🚀 How I Ran 11 Microservices with OpenChoreo

Limani Ndou — Wed, 25 Mar 2026 22:05:07 +0000

I used OpenChoreo to deploy and manage the Google Cloud microservices demo (11 services) on my local machine. The result was a cleaner developer workflow, better service visibility, and a production-like platform experience without building custom platform tooling from scratch.

1️⃣ What OpenChoreo?
OpenChoreo is a developer platform for Kubernetes offering development and architecture abstractions, a Backstage-powered developer portal, application CI/CD, GitOps, and observability. It composes CNCF and complementary projects into a modular, extensible framework that provides both strong governance for platform engineers and simplified abstractions for developers.

OpenChoreo was developed by WSO2 and is now part of the Cloud Native Computing Foundation (CNCF) ecosystem.

2️⃣ Why OpenChoreo?
OpenChoreo it provides all essential building blocks of an Internal Developer Platform (IDP) :

High-level APIs for modeling cloud-native applications
A Control Plane that understands and enforces these APIs with GitOps support
A built-in workflow engine for CI/CD pipelines and automation
An opinionated Data Plane with runtime enforcement of design-time semantics
Built-in security, networking, and observability integrations
This is the key reason I used it for this 11-microservice demo.

3️⃣ GCP Microservices Demo: What It Includes
Repository: https://github.com/GoogleCloudPlatform/microservices-demo

🌐 frontend (Go): Serves the website and auto-generates user session IDs.
🛒 cartservice (C#): Stores/retrieves cart items in Redis.
📚 productcatalogservice (Go): Product listing, search, and product details from JSON.
💱 currencyservice (Node.js): Converts money values using ECB exchange rates (highest QPS).
💳 paymentservice (Node.js): Mock credit card charge and transaction ID generation.
🚚 shippingservice (Go): Shipping cost estimates and mock shipping execution.
✉️ emailservice (Python): Mock order confirmation emails.
🎯 checkoutservice (Go): Orchestrates cart, payment, shipping, and email flow.
💡 recommendationservice (Python): Suggests products from cart context.
📢 adservice (Java): Returns text ads based on context words.
🔄 loadgenerator (Python/Locust): Simulates realistic shopping traffic.
4️⃣ What I Did (Step by Step)

⚙️ Prerequisites
Ensure the following before you begin:
-Docker (Engine 26.0+ recommended)
-Allocate at least 4 GB RAM and 2 CPUs.
-If you plan to install with WorkflowPlane, allocate 8 GB RAM and 4 CPUs for optimal performance.
-5-10 minutes of setup time

Step 1: Start OpenChoreo Quick Start

docker run --rm -it --name openchoreo-quick-start `
  --pull always `
  -v //var/run/docker.sock:/var/run/docker.sock `
  -p 8080:8080 -p 9443:9443 `
  ghcr.io/openchoreo/quick-start:v1.0.0

Step 2: Install OpenChoreo(two options):

./install.sh

With observability

./install.sh --with-build --with-observability

Step 3: Choose a Deployment Option
There are two ways to deploy the GCP Microservices Demo:

Option A: Use the built-in OpenChoreo deploy script for GCP Microservices Demo (11 services: frontend, cart, checkout, payment, and more)

./deploy-gcp-demo.sh

Option B: Clone and deploy manually

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

kubectl apply -f ./release/kubernetes-manifests.yaml
I used Option B (clone and deploy).

Step 4: Verify in OpenChoreo UI
Access the UI at: http://openchoreo.localhost:8080/

Default login credentials:

Username: admin@openchoreo.dev
Password: Admin@123

Check:

Project and component catalog
Deployment pipeline view
Environment progression

Step 5: Validate End-to-End App Behavior
I opened the storefront and validated core user flow (browse, cart, checkout).

📚 References
Documentation: https://openchoreo.dev/docs/
Documentation: https://insights.linuxfoundation.org/project/openchore
OpenChoreo Quick Start Guide:
https://openchoreo.dev/docs/getting-started/quick-start-guide/
GCP Microservices Demo Repository:
https://github.com/GoogleCloudPlatform/microservices-demo

🚀 How I Built a Serverless Expense Tracker Using AWS Lambda and Amazon S3 - No Server Required

Limani Ndou — Fri, 20 Mar 2026 19:50:36 +0000

🚀I have built a fully serverless expense tracking pipeline on AWS no servers, no manual work, just pure cloud automation.

The problem: combining monthly expense CSVs every year is tedious and error-prone. So I automated the entire process.

🪣 Amazon S3 (Simple Storage Service)
Amazon S3 is a cloud-based object storage service that stores and retrieves any amount of data from anywhere in the world. It can also watch for new files and automatically notify other AWS services when something is uploaded.
💡 Think of it as a smart cloud hard drive the moment a CSV lands in S3, it wakes up Lambda automatically.

⚡ AWS Lambda
AWS Lambda is a serverless compute service that runs your code in response to events, without provisioning or managing any servers. You only pay for the milliseconds your code actually runs.
💡 Think of it as a worker who sleeps until you need them triggered, runs the task, goes back to sleep. No server running 24/7, no idle costs.

🔐 AWS IAM (Identity & Access Management)
AWS IAM is a service that controls access to AWS resources by defining who is authenticated and what actions they are authorised to perform.
💡 Think of it as the security guard of AWS without IAM, Lambda would not be allowed to touch a single file in S3.

📊 Amazon CloudWatch
Amazon CloudWatch is a monitoring and observability service that collects logs, metrics, and events from AWS services in real time.
💡 Think of it as the CCTV of your cloud application every log line, every error, every execution time recorded automatically.

⚙️ How The Pipeline Works:

Upload monthly CSV to S3
S3 fires an event → Lambda wakes up
Lambda reads the new file + existing master
Merges, sorts by date, removes duplicates
Recalculates category totals in South African Rand
Saves updated master file back to S3 instantly

📈 Real Results — 3 Months of SA Expense Data:
• ✅ 50 transactions merged automatically
• ✅ 0 manual steps required
• ✅ Categories: Groceries, Fuel, Utilities, Health & more
• 💰 Grand Total (YTD 2026): R41,859.97

🔗 Full source code, architecture diagram & sample data if you want to try:
👉 https://lnkd.in/dQNytjKD

DEV Community: Limani Ndou

🚀 How I Ran 11 Microservices with OpenChoreo

🚀 How I Built a Serverless Expense Tracker Using AWS Lambda and Amazon S3 - No Server Required