--- title: Amazon Rekognition description: |- Amazon Rekognition makes it easy to add image and video analysis to your applications. breadcrumbs: Docs > Integrations > Amazon Rekognition --- > For the complete documentation index, see [llms.txt](https://docs.datadoghq.com/llms.txt). # Amazon Rekognition Integration version2.0.0 {% callout %} # Important note for users on the following Datadog sites: us2.ddog-gov.com {% alert level="info" %} To find out if this integration is available in your organization, see your [Datadog Integrations](https://app.datadoghq.com/integrations) page or ask your organization administrator. To initiate an exception request to enable this integration for your organization, email [support@ddog-gov.com](mailto:support@ddog-gov.com). {% /alert %} {% /callout %} ## Overview{% #overview %} Amazon Rekognition makes it easy to add image and video analysis to your applications. You just provide an image or video to the Rekognition API, and the service can identify objects, people, text, scenes, and activities. Enable this integration to see all your Rekognition metrics in Datadog. ## Setup{% #setup %} ### Installation{% #installation %} If you haven't already, set up the [Amazon Web Services integration](https://docs.datadoghq.com/integrations/amazon_web_services.md) first. ### Metric collection{% #metric-collection %} 1. In the [AWS integration page](https://app.datadoghq.com/integrations/amazon-web-services), ensure that `Rekognition` is enabled under the `Metric Collection` tab. 1. Install the [Datadog - Amazon Rekognition integration](https://app.datadoghq.com/integrations/amazon-rekognition). ### Log collection{% #log-collection %} #### Enable logging{% #enable-logging %} Configure Amazon Rekognition to send logs either to a S3 bucket or to CloudWatch. **Note**: If you log to a S3 bucket, make sure that `amazon_rekognition` is set as *Target prefix*. #### Send logs to Datadog{% #send-logs-to-datadog %} 1. If you haven't already, set up the [Datadog Forwarder Lambda function](https://docs.datadoghq.com/logs/guide/forwarder.md). 1. Once the Lambda function is installed, manually add a trigger on the S3 bucket or CloudWatch log group that contains your Amazon Rekognition logs in the AWS console: - [Add a manual trigger on the S3 bucket](https://docs.datadoghq.com/logs/guide/send-aws-services-logs-with-the-datadog-lambda-function.md#collecting-logs-from-s3-buckets) - [Add a manual trigger on the CloudWatch Log Group](https://docs.datadoghq.com/logs/guide/send-aws-services-logs-with-the-datadog-lambda-function.md#collecting-logs-from-cloudwatch-log-group) ## Data Collected{% #data-collected %} ### Metrics{% #metrics %} | | | | | **aws.rekognition.call_count**(count) | The average number of specified operations performed in your account. | | **aws.rekognition.call_count.sum**(count) | The sum of the number of specified operations performed in your account. | | **aws.rekognition.desired_inference_units**(gauge) | The number of inference units that Rekognition is scaling up or down to. | | **aws.rekognition.detected_face_count**(count) | The average number of faces detected with the IndexFaces or DetectFaces operation. | | **aws.rekognition.detected_face_count.sum**(count) | The sum of the number of faces detected with the IndexFaces or DetectFaces operation. | | **aws.rekognition.detected_label_count**(count) | The average number of labels detected with the DetectLabels operation. | | **aws.rekognition.detected_label_count.sum**(count) | The sum of the number of labels detected with the DetectLabels operation. | | **aws.rekognition.in_service_inference_units**(gauge) | The number of inference units that the model is using. | | **aws.rekognition.max_inference_units**(gauge) | The maximum number of inference units specified during the StartProjectVersion request. | | **aws.rekognition.min_inference_units**(gauge) | The minimum number of inference units specified during the StartProjectVersion request. | | **aws.rekognition.response_time**(count) | The time in milliseconds for Rekognition to compute the response.*Shown as millisecond* | | **aws.rekognition.response_time.samplecount**(count) | The number of datapoints used to compute the response time. | | **aws.rekognition.response_time.sum**(count) | The sum of the time in milliseconds for Rekognition to compute the response.*Shown as millisecond* | | **aws.rekognition.server_error_count**(count) | The number of server errors. | | **aws.rekognition.server_error_count.sum**(count) | The sum of the number of server errors. | | **aws.rekognition.successful_request_count**(count) | The average number of successful requests. | | **aws.rekognition.successful_request_count.sum**(count) | The sum of the number of successful requests. | | **aws.rekognition.throttled_count**(count) | The average number of throttled requests. Rekognition throttles a request when it receives more requests than the limit of transactions per second set for your account. | | **aws.rekognition.throttled_count.sum**(count) | The sum of the number of throttled requests. Rekognition throttles a request when it receives more requests than the limit of transactions per second set for your account. | | **aws.rekognition.user_error_count**(count) | The average number of user errors (invalid parameters - invalid image - no permission etc). | | **aws.rekognition.user_error_count.sum**(count) | The sum of the number of user errors (invalid parameters - invalid image - no permission etc). | ### Events{% #events %} The Amazon Rekognition integration does not include any events. ### Service Checks{% #service-checks %} The Amazon Rekognition integration does not include any service checks. ## Troubleshooting{% #troubleshooting %} Need help? Contact [Datadog support](https://docs.datadoghq.com/help/).