Configure your Splunk Observability Cloud account to collect Azure OpenAI metrics

Learn how to configure your Splunk Observability Cloud account to collect Azure OpenAI metrics.

You can monitor the performance of Azure OpenAI applications by configuring your Azure OpenAI applications to send metrics to Splunk Observability Cloud. This solution creates a cloud connection in your Splunk Observability Cloud account that collects metrics from Azure application insights.

Complete the following steps to collect metrics from your Azure OpenAI applications.

  1. Connect Azure to Splunk Observability Cloud. For more information on the connection methods and instructions for each method, see Available options to connect with Azure.
    Note: If you connect with Azure using the guided setup, the Select the data to import page includes the option to import data from All Azure services or Specified Azure services only. If you select Specified Azure services only, select Cognitive Services from the Specified services drop-down menu to ensure that Azure OpenAI metrics are ingested.
  2. To monitor Azure OpenAI metrics with Splunk Observability Cloud, run your applications that use Azure OpenAI models.

メトリクスと属性

Azure OpenAI で使用可能なメトリクスを確認します。

Azure OpenAI アプリケーションでは以下のメトリクス、リソース属性が利用できます。
メトリクス名 タイプ ユニット（Units） 説明 ディメンション
ProcessedPromptTokens counter count OpenAI モデルで処理された（入力）プロンプトトークンの数。

  • ApiName

  • ModelDeploymentName

  • FeatureName

  • UsageChannel

  • Region

  • ModelVersion
GeneratedTokens

counter

 count OpenAI モデルで生成（出力）されたトークンの数。

  • ApiName

  • ModelDeploymentName

  • FeatureName

  • UsageChannel

  • Region

  • ModelVersion
AzureOpenAIRequests counter count Azure OpenAI サービスへの呼び出し数。

  • ApiName

  • OperationName

  • Region

  • ModelDeploymentName

  • ModelName

  • ModelVersion

  • StatusCode
AzureOpenAITimeToResponse ヒストグラム ミリ秒 ユーザーがプロンプトを送信してから最初の応答が表示されるまでにかかった時間。

  • ApiName

  • OperationName

  • Region

  • ModelDeploymentName

  • ModelName

  • ModelVersion

  • StatusCode
AzureOpenAIAvailabilityRate gauge percent 次の計算式で求められる可用性の割合：（総呼び出し数 - サーバーエラー数）÷ 総呼び出し数サーバーエラーには 500 秒以上の HTTP 応答が含まれます。

  • ApiName

  • OperationName

  • Region

  • ModelDeploymentName

  • ModelName

  • ModelVersion

  • StatusCode

AzureOpenAITokenPerSecond

 gauge count 特定の Azure OpenAI モデル応答の生成速度。

  • Region

  • ModelDeploymentName

  • ModelName

  • ModelVersion
AzureOpenAIContextTokensCacheMatchRate gauge percent キャッシュに到達したプロンプトトークンの割合。

  • Region

  • ModelDeploymentName

  • ModelName

  • ModelVersion

Next steps

How to monitor your AI components after you set up Observability for AI.

After you set up data collection from supported AI components to Splunk Observability Cloud, the data populates built-in experiences that you can use to monitor and troubleshoot your AI components.

The following table describes the tools you can use to monitor and troubleshoot your AI components.
Monitoring tool Use this tool to Link to documentation
Built-in navigators Orient and explore different layers of your AI tech stack.
Built-in dashboards Assess service, endpoint, and system health at a glance.
Splunk Application Performance Monitoring (APM) service map and trace view View all of your LLM service dependency graphs and user interactions in the service map or trace view.

Splunk APM を使用して LLM サービスをモニタリングする