.png)
Cloud Agnostic vs Cloud Native Microservices Architecture
- Anand Nerurkar
- Apr 21
- 6 min read
☁️ Cloud Agnostic vs Cloud Native Microservices Architecture
Aspect | Cloud Native | Cloud Agnostic |
Definition | Designed specifically to run optimally on a specific cloud provider (e.g., Azure, AWS, GCP). | Designed to run on any cloud provider or on-premises with minimal changes. |
Tightly Coupled to Cloud Services | Yes. Uses cloud provider’s proprietary services (e.g., Azure Cosmos DB, AWS Lambda). | No. Uses open standards and portable services (e.g., PostgreSQL, Kubernetes). |
Tooling & Services | Leverages cloud-native tools (e.g., Azure DevOps, Azure Functions, Azure AKS, Application Gateway). | Uses open-source or cross-platform tools (e.g., Jenkins, Docker, Kubernetes, Kafka). |
Portability | Low – hard to migrate to another cloud. | High – easier to switch providers or run hybrid/cloud/on-prem. |
Performance Optimization | High – can use cloud-specific features for optimization. | Moderate – might avoid vendor-optimized services. |
Cost Efficiency | Often better due to integrated services and scaling options. | May require more effort to manage infrastructure manually. |
Examples of Cloud Services Used | - Azure API Management - Azure Key Vault - Azure Monitor - Azure Functions | - Apache Kafka - HashiCorp Vault - Prometheus/Grafana - Docker + Kubernetes |
Deployment Platform | Cloud-native PaaS like Azure AKS, AWS ECS, GCP Cloud Run | Kubernetes, OpenShift, Docker Swarm across any cloud or on-prem |
Use Case Fit | When you are committed to one cloud and want to leverage its full power. | When you want cloud independence, multi-cloud, or hybrid strategy. |
🎯 When to Choose What?
Go Cloud Native when:
You’re locked in to a single cloud provider.
You want rapid development, autoscaling, and native integrations.
You’re building modern apps with high agility in a cloud-first environment.
Go Cloud Agnostic when:
You have a multi-cloud strategy or are preparing for cloud migration.
You want to avoid vendor lock-in.
You’re building systems that require high portability and flexibility.
🏦 Example in Banking:
Cloud Native: A credit scoring microservice uses Azure Cognitive Services for risk modeling and stores data in Azure Cosmos DB.
Cloud Agnostic: A fraud detection service runs in Kubernetes, uses Apache Kafka for event streaming, and stores data in PostgreSQL – portable across Azure, AWS, or on-prem.
pls come up with cloud agnostic microservices architecture with Azure Cloud leveraing all azure cloud services for a personal banking use case -text version flow
Absolutely! Below is a Cloud Agnostic Microservices Architecture for a Personal Banking Use Case—deployed on Azure Cloud, yet designed to remain portable across any cloud provider (AWS, GCP, on-prem). This architecture uses open-source, cloud-agnostic components while integrating with Azure-native services for deployment, management, and observability.
🏦 Personal Banking – Cloud Agnostic Microservices Architecture (on Azure Cloud)
🔹 Key Principles
All microservices are stateless, containerized with Docker.
Services deployed via Kubernetes (AKS) for orchestration.
Communication and observability managed via Istio (Service Mesh).
Event-driven architecture using Apache Kafka.
Configuration, secrets, and identity managed in a portable, secure way.
🧱 Core Components
🧩 Microservices (Dockerized & loosely coupled)
CustomerProfileService – manages customer onboarding, KYC
AccountService – creates and manages savings/current accounts
TransactionService – handles transfers, deposits, withdrawals
LoanService – manages loan applications, status tracking
CardService – handles credit/debit card issuance and transactions
NotificationService – sends email/SMS/app alerts
FraudDetectionService – listens to events and flags anomalies
☁️ Cloud Agnostic Tools
Function | Component | Description |
Service Orchestration | Kubernetes (AKS) | Workload management, scaling |
Service Mesh | Istio | Traffic routing, observability, security |
API Gateway | Azure API Management or Kong | Abstracts microservices from frontend |
Streaming | Apache Kafka (Confluent or self-managed) | Event-based communication |
Persistence | PostgreSQL, MongoDB | Portable DBs across clouds |
Secrets Management | HashiCorp Vault (or Azure Key Vault) | Store secrets, API keys securely |
Config Management | Spring Cloud Config | Externalized configuration |
Monitoring | Prometheus + Grafana | Metrics, dashboards |
Logging | ELK Stack (Elasticsearch, Logstash, Kibana) | Centralized logging |
CI/CD | Azure DevOps / GitHub Actions | Container build & deployment pipelines |
🔐 Security Layer
SSO & Identity: Azure AD integration via OpenID Connect (OIDC) for auth.
RBAC & Least Privilege: IAM roles and Kubernetes RBAC policies.
mTLS with Istio: Service-to-service encryption.
SSL Termination: Configured at Azure Application Gateway.
🌐 Network & Infrastructure (on Azure)
Layer | Azure Component | Purpose |
Subscription | Azure Subscription | Logical resource grouping, RBAC |
Networking | Azure VNet | Isolated network for resources |
Subnets | Separate for App, DB, Gateway | Network isolation |
Firewall & NSGs | Azure Firewall + NSG | Traffic filtering and control |
Ingress | Azure Application Gateway | L7 Load Balancer + WAF |
Load Balancer | Internal LB (for east-west traffic) | High availability |
Traffic Manager | Azure Traffic Manager | Geo-routing and failover |
DNS & SSL | Azure DNS + Cert Manager | Domain and TLS config |
Registry | Azure Container Registry (ACR) | Stores Docker images |
Kubernetes | Azure Kubernetes Service (AKS) | Microservices deployment |
Monitoring | Azure Monitor, Application Insights | Telemetry, logs, tracing |
🧭 User Request Flow Example
Customer logs in → Auth via Azure AD or custom OIDC.
Front-end calls API Gateway → Routes to relevant microservice.
New account creation → CustomerProfileService & AccountService interact.
Events emitted to Kafka → Fraud check, welcome email triggered.
Data persisted → PostgreSQL & MongoDB for customer/account data.
Dashboard rendered → Frontend calls API to fetch live account status.
🔁 High Availability & Scaling
Multi-AZ deployment via AKS and Azure Load Balancer
Auto-scaling pods with HPA (Horizontal Pod Autoscaler)
Kafka with replication across availability zones
⚠️ Risks and Mitigation (Snapshot)
Category | Example Risk | Mitigation |
Business | Downtime during high traffic | Multi-AZ, autoscaling |
Ops | Deployment failure | Canary releases, blue-green deployments |
Security | Data breach | mTLS, Azure AD, Vault |
Compliance | GDPR/PCI violations | Auditing, tokenization, data encryption |
Technology | Service dependency | Circuit breakers, retries, fallbacks |
People | Skill gap in cloud/K8s | Training, platform engineering |
Environment | Regional outage | Geo-redundancy, Azure Traffic Manager |
:
Great! Here's an expanded case study walkthrough for a Personal Banking Cloud-Agnostic Microservices Architecture (deployed on Azure Cloud) covering a realistic End-to-End Customer Flow.
🧾 Case Study: Personal Banking – Cloud-Agnostic Architecture on Azure
🎯 Use Case: Customer Onboarding → Account Creation → UPI Setup → Transaction → Fraud Check
👤 Step 1: Customer Onboarding
Scenario: A user visits the banking portal/app to open a new savings account.
Flow:
User enters personal details and uploads KYC documents via frontend.
API Gateway forwards the request to CustomerProfileService.
CustomerProfileService performs:
Input validation
KYC data formatting
Publishes CustomerOnboarded event to Kafka
DocumentService asynchronously receives the event and stores documents in Blob Storage.
NotificationService sends a "Thank you for registering" SMS/email.
FraudDetectionService runs a check on initial customer profile using basic heuristics.
Azure Services Used:
Azure API Management
AKS-hosted Spring Boot services
Azure Blob Storage
Azure AD B2C (for SSO)
Kafka (Confluent on AKS or Event Hub Kafka-compatible)
🏦 Step 2: Account Creation
Scenario: Post-onboarding, the system auto-triggers account creation.
Flow:
CustomerOnboarded event picked up by AccountService.
Account number is generated.
Account data persisted in Azure SQL/PostgreSQL.
Event AccountCreated is emitted.
NotificationService triggers welcome messages with account info.
Azure Services Used:
Azure PostgreSQL (Flexible Server)
Azure Event Hubs or Kafka
Azure DevOps CI/CD pipeline deploys services
📲 Step 3: UPI Setup & Device Binding
Scenario: The user links their bank account to UPI and verifies mobile device.
Flow:
UPIService receives device binding and UPI handle request via API Gateway.
Mobile OTP verification done via 3rd-party provider.
UPI handle linked, and status updated.
UPIService publishes UPILinked event.
Fraud check is invoked if device location/IP is suspicious.
Azure Services Used:
Azure API Management
Azure AD for secure auth with OAuth2
Azure Monitor + Application Insights (for anomaly detection)
Kafka for UPILinked event streaming
💸 Step 4: Transaction Processing (UPI Payment)
Scenario: Customer initiates a UPI payment from their account.
Flow:
User initiates payment in mobile app.
Request hits TransactionService through the Gateway.
TransactionService:
Verifies balance from AccountService
Creates a debit transaction
Emits TransactionInitiated to Kafka
FraudDetectionService picks the event and runs transaction scoring.
If approved:
Updates account balance in PostgreSQL.
Confirms to UPI layer.
Publishes TransactionSuccess.
Azure Services Used:
Azure SQL/PostgreSQL
Azure Kafka/Event Hub
Azure AD (access token validation)
Azure Monitor (real-time tracking)
🚨 Step 5: Fraud Detection & Real-Time Alerts
Scenario: Fraud Detection identifies an unusual pattern.
Flow:
FraudDetectionService (running ML model) flags a transaction.
Event: FraudAlertRaised is published.
NotificationService sends alert to customer.
TransactionService reverses or blocks further actions if required.
Audit trail is logged in centralized ELK stack.
Azure Services Used:
Azure Machine Learning for model scoring
Azure Event Hub for event propagation
Azure Monitor / Log Analytics
Azure App Insights (for distributed tracing)
🌍 End-to-End Integration Overview
Tier | Services Involved |
Frontend | Web / Mobile App (calls APIs) |
API Gateway | Azure API Management |
Service Mesh | Istio on AKS |
Microservices | Spring Boot-based, Dockerized |
Asynchronous Events | Kafka or Azure Event Hub |
Data Layer | Azure PostgreSQL, MongoDB |
Security | Azure AD B2C, OAuth2, mTLS |
CI/CD | Azure DevOps + ACR |
Monitoring | Azure Monitor, App Insights |
Networking | VNet, Subnets, NSG, Azure Firewall, Application Gateway with SSL |
Scalability | AKS with HPA + Traffic Manager for Geo-distribution |
Comentários