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    REST Resource: projects.locations.endpoints

    Resource: Endpoint

    Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

    Fields
    name string

    Output only. The resource name of the Endpoint.

    displayName string

    Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.

    description string

    The description of the Endpoint.

    deployedModels[] object ( DeployedModel )

    Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.

    trafficSplit map (key: string, value: integer)

    A map from a DeployedModel's id to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel.

    If a DeployedModel's id is not listed in this map, then it receives no traffic.

    The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.

    etag string

    Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

    labels map (key: string, value: string)

    The labels with user-defined metadata to organize your endpoints.

    label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

    See https://goo.gl/xmQnxf for more information and examples of labels.

    createTime string ( Timestamp format)

    Output only. timestamp when this Endpoint was created.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    updateTime string ( Timestamp format)

    Output only. timestamp when this Endpoint was last updated.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    encryptionSpec object ( EncryptionSpec )

    Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.

    network string

    Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered.

    Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network.

    Only one of the fields, network or enablePrivateServiceConnect , can be set.

    Format : projects/{project}/global/networks/{network} . Where {project} is a project number, as in 12345 , and {network} is network name.

    enablePrivateServiceConnect
    (deprecated)     boolean

    Deprecated: If true, expose the Endpoint via private service connect.

    Only one of the fields, network or enablePrivateServiceConnect , can be set.

    privateServiceConnectConfig object ( PrivateServiceConnectConfig )

    Optional. Configuration for private service connect.

    network and privateServiceConnectConfig are mutually exclusive.

    modelDeploymentMonitoringJob string

    Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob . Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{modelDeploymentMonitoringJob}

    predictRequestResponseLoggingConfig object ( PredictRequestResponseLoggingConfig )

    Configures the request-response logging for online prediction.

    dedicatedEndpointEnabled boolean

    If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.

    dedicatedEndpointDns string

    Output only. DNS of the dedicated endpoint. Will only be populated if dedicatedEndpointEnabled is true. Depending on the features enabled, uid might be a random number or a string. For example, if fast_tryout is enabled, uid will be fasttryout. Format: https://{endpointId}.{region}-{uid}.prediction.vertexai.goog .

    clientConnectionConfig object ( ClientConnectionConfig )

    Configurations that are applied to the endpoint for online prediction.

    satisfiesPzs boolean

    Output only. reserved for future use.

    satisfiesPzi boolean

    Output only. reserved for future use.

    genAiAdvancedFeaturesConfig object ( GenAiAdvancedFeaturesConfig )

    Optional. Configuration for GenAiAdvancedFeatures. If the endpoint is serving GenAI models, advanced features like native RAG integration can be configured. Currently, only Model Garden models are supported.

    JSON representation 
     { 
 "name" 
 : 
 string 
 , 
 "displayName" 
 : 
 string 
 , 
 "description" 
 : 
 string 
 , 
 "deployedModels" 
 : 
 [ 
 { 
 object (  DeployedModel 
 
) 
 } 
 ] 
 , 
 "trafficSplit" 
 : 
 { 
 string 
 : 
 integer 
 , 
 ... 
 } 
 , 
 "etag" 
 : 
 string 
 , 
 "labels" 
 : 
 { 
 string 
 : 
 string 
 , 
 ... 
 } 
 , 
 "createTime" 
 : 
 string 
 , 
 "updateTime" 
 : 
 string 
 , 
 "encryptionSpec" 
 : 
 { 
 object (  EncryptionSpec 
 
) 
 } 
 , 
 "network" 
 : 
 string 
 , 
 "enablePrivateServiceConnect" 
 : 
 boolean 
 , 
 "privateServiceConnectConfig" 
 : 
 { 
 object (  PrivateServiceConnectConfig 
 
) 
 } 
 , 
 "modelDeploymentMonitoringJob" 
 : 
 string 
 , 
 "predictRequestResponseLoggingConfig" 
 : 
 { 
 object (  PredictRequestResponseLoggingConfig 
 
) 
 } 
 , 
 "dedicatedEndpointEnabled" 
 : 
 boolean 
 , 
 "dedicatedEndpointDns" 
 : 
 string 
 , 
 "clientConnectionConfig" 
 : 
 { 
 object (  ClientConnectionConfig 
 
) 
 } 
 , 
 "satisfiesPzs" 
 : 
 boolean 
 , 
 "satisfiesPzi" 
 : 
 boolean 
 , 
 "genAiAdvancedFeaturesConfig" 
 : 
 { 
 object (  GenAiAdvancedFeaturesConfig 
 
) 
 } 
 }

    DeployedModel

    A deployment of a Model. endpoints contain one or more DeployedModels.

    Fields
    id string

    Immutable. The id of the DeployedModel. If not provided upon deployment, Vertex AI will generate a value for this id.

    This value should be 1-10 characters, and valid characters are /[0-9]/ .

    model string

    The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint.

    The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.

    gdcConnectedModel string

    GDC pretrained / Gemini model name. The model name is a plain model name, e.g. gemini-1.5-flash-002.

    modelVersionId string

    Output only. The version id of the model that is deployed.

    displayName string

    The display name of the DeployedModel. If not provided upon creation, the Model's displayName is used.

    createTime string ( Timestamp format)

    Output only. timestamp when the DeployedModel was created.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    explanationSpec object ( ExplanationSpec )

    Explanation configuration for this DeployedModel.

    When deploying a Model using EndpointService.DeployModel , this value overrides the value of Model.explanation_spec . All fields of explanationSpec are optional in the request. If a field of explanationSpec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanationSpec will be used for the explanation configuration.

    disableExplanations boolean

    If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanationSpec .

    disableContainerLogging boolean

    For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing .

    user can disable container logging by setting this flag to true.

    enableAccessLogging boolean

    If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request.

    Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.

    privateEndpoints object ( PrivateEndpoints )

    Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.

    fasterDeploymentConfig object ( FasterDeploymentConfig )

    Configuration for faster model deployment.

    status object ( Status )

    Output only. Runtime status of the deployed model.

    systemLabels map (key: string, value: string)

    System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.

    checkpointId string

    The checkpoint id of the model.

    speculativeDecodingSpec object ( SpeculativeDecodingSpec )

    Optional. Spec for configuring speculative decoding.

    prediction_resources Union type
    The prediction (for example, the machine) resources that the DeployedModel uses. The user is billed for the resources (at least their minimal amount) even if the DeployedModel receives no traffic. Not all Models support all resources types. See Model.supported_deployment_resources_types . Required except for Large Model Deploy use cases. prediction_resources can be only one of the following:
    dedicatedResources object ( DedicatedResources )

    A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.

    automaticResources object ( AutomaticResources )

    A description of resources that to large degree are decided by Vertex AI, and require only a modest additional configuration.

    sharedResources string

    The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deploymentResourcePool}

    JSON representation 
     { 
 "id" 
 : 
 string 
 , 
 "model" 
 : 
 string 
 , 
 "gdcConnectedModel" 
 : 
 string 
 , 
 "modelVersionId" 
 : 
 string 
 , 
 "displayName" 
 : 
 string 
 , 
 "createTime" 
 : 
 string 
 , 
 "explanationSpec" 
 : 
 { 
 object (  ExplanationSpec 
 
) 
 } 
 , 
 "disableExplanations" 
 : 
 boolean 
 , 
 "serviceAccount" 
 : 
 string 
 , 
 "disableContainerLogging" 
 : 
 boolean 
 , 
 "enableAccessLogging" 
 : 
 boolean 
 , 
 "privateEndpoints" 
 : 
 { 
 object (  PrivateEndpoints 
 
) 
 } 
 , 
 "fasterDeploymentConfig" 
 : 
 { 
 object (  FasterDeploymentConfig 
 
) 
 } 
 , 
 "status" 
 : 
 { 
 object (  Status 
 
) 
 } 
 , 
 "systemLabels" 
 : 
 { 
 string 
 : 
 string 
 , 
 ... 
 } 
 , 
 "checkpointId" 
 : 
 string 
 , 
 "speculativeDecodingSpec" 
 : 
 { 
 object (  SpeculativeDecodingSpec 
 
) 
 } 
 , 
 // prediction_resources 
 "dedicatedResources" 
 : 
 { 
 object (  DedicatedResources 
 
) 
 } 
 , 
 "automaticResources" 
 : 
 { 
 object (  AutomaticResources 
 
) 
 } 
 , 
 "sharedResources" 
 : 
 string 
 // Union type 
 }

    PrivateEndpoints

    PrivateEndpoints proto is used to provide paths for users to send requests privately. To send request via private service access, use predictHttpUri, explainHttpUri or healthHttpUri. To send request via private service connect, use serviceAttachment.

    Fields
    predictHttpUri string

    Output only. Http(s) path to send prediction requests.

    explainHttpUri string

    Output only. Http(s) path to send explain requests.

    healthHttpUri string

    Output only. Http(s) path to send health check requests.

    serviceAttachment string

    Output only. The name of the service attachment resource. Populated if private service connect is enabled.

    JSON representation 
     { 
 "predictHttpUri" 
 : 
 string 
 , 
 "explainHttpUri" 
 : 
 string 
 , 
 "healthHttpUri" 
 : 
 string 
 , 
 "serviceAttachment" 
 : 
 string 
 }

    FasterDeploymentConfig

    Configuration for faster model deployment.

    Fields
    fastTryoutEnabled boolean

    If true, enable fast tryout feature for this deployed model.

    JSON representation 
     { 
 "fastTryoutEnabled" 
 : 
 boolean 
 }

    Status

    Runtime status of the deployed model.

    Fields
    message string

    Output only. The latest deployed model's status message (if any).

    lastUpdateTime string ( Timestamp format)

    Output only. The time at which the status was last updated.

    Uses RFC 3339, where generated output will always be Z-normalized and use 0, 3, 6 or 9 fractional digits. Offsets other than "Z" are also accepted. Examples: "2014-10-02T15:01:23Z" , "2014-10-02T15:01:23.045123456Z" or "2014-10-02T15:01:23+05:30" .

    availableReplicaCount integer

    Output only. The number of available replicas of the deployed model.

    JSON representation 
     { 
 "message" 
 : 
 string 
 , 
 "lastUpdateTime" 
 : 
 string 
 , 
 "availableReplicaCount" 
 : 
 integer 
 }

    SpeculativeDecodingSpec

    Configuration for Speculative Decoding.

    Fields
    speculativeTokenCount integer

    The number of speculative tokens to generate at each step.

    speculation Union type
    The type of speculation method to use. speculation can be only one of the following:
    draftModelSpeculation object ( DraftModelSpeculation )

    draft model speculation.

    ngramSpeculation object ( NgramSpeculation )

    N-Gram speculation.

    JSON representation 
     { 
 "speculativeTokenCount" 
 : 
 integer 
 , 
 // speculation 
 "draftModelSpeculation" 
 : 
 { 
 object (  DraftModelSpeculation 
 
) 
 } 
 , 
 "ngramSpeculation" 
 : 
 { 
 object (  NgramSpeculation 
 
) 
 } 
 // Union type 
 }

    DraftModelSpeculation

    Draft model speculation works by using the smaller model to generate candidate tokens for speculative decoding.

    Fields
    draftModel string

    Required. The resource name of the draft model.

    JSON representation 
     { 
 "draftModel" 
 : 
 string 
 }

    NgramSpeculation

    N-Gram speculation works by trying to find matching tokens in the previous prompt sequence and use those as speculation for generating new tokens.

    Fields
    ngramSize integer

    The number of last N input tokens used as ngram to search/match against the previous prompt sequence. This is equal to the N in N-Gram. The default value is 3 if not specified.

    JSON representation 
     { 
 "ngramSize" 
 : 
 integer 
 }

    PredictRequestResponseLoggingConfig

    Configuration for logging request-response to a BigQuery table.

    Fields
    enabled boolean

    If logging is enabled or not.

    samplingRate number

    Percentage of requests to be logged, expressed as a fraction in range(0,1].

    bigqueryDestination object ( BigQueryDestination )

    BigQuery table for logging. If only given a project, a new dataset will be created with name logging_<endpoint-display-name>_<endpoint-id> where will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores). If no table name is given, a new table will be created with name request_response_logging

    JSON representation 
     { 
 "enabled" 
 : 
 boolean 
 , 
 "samplingRate" 
 : 
 number 
 , 
 "bigqueryDestination" 
 : 
 { 
 object (  BigQueryDestination 
 
) 
 } 
 }

    ClientConnectionConfig

    Configurations (e.g. inference timeout) that are applied on your endpoints.

    Fields
    inferenceTimeout string ( Duration format)

    Customizable online prediction request timeout.

    A duration in seconds with up to nine fractional digits, ending with ' s '. Example: "3.5s" .

    JSON representation 
     { 
 "inferenceTimeout" 
 : 
 string 
 }

    GenAiAdvancedFeaturesConfig

    Configuration for GenAiAdvancedFeatures.

    Fields
    ragConfig object ( RagConfig )

    Configuration for Retrieval Augmented Generation feature.

    JSON representation 
     { 
 "ragConfig" 
 : 
 { 
 object (  RagConfig 
 
) 
 } 
 }

    RagConfig

    Configuration for Retrieval Augmented Generation feature.

    Fields
    enableRag boolean

    If true, enable Retrieval Augmented Generation in ChatCompletion request. Once enabled, the endpoint will be identified as GenAI endpoint and Arthedain router will be used.

    JSON representation 
     { 
 "enableRag" 
 : 
 boolean 
 }

    Methods

    create

    		Creates an Endpoint.

    delete

    		Deletes an Endpoint.

    deployModel

    		Deploys a Model into this Endpoint, creating a DeployedModel within it.

    directPredict

    		Perform an unary online prediction request to a gRPC model server for Vertex first-party products and frameworks.

    directRawPredict

    		Perform an unary online prediction request to a gRPC model server for custom containers.

    explain

    		Perform an online explanation.

    get

    		Gets an Endpoint.

    list

    		Lists Endpoints in a Location.

    mutateDeployedModel

    		Updates an existing deployed model.

    patch

    		Updates an Endpoint.

    predict

    		Perform an online prediction.

    predictLongRunning

    rawPredict

    		Perform an online prediction with an arbitrary HTTP payload.

    serverStreamingPredict

    		Perform a server-side streaming online prediction request for Vertex LLM streaming.

    streamRawPredict

    		Perform a streaming online prediction with an arbitrary HTTP payload.

    undeployModel

    		Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using.

    update

    		Updates an Endpoint with a long running operation.
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