Global Synthetic Monitoring: A Production-Ready, Multi-Region Monitoring Platform Built on AWS

Modern applications are global - your monitoring should be too.
Global Synthetic Monitoring is a production-ready, enterprise-grade solution that simulates real user interactions from 50+ global locations, ensuring your applications remain fast, available, and reliable - before real customers ever notice a problem.

This article explores the architecture, features, and deployment model behind the project, blending both product value and the deep AWS engineering that powers it.

Why Global Synthetic Monitoring Matters

Traditional monitoring tells you what happened.
Synthetic monitoring tells you what will happen.

By proactively running scheduled API tests, browser automation scripts, and uptime probes from multiple global regions, organizations gain:

• Early detection of outages
• Performance baselines for key user flows
• Monitoring from actual end-user geographies
• Validation before every production deployment
• Confidence in SLAs and compliance

This platform provides all of that - using a fully automated, scalable AWS architecture.

High-Level Architecture

The platform uses a multi-layer cloud-native design with edge security, scalable compute, and an enterprise observability stack.

System Architecture

It is divided into five key layers:

Internet Layer
Represents end users and systems interacting with your monitoring APIs.

Edge Layer
Traffic first hits CloudFront CDN, then flows into API Gateway, protected by AWS WAF for security.

Compute Layer
Three compute models handle workloads:

• Lambda for serverless operations
• ECS Fargate for containerized synthetic tests
• EC2 Auto Scaling for heavy/long-running tests

Data Layer
Stores configurations, results, logs, and cache:

• RDS/Aurora for relational data
• DynamoDB for high-speed lookups
• S3 for logs, scripts, artifacts
• ElastiCache for caching frequent queries

Observability Layer
Monitors system behavior:

• CloudWatch for metrics/logs
• X-Ray for distributed tracing
• SNS for alerts

This architecture ensures global scale, high availability, security, and strong observability.

How Requests Flow Through the System

Each synthetic monitoring test triggers a full request life cycle from API entry to compute execution to data retrieval.

Data Flow Sequence

User Request
A client or scheduled test triggers a request that reaches API Gateway.

Authentication
API Gateway validates the authentication token before allowing further processing.

Compute Processing
Lambda, ECS, or EC2 executes the business logic depending on the type of test or workload.

Caching
The compute layer checks ElastiCache for cached data to reduce database load:

• If the cache contains the data, the result is returned immediately.
• If not, the system queries the database, processes the result, and updates the cache.

Response
The processed data is returned to the user through API Gateway.

Observability
Compute services send logs and metrics to CloudWatch. Threshold rules trigger SNS alerts when an issue is detected.

This flow ensures performance optimization through caching and provides strong visibility into every request.

Core Features That Make It Enterprise-Grade

High Availability (99.99% SLA)

• Multi-AZ deployment
• Automatic failover
• Health checks at every layer

Auto Scaling Everywhere

• Lambda concurrency scaling
• ECS Fargate scaling rules
• EC2 Auto Scaling with predictive scaling

End-to-End Security

• TLS 1.3 for all network traffic
• IAM least privilege roles
• VPC isolation with private subnets
• AWS WAF for Layer-7 protection

Complete Observability

• CloudWatch dashboards
• Custom error, latency, and business metrics
• Distributed tracing with X-Ray
• Real-time alerts via SNS

Cost Optimization Built-In

• Spot instances for non-critical workloads
• S3 Intelligent-Tiering
• Auto-shutdown for dev/test environments
• Reserved instances for predictable usage

Disaster Recovery & Compliance

• Daily & weekly automated backups
• Cross-region replication
• RTO < 1 hour / RPO < 15 minutes
• HIPAA, PCI-DSS, SOC 2, and GDPR alignment

Infrastructure as Code with Pulumi (Python)

The entire platform is defined using Pulumi + Python, enabling:

• Version-controlled infrastructure
• Repeatable deployments across dev/stage/prod
• GitOps workflows
• Modular infrastructure stacks

Example Pulumi patterns used:

• VPC and subnets
• RDS/Aurora clusters
• ECS Fargate services
• CloudWatch alarms
• IAM policies & roles

This ensures 100% reproducibility from local dev to production.

Deployment Flow: From Commit to Global Monitoring

Everything runs through a modern CI/CD pipeline powered by GitHub Actions.

Deployment Pipeline

Development
Engineers push changes to GitHub.

CI/CD Pipeline
GitHub Actions runs automated build, testing, and package creation.

Staging
The tested build is deployed to a staging environment for final checks.

Canary Deployment
A small percentage (10%) of production traffic is routed to the new version.

Health Check
If performance metrics and health checks pass, traffic is gradually increased to 50%, then finally to 100%.
If any threshold fails, the system automatically rolls back to the previous stable version.

This pipeline ensures controlled, zero-downtime deployments with continuous validation.

Cost Breakdown

Designed to scale up when needed, and down to save money.

Final Thoughts

Global Synthetic Monitoring is more than a monitoring tool, it’s a global reliability platform.
It delivers:

• Early detection of failures
• Deep performance insights
• Strong security & compliance
• Fast, automated deployments
• Full observability and cost control

With a modern AWS architecture and Pulumi IaC foundation, it is built for teams that demand production-grade reliability with global reach.

If you’re building SaaS, enterprise apps, or latency-sensitive APIs, this solution provides the synthetic visibility you need to stay ahead of issues worldwide.

Here’s a clean way to include the repo link and owner details at the end of your blog post:

Project Repository

GitHub Repo: https://github.com/rahulladumor/synthetic-monitoring-global

Project Author

Author: Rahul Ladumor
Portfolio: https://acloudwithrahul.in
GitHub: https://github.com/rahulladumor
LinkedIn: https://linkedin.com/in/rahulladumor
Email: rahul.ladumor@infratales.com