Configure AI Proxy for Claude Code with Google Vertex AIv3.8+
v3.13+Set up the AI Proxy plugin to work with Claude Code, using Google Vertex AI with Gemini 2.5 Flash model. For a detailed guide on how to use Google Vertex AI with Claude Code see this guide
Prerequisites
-
Google Cloud Platform account
-
Vertex AI API enabled
-
Service account with Vertex AI permissions
Environment variables
-
GCP_SERVICE_ACCOUNT_JSON: The GCP service account JSON credentials for authentication. -
VERTEX_API_ENDPOINT: The Vertex AI API endpoint (e.g., us-east5-aiplatform.googleapis.com). -
GCP_PROJECT_ID: The GCP project ID. -
GCP_LOCATION_ID: The GCP location/region ID (e.g., us-east5).
Add this section to your kong.yaml configuration file:
_format_version: "3.0"
plugins:
- name: ai-proxy-advanced
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: ${{ env "DECK_GCP_SERVICE_ACCOUNT_JSON" }}
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: ${{ env "DECK_VERTEX_API_ENDPOINT" }}
project_id: ${{ env "DECK_GCP_PROJECT_ID" }}
location_id: ${{ env "DECK_GCP_LOCATION_ID" }}
Make the following request:
curl -i -X POST http://localhost:8001/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make the following request:
curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/plugins/ \
--header "accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $KONNECT_TOKEN" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane.
See the Konnect API reference to learn about region-specific URLs and personal access tokens.
echo "
apiVersion: configuration.konghq.com/v1
kind: KongClusterPlugin
metadata:
name: ai-proxy-advanced
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
konghq.com/tags: ''
labels:
global: 'true'
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: '$GCP_SERVICE_ACCOUNT_JSON'
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: '$VERTEX_API_ENDPOINT'
project_id: '$GCP_PROJECT_ID'
location_id: '$GCP_LOCATION_ID'
plugin: ai-proxy-advanced
" | kubectl apply -f -
Prerequisite: Configure your Personal Access Token
terraform {
required_providers {
konnect = {
source = "kong/konnect"
}
}
}
provider "konnect" {
personal_access_token = "$KONNECT_TOKEN"
server_url = "https://us.api.konghq.com/"
}
Add the following to your Terraform configuration to create a Konnect Gateway Plugin:
resource "konnect_gateway_plugin_ai_proxy_advanced" "my_ai_proxy_advanced" {
enabled = true
config = {
llm_format = "anthropic"
targets = [
{
route_type = "llm/v1/chat"
logging = {
log_payloads = false
log_statistics = true
}
auth = {
allow_override = false
gcp_use_service_account = true
gcp_service_account_json = var.gcp_service_account_json
}
model = {
provider = "gemini"
name = "gemini-2.5-flash"
options = {
anthropic_version = "vertex-2023-10-16"
gemini = {
api_endpoint = var.vertex_api_endpoint
project_id = var.gcp_project_id
location_id = var.gcp_location_id
}
}
}
} ]
}
tags = []
control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
}
This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.
variable "gcp_location_id" {
type = string
}
Add this section to your kong.yaml configuration file:
_format_version: "3.0"
plugins:
- name: ai-proxy-advanced
service: serviceName|Id
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: ${{ env "DECK_GCP_SERVICE_ACCOUNT_JSON" }}
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: ${{ env "DECK_VERTEX_API_ENDPOINT" }}
project_id: ${{ env "DECK_GCP_PROJECT_ID" }}
location_id: ${{ env "DECK_GCP_LOCATION_ID" }}
Make sure to replace the following placeholders with your own values:
-
serviceName|Id: Theidornameof the service the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/services/{serviceName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
serviceName|Id: Theidornameof the service the plugin configuration will target.
Make the following request:
curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/services/{serviceId}/plugins/ \
--header "accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $KONNECT_TOKEN" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
serviceId: Theidof the service the plugin configuration will target.
See the Konnect API reference to learn about region-specific URLs and personal access tokens.
echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: ai-proxy-advanced
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
konghq.com/tags: ''
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: '$GCP_SERVICE_ACCOUNT_JSON'
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: '$VERTEX_API_ENDPOINT'
project_id: '$GCP_PROJECT_ID'
location_id: '$GCP_LOCATION_ID'
plugin: ai-proxy-advanced
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the service resource:
kubectl annotate -n kong service SERVICE_NAME konghq.com/plugins=ai-proxy-advanced
Prerequisite: Configure your Personal Access Token
terraform {
required_providers {
konnect = {
source = "kong/konnect"
}
}
}
provider "konnect" {
personal_access_token = "$KONNECT_TOKEN"
server_url = "https://us.api.konghq.com/"
}
Add the following to your Terraform configuration to create a Konnect Gateway Plugin:
resource "konnect_gateway_plugin_ai_proxy_advanced" "my_ai_proxy_advanced" {
enabled = true
config = {
llm_format = "anthropic"
targets = [
{
route_type = "llm/v1/chat"
logging = {
log_payloads = false
log_statistics = true
}
auth = {
allow_override = false
gcp_use_service_account = true
gcp_service_account_json = var.gcp_service_account_json
}
model = {
provider = "gemini"
name = "gemini-2.5-flash"
options = {
anthropic_version = "vertex-2023-10-16"
gemini = {
api_endpoint = var.vertex_api_endpoint
project_id = var.gcp_project_id
location_id = var.gcp_location_id
}
}
}
} ]
}
tags = []
control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
service = {
id = konnect_gateway_service.my_service.id
}
}
This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.
variable "gcp_location_id" {
type = string
}
Add this section to your kong.yaml configuration file:
_format_version: "3.0"
plugins:
- name: ai-proxy-advanced
route: routeName|Id
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: ${{ env "DECK_GCP_SERVICE_ACCOUNT_JSON" }}
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: ${{ env "DECK_VERTEX_API_ENDPOINT" }}
project_id: ${{ env "DECK_GCP_PROJECT_ID" }}
location_id: ${{ env "DECK_GCP_LOCATION_ID" }}
Make sure to replace the following placeholders with your own values:
-
routeName|Id: Theidornameof the route the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/routes/{routeName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
routeName|Id: Theidornameof the route the plugin configuration will target.
Make the following request:
curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/routes/{routeId}/plugins/ \
--header "accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $KONNECT_TOKEN" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
routeId: Theidof the route the plugin configuration will target.
See the Konnect API reference to learn about region-specific URLs and personal access tokens.
echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: ai-proxy-advanced
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
konghq.com/tags: ''
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: '$GCP_SERVICE_ACCOUNT_JSON'
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: '$VERTEX_API_ENDPOINT'
project_id: '$GCP_PROJECT_ID'
location_id: '$GCP_LOCATION_ID'
plugin: ai-proxy-advanced
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the httproute or ingress resource:
kubectl annotate -n kong httproute konghq.com/plugins=ai-proxy-advanced
kubectl annotate -n kong ingress konghq.com/plugins=ai-proxy-advanced
Prerequisite: Configure your Personal Access Token
terraform {
required_providers {
konnect = {
source = "kong/konnect"
}
}
}
provider "konnect" {
personal_access_token = "$KONNECT_TOKEN"
server_url = "https://us.api.konghq.com/"
}
Add the following to your Terraform configuration to create a Konnect Gateway Plugin:
resource "konnect_gateway_plugin_ai_proxy_advanced" "my_ai_proxy_advanced" {
enabled = true
config = {
llm_format = "anthropic"
targets = [
{
route_type = "llm/v1/chat"
logging = {
log_payloads = false
log_statistics = true
}
auth = {
allow_override = false
gcp_use_service_account = true
gcp_service_account_json = var.gcp_service_account_json
}
model = {
provider = "gemini"
name = "gemini-2.5-flash"
options = {
anthropic_version = "vertex-2023-10-16"
gemini = {
api_endpoint = var.vertex_api_endpoint
project_id = var.gcp_project_id
location_id = var.gcp_location_id
}
}
}
} ]
}
tags = []
control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
route = {
id = konnect_gateway_route.my_route.id
}
}
This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.
variable "gcp_location_id" {
type = string
}
Add this section to your kong.yaml configuration file:
_format_version: "3.0"
plugins:
- name: ai-proxy-advanced
consumer: consumerName|Id
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: ${{ env "DECK_GCP_SERVICE_ACCOUNT_JSON" }}
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: ${{ env "DECK_VERTEX_API_ENDPOINT" }}
project_id: ${{ env "DECK_GCP_PROJECT_ID" }}
location_id: ${{ env "DECK_GCP_LOCATION_ID" }}
Make sure to replace the following placeholders with your own values:
-
consumerName|Id: Theidornameof the consumer the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumers/{consumerName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
consumerName|Id: Theidornameof the consumer the plugin configuration will target.
Make the following request:
curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/consumers/{consumerId}/plugins/ \
--header "accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $KONNECT_TOKEN" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
consumerId: Theidof the consumer the plugin configuration will target.
See the Konnect API reference to learn about region-specific URLs and personal access tokens.
echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: ai-proxy-advanced
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
konghq.com/tags: ''
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: '$GCP_SERVICE_ACCOUNT_JSON'
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: '$VERTEX_API_ENDPOINT'
project_id: '$GCP_PROJECT_ID'
location_id: '$GCP_LOCATION_ID'
plugin: ai-proxy-advanced
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the KongConsumer resource:
kubectl annotate -n kong kongconsumer CONSUMER_NAME konghq.com/plugins=ai-proxy-advanced
Prerequisite: Configure your Personal Access Token
terraform {
required_providers {
konnect = {
source = "kong/konnect"
}
}
}
provider "konnect" {
personal_access_token = "$KONNECT_TOKEN"
server_url = "https://us.api.konghq.com/"
}
Add the following to your Terraform configuration to create a Konnect Gateway Plugin:
resource "konnect_gateway_plugin_ai_proxy_advanced" "my_ai_proxy_advanced" {
enabled = true
config = {
llm_format = "anthropic"
targets = [
{
route_type = "llm/v1/chat"
logging = {
log_payloads = false
log_statistics = true
}
auth = {
allow_override = false
gcp_use_service_account = true
gcp_service_account_json = var.gcp_service_account_json
}
model = {
provider = "gemini"
name = "gemini-2.5-flash"
options = {
anthropic_version = "vertex-2023-10-16"
gemini = {
api_endpoint = var.vertex_api_endpoint
project_id = var.gcp_project_id
location_id = var.gcp_location_id
}
}
}
} ]
}
tags = []
control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
consumer = {
id = konnect_gateway_consumer.my_consumer.id
}
}
This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.
variable "gcp_location_id" {
type = string
}
Add this section to your kong.yaml configuration file:
_format_version: "3.0"
plugins:
- name: ai-proxy-advanced
consumer_group: consumerGroupName|Id
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: ${{ env "DECK_GCP_SERVICE_ACCOUNT_JSON" }}
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: ${{ env "DECK_VERTEX_API_ENDPOINT" }}
project_id: ${{ env "DECK_GCP_PROJECT_ID" }}
location_id: ${{ env "DECK_GCP_LOCATION_ID" }}
Make sure to replace the following placeholders with your own values:
-
consumerGroupName|Id: Theidornameof the consumer group the plugin configuration will target.
Make the following request:
curl -i -X POST http://localhost:8001/consumer_groups/{consumerGroupName|Id}/plugins/ \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
consumerGroupName|Id: Theidornameof the consumer group the plugin configuration will target.
Make the following request:
curl -X POST https://{region}.api.konghq.com/v2/control-planes/{controlPlaneId}/core-entities/consumer_groups/{consumerGroupId}/plugins/ \
--header "accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer $KONNECT_TOKEN" \
--data '
{
"name": "ai-proxy-advanced",
"config": {
"llm_format": "anthropic",
"targets": [
{
"route_type": "llm/v1/chat",
"logging": {
"log_payloads": false,
"log_statistics": true
},
"auth": {
"allow_override": false,
"gcp_use_service_account": true,
"gcp_service_account_json": "'$GCP_SERVICE_ACCOUNT_JSON'"
},
"model": {
"provider": "gemini",
"name": "gemini-2.5-flash",
"options": {
"anthropic_version": "vertex-2023-10-16",
"gemini": {
"api_endpoint": "'$VERTEX_API_ENDPOINT'",
"project_id": "'$GCP_PROJECT_ID'",
"location_id": "'$GCP_LOCATION_ID'"
}
}
}
}
]
},
"tags": []
}
'
Make sure to replace the following placeholders with your own values:
-
region: Geographic region where your Kong Konnect is hosted and operates. -
KONNECT_TOKEN: Your Personal Access Token (PAT) associated with your Konnect account. -
controlPlaneId: Theidof the control plane. -
consumerGroupId: Theidof the consumer group the plugin configuration will target.
See the Konnect API reference to learn about region-specific URLs and personal access tokens.
echo "
apiVersion: configuration.konghq.com/v1
kind: KongPlugin
metadata:
name: ai-proxy-advanced
namespace: kong
annotations:
kubernetes.io/ingress.class: kong
konghq.com/tags: ''
config:
llm_format: anthropic
targets:
- route_type: llm/v1/chat
logging:
log_payloads: false
log_statistics: true
auth:
allow_override: false
gcp_use_service_account: true
gcp_service_account_json: '$GCP_SERVICE_ACCOUNT_JSON'
model:
provider: gemini
name: gemini-2.5-flash
options:
anthropic_version: vertex-2023-10-16
gemini:
api_endpoint: '$VERTEX_API_ENDPOINT'
project_id: '$GCP_PROJECT_ID'
location_id: '$GCP_LOCATION_ID'
plugin: ai-proxy-advanced
" | kubectl apply -f -
Next, apply the KongPlugin resource by annotating the KongConsumerGroup resource:
kubectl annotate -n kong kongconsumergroup CONSUMERGROUP_NAME konghq.com/plugins=ai-proxy-advanced
Prerequisite: Configure your Personal Access Token
terraform {
required_providers {
konnect = {
source = "kong/konnect"
}
}
}
provider "konnect" {
personal_access_token = "$KONNECT_TOKEN"
server_url = "https://us.api.konghq.com/"
}
Add the following to your Terraform configuration to create a Konnect Gateway Plugin:
resource "konnect_gateway_plugin_ai_proxy_advanced" "my_ai_proxy_advanced" {
enabled = true
config = {
llm_format = "anthropic"
targets = [
{
route_type = "llm/v1/chat"
logging = {
log_payloads = false
log_statistics = true
}
auth = {
allow_override = false
gcp_use_service_account = true
gcp_service_account_json = var.gcp_service_account_json
}
model = {
provider = "gemini"
name = "gemini-2.5-flash"
options = {
anthropic_version = "vertex-2023-10-16"
gemini = {
api_endpoint = var.vertex_api_endpoint
project_id = var.gcp_project_id
location_id = var.gcp_location_id
}
}
}
} ]
}
tags = []
control_plane_id = konnect_gateway_control_plane.my_konnect_cp.id
consumer_group = {
id = konnect_gateway_consumer_group.my_consumer_group.id
}
}
This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.
variable "gcp_location_id" {
type = string
}