diff --git a/mlflow/models/container/__init__.py b/mlflow/models/container/__init__.py
index efaa16f0ba292..3e55d96eaddac 100644
--- a/mlflow/models/container/__init__.py
+++ b/mlflow/models/container/__init__.py
@@ -38,6 +38,8 @@
 DISABLE_NGINX = "DISABLE_NGINX"
 ENABLE_MLSERVER = "ENABLE_MLSERVER"
 
+SERVING_ENVIRONMENT = "SERVING_ENVIRONMENT"
+
 
 def _init(cmd):
     """
diff --git a/mlflow/sagemaker/__init__.py b/mlflow/sagemaker/__init__.py
index a813cd8360468..df443bf1d2d6f 100644
--- a/mlflow/sagemaker/__init__.py
+++ b/mlflow/sagemaker/__init__.py
@@ -22,7 +22,7 @@
 from mlflow.utils.annotations import experimental
 from mlflow.utils.file_utils import TempDir
 from mlflow.models.container import SUPPORTED_FLAVORS as SUPPORTED_DEPLOYMENT_FLAVORS
-from mlflow.models.container import DEPLOYMENT_CONFIG_KEY_FLAVOR_NAME
+from mlflow.models.container import DEPLOYMENT_CONFIG_KEY_FLAVOR_NAME, SERVING_ENVIRONMENT
 
 
 DEFAULT_IMAGE_NAME = "mlflow-pyfunc"
@@ -41,6 +41,8 @@
 DEFAULT_SAGEMAKER_INSTANCE_TYPE = "ml.m4.xlarge"
 DEFAULT_SAGEMAKER_INSTANCE_COUNT = 1
 
+SAGEMAKER_SERVING_ENVIRONMENT = "SageMaker"
+
 _logger = logging.getLogger(__name__)
 
 _full_template = "{account}.dkr.ecr.{region}.amazonaws.com/{image}:{version}"
@@ -1208,7 +1210,10 @@ def _get_deployment_config(flavor_name):
     """
     :return: The deployment configuration as a dictionary
     """
-    deployment_config = {DEPLOYMENT_CONFIG_KEY_FLAVOR_NAME: flavor_name}
+    deployment_config = {
+        DEPLOYMENT_CONFIG_KEY_FLAVOR_NAME: flavor_name,
+        SERVING_ENVIRONMENT: SAGEMAKER_SERVING_ENVIRONMENT,
+    }
     return deployment_config
 
 
diff --git a/tests/sagemaker/test_deployment.py b/tests/sagemaker/test_deployment.py
index 28f91a75d8127..705bff2255a77 100644
--- a/tests/sagemaker/test_deployment.py
+++ b/tests/sagemaker/test_deployment.py
@@ -216,7 +216,7 @@ def test_attempting_to_deploy_in_asynchronous_mode_without_archiving_throws_exce
 
 @pytest.mark.large
 @mock_sagemaker_aws_services
-def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_from_local(
+def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local(
     pretrained_model, sagemaker_client
 ):
     app_name = "test-app"
@@ -245,11 +245,18 @@ def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_from_loca
     assert app_name in [
         endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
     ]
+    model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][
+        "Environment"
+    ]
+    assert model_environment == {
+        "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
+        "SERVING_ENVIRONMENT": "SageMaker",
+    }
 
 
 @pytest.mark.large
 @mock_sagemaker_aws_services
-def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_from_local(
+def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_local(
     pretrained_model, sagemaker_client
 ):
     app_name = "test-app"
@@ -288,11 +295,18 @@ def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_from_
     assert app_name in [
         endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
     ]
+    model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][
+        "Environment"
+    ]
+    assert model_environment == {
+        "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
+        "SERVING_ENVIRONMENT": "SageMaker",
+    }
 
 
 @pytest.mark.large
 @mock_sagemaker_aws_services
-def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_from_s3(
+def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3(
     pretrained_model, sagemaker_client
 ):
     local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
@@ -328,11 +342,18 @@ def test_deploy_creates_sagemaker_and_s3_resources_with_expected_names_from_s3(
     assert app_name in [
         endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
     ]
+    model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][
+        "Environment"
+    ]
+    assert model_environment == {
+        "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
+        "SERVING_ENVIRONMENT": "SageMaker",
+    }
 
 
 @pytest.mark.large
 @mock_sagemaker_aws_services
-def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_from_s3(
+def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_and_env_from_s3(
     pretrained_model, sagemaker_client
 ):
     local_model_path = _download_artifact_from_uri(pretrained_model.model_uri)
@@ -373,6 +394,13 @@ def test_deploy_cli_creates_sagemaker_and_s3_resources_with_expected_names_from_
     assert app_name in [
         endpoint["EndpointName"] for endpoint in sagemaker_client.list_endpoints()["Endpoints"]
     ]
+    model_environment = sagemaker_client.describe_model(ModelName=model_name)["PrimaryContainer"][
+        "Environment"
+    ]
+    assert model_environment == {
+        "MLFLOW_DEPLOYMENT_FLAVOR_NAME": "python_function",
+        "SERVING_ENVIRONMENT": "SageMaker",
+    }
 
 
 @pytest.mark.large