
The timestamp when the request was initiated. This helps in tracking the timeline of requests and identifying patterns over time.

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Detailed Request Information

When monitoring your endpoint performance, you can expand individual requests to access detailed information. This feature provides in-depth insights into each request, helping you analyze performance, troubleshoot issues, and understand how your endpoint is being used. To view detailed information for a specific request:
  1. Locate the request you want to examine in your monitoring dashboard.
  2. Click on the request to expand its details.
  3. You’ll see a tabbed interface with the following sections:

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Metrics

View detailed performance data for the request, including:
  • Time taken to process the request
  • LLM request ID
  • Request status
Use this tab for in-depth performance analysis and to identify potential bottlenecks.

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Metadata

Access additional context about the request. This information is helpful for understanding the request’s parameters and overall configuration.

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Options

Review the configuration settings used for this specific request. This tab is useful to ensure the endpoint was called with the expected options and to verify any custom settings applied.

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Arguments

Examine the input parameters provided to the endpoint for this request. This tab is particularly useful for:
  • Reproducing issues
  • Understanding how your endpoint is being used in practice
  • Verifying the correct inputs were sent to the endpoint
To navigate between these sections, simply click on the desired tab in the left sidebar. The main content area on the right will update to display the selected information. By leveraging this detailed view, you can gain valuable insights into your endpoint’s performance and usage patterns, enabling you to optimize your AI applications more effectively.

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Debugging Techniques

When investigating issues or optimizing performance:
  1. Analyze Trends: Look for patterns in the Total Time Taken or LLM Time Taken across requests. Sudden increases may indicate performance degradation.
  2. Compare Versions: Use the version history to identify if recent changes have impacted performance or reliability.
  3. Inspect Failures: For any non-”Complete” statuses, expand the request details to examine metrics, options, and arguments for clues about what went wrong.
  4. Token Usage: Monitor the LLM Total Token to ensure it aligns with expectations and to manage costs effectively.
  5. Source Analysis: If issues are isolated to specific sources, it may indicate problems with particular integrations or client implementations.

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Advanced Monitoring

For more in-depth analysis:

LLM Logs

Access detailed logs of language model operations for advanced debugging. These logs provide insights into the internal workings of the LLM during request processing.

SDK & Integration

Consult the SDK documentation for implementation details and best practices for client integration. Proper SDK usage can prevent many common issues and optimize endpoint interaction.
By leveraging these dashboard features and following these debugging strategies, you can effectively monitor your endpoint’s health, quickly identify and resolve issues, and ensure optimal performance.