Online Boutique is a cloud-first microservices demo application. The application is a web-based e-commerce app where users can browse items, add them to the cart, and purchase them.

Google uses this application to demonstrate how developers can modernize enterprise applications using Google Cloud products, including: Google Kubernetes Engine (GKE), Cloud Service Mesh (CSM), gRPC, Cloud Operations, Spanner, Memorystore, AlloyDB, and Gemini. This application works on any Kubernetes cluster.

If you’re using this demo, please ★Star this repository to show your interest!

Note to Googlers: Please fill out the form at go/microservices-demo.

Architecture

Online Boutique is composed of 11 microservices written in different languages that talk to each other over gRPC.

Find Protocol Buffers Descriptions at the ./protos directory.

Service	Language	Description
frontend	Go	Exposes an HTTP server to serve the website. Does not require signup/login and generates session IDs for all users automatically.
cartservice	C#	Stores the items in the user's shopping cart in Redis and retrieves it.
productcatalogservice	Go	Provides the list of products from a JSON file and ability to search products and get individual products.
currencyservice	Node.js	Converts one money amount to another currency. Uses real values fetched from European Central Bank. It's the highest QPS service.
paymentservice	Node.js	Charges the given credit card info (mock) with the given amount and returns a transaction ID.
shippingservice	Go	Gives shipping cost estimates based on the shopping cart. Ships items to the given address (mock)
emailservice	Python	Sends users an order confirmation email (mock).
checkoutservice	Go	Retrieves user cart, prepares order and orchestrates the payment, shipping and the email notification.
recommendationservice	Python	Recommends other products based on what's given in the cart.
adservice	Java	Provides text ads based on given context words.
loadgenerator	Python/Locust	Continuously sends requests imitating realistic user shopping flows to the frontend.

Screenshots

Home Page	Checkout Screen

Quickstart (GKE)

1. Ensure you have the following requirements:

   • Google Cloud project.
   • Shell environment with gcloud, git, and kubectl.

2. Clone the latest major version.

   git clone --depth 1 --branch v0 https://github.com/GoogleCloudPlatform/microservices-demo.git
   cd microservices-demo/

   The --depth 1 argument skips downloading git history.

3. Set the Google Cloud project and region and ensure the Google Kubernetes Engine API is enabled.

   export PROJECT_ID=<PROJECT_ID>
   export REGION=us-central1
   gcloud services enable container.googleapis.com \
     --project=${PROJECT_ID}

   Substitute <PROJECT_ID> with the ID of your Google Cloud project.

4. Create a GKE cluster and get the credentials for it.

   gcloud container clusters create-auto online-boutique \
     --project=${PROJECT_ID} --region=${REGION}

   Creating the cluster may take a few minutes.

5. Deploy Online Boutique to the cluster.

   kubectl apply -f ./release/kubernetes-manifests.yaml

6. Wait for the pods to be ready.

   kubectl get pods

   After a few minutes, you should see the Pods in a Running state:

   NAME                                     READY   STATUS    RESTARTS   AGE
   adservice-76bdd69666-ckc5j               1/1     Running   0          2m58s
   cartservice-66d497c6b7-dp5jr             1/1     Running   0          2m59s
   checkoutservice-666c784bd6-4jd22         1/1     Running   0          3m1s
   currencyservice-5d5d496984-4jmd7         1/1     Running   0          2m59s
   emailservice-667457d9d6-75jcq            1/1     Running   0          3m2s
   frontend-6b8d69b9fb-wjqdg                1/1     Running   0          3m1s
   loadgenerator-665b5cd444-gwqdq           1/1     Running   0          3m
   paymentservice-68596d6dd6-bf6bv          1/1     Running   0          3m
   productcatalogservice-557d474574-888kr   1/1     Running   0          3m
   recommendationservice-69c56b74d4-7z8r5   1/1     Running   0          3m1s
   redis-cart-5f59546cdd-5jnqf              1/1     Running   0          2m58s
   shippingservice-6ccc89f8fd-v686r         1/1     Running   0          2m58s
   

7. Access the web frontend in a browser using the frontend's external IP.

   kubectl get service frontend-external | awk '{print $4}'

   Visit http://EXTERNAL_IP in a web browser to access your instance of Online Boutique.

8. Congrats! You've deployed the default Online Boutique. To deploy a different variation of Online Boutique (e.g., with Google Cloud Operations tracing, Istio, etc.), see Deploy Online Boutique variations with Kustomize.

9. Once you are done with it, delete the GKE cluster.

   gcloud container clusters delete online-boutique \
     --project=${PROJECT_ID} --region=${REGION}

   Deleting the cluster may take a few minutes.

Additional deployment options

• Terraform: See these instructions to learn how to deploy Online Boutique using Terraform.
• Istio / Cloud Service Mesh: See these instructions to deploy Online Boutique alongside an Istio-backed service mesh.
• Non-GKE clusters (Minikube, Kind, etc): See the Development guide to learn how you can deploy Online Boutique on non-GKE clusters.
• AI assistant using Gemini: See these instructions to deploy a Gemini-powered AI assistant that suggests products to purchase based on an image.
• And more: The /kustomize directory contains instructions for customizing the deployment of Online Boutique with other variations.

Documentation

• Development to learn how to run and develop this app locally.

Demos featuring Online Boutique

• Platform Engineering in action: Deploy the Online Boutique sample apps with Score and Humanitec
• The new Kubernetes Gateway API with Istio and Anthos Service Mesh (ASM)
• Use Azure Redis Cache with the Online Boutique sample on AKS
• Sail Sharp, 8 tips to optimize and secure your .NET containers for Kubernetes
• Deploy multi-region application with Anthos and Google cloud Spanner
• Use Google Cloud Memorystore (Redis) with the Online Boutique sample on GKE
• Use Helm to simplify the deployment of Online Boutique, with a Service Mesh, GitOps, and more!
• How to reduce microservices complexity with Apigee and Anthos Service Mesh
• gRPC health probes with Kubernetes 1.24+
• Use Google Cloud Spanner with the Online Boutique sample
• Seamlessly encrypt traffic from any apps in your Mesh to Memorystore (redis)
• Strengthen your app's security with Cloud Service Mesh and Anthos Config Management
• From edge to mesh: Exposing service mesh applications through GKE Ingress
• Take the first step toward SRE with Cloud Operations Sandbox
• Deploying the Online Boutique sample application on Cloud Service Mesh
• Anthos Service Mesh Workshop: Lab Guide
• KubeCon EU 2019 - Reinventing Networking: A Deep Dive into Istio's Multicluster Gateways - Steve Dake, Independent
• Google Cloud Next'18 SF
  • Day 1 Keynote showing GKE On-Prem
  • Day 3 Keynote showing Stackdriver APM (Tracing, Code Search, Profiler, Google Cloud Build)
  • Introduction to Service Management with Istio
• Google Cloud Next'18 London – Keynote showing Stackdriver Incident Response Management

Archodex Configuration

This fork requires additional secrets and configuration for LLM services and OpenTelemetry tracing.

Required Secrets

Create these secrets before deploying. Secrets are namespaced - create them in the same namespace where services will be deployed (e.g., -n qa or -n prod).

Secret Name	Key	Used By	Description
adservice-aws-credentials	AWS_ACCESS_KEY_ID	adservice	AWS access key for Bedrock (must grant access to Amazon Nova 2 Lite model)
adservice-aws-credentials	AWS_SECRET_ACCESS_KEY	adservice	AWS secret key for Bedrock
recommendationservice-openrouter	OPENROUTER_API_KEY	recommendationservice	OpenRouter API key
archodex-otel-axiom-token	value	opentelemetrycollector	Axiom API token (if using otel-tracing component)

Example:

# LLM Services (required) - add -n <namespace> to match your deployment
kubectl create secret generic adservice-aws-credentials \
  -n <namespace> \
  --from-literal=AWS_ACCESS_KEY_ID=<your-key> \
  --from-literal=AWS_SECRET_ACCESS_KEY=<your-secret>

kubectl create secret generic recommendationservice-openrouter \
  -n <namespace> \
  --from-literal=OPENROUTER_API_KEY=<your-key>

# OpenTelemetry (if using otel-tracing component)
kubectl create secret generic archodex-otel-axiom-token \
  -n <namespace> \
  --from-literal=value=<your-axiom-api-token>

Required ConfigMaps

ConfigMaps are also namespaced - create them in the same namespace as the services.

ConfigMap Name	Key	Used By	Description
otel-collector-env	dataset	opentelemetrycollector	Axiom dataset name (if using otel-tracing component)

Example:

# OpenTelemetry (if using otel-tracing component)
kubectl create configmap otel-collector-env \
  -n <namespace> \
  --from-literal=dataset=<your-axiom-dataset>

Optional Configuration (ConfigMap)

The llm-config ConfigMap (deployed with the services) contains shared LLM settings:

Key	Default	Description
AWS_REGION	us-west-2	AWS region for Bedrock API
OPENROUTER_TIMEOUT_SECONDS	30	Request timeout for OpenRouter calls

Environment-Specific Overrides (llm-config-override)

Use the llm-config-override ConfigMap to customize settings per environment:

Key	Default	Description
OPENROUTER_MODEL	openai/gpt-oss-120b:free	OpenRouter model for recommendationservice
TZ	America/Los_Angeles	Timezone for rate limiting active hours
LLM_ACTIVE_HOURS	9-17	Hours when LLM calls are allowed (e.g., 9-17, 8:30-16:30, or 9-11,15-19)
LLM_RATE_LIMIT_PER_MINUTE	2	Max LLM calls per minute during active hours
LLM_SAMPLE_RATE	0.0001	Fraction of requests eligible for LLM (0.01% default)

Setup (one-time per namespace):

kubectl create configmap llm-config-override -n <namespace> \
  --from-literal=TZ=Asia/Tokyo \
  --from-literal=LLM_ACTIVE_HOURS=8:30-16:30

Rate Limiting: The recommendationservice uses OpenRouter's free tier which has strict limits (1000 requests/day with 10 credits). The rate limiting settings control when and how often LLM calls are made:

• During LLM_ACTIVE_HOURS: Rate limited to LLM_RATE_LIMIT_PER_MINUTE calls per minute
• Outside LLM_ACTIVE_HOURS: Sampled at LLM_SAMPLE_RATE (default ~1 call/hour)

To update the override ConfigMap:

kubectl edit configmap llm-config-override -n <namespace>