Day 16 – Serverless in Multi-Cloud: Combining AWS Lambda, Azure Functions & GCP Functions

Introduction

Imagine this: A fintech company handles millions of micro-transactions daily. Their engineers don’t want to manage servers. They want functions that scale instantly, cost only when used, and integrate across AWS, Azure, and GCP.

That’s the promise of serverless computing.

In single-cloud, serverless is straightforward. But in multi-cloud environments, things get exciting:

●      AWS Lambda powers financial events.

●      Azure Functions handle enterprise integrations (like Active Directory).

●      GCP Functions process data pipelines into BigQuery.

At CuriosityTech.in, when we train engineers on multi-cloud, we show them how serverless can glue together different cloud-native services.

Section 1 – Understanding Serverless in Multi-Cloud

●      Serverless = FaaS (Function as a Service)

●      Write small pieces of code (functions) that run only when triggered.

●      No provisioning or management of servers.

●      Billed per execution.

Why Multi-Cloud Serverless?

●      Reduces vendor lock-in.

●      Lets you pick the best service per provider.

●      Enables global high availability.

Section 2 – Serverless Services Compared

Feature	AWS Lambda	Azure Functions	GCP Cloud Functions
Runtime Support	Node.js, Python, Go, Java, .NET	Node.js, Python, Java, PowerShell, C#	Node.js, Python, Go, Java
Max Execution Time	15 minutes	60 minutes	9 minutes
Trigger Sources	S3, DynamoDB, API Gateway, EventBridge	HTTP, Event Grid, Service Bus, Blob Storage	Pub/Sub, Cloud Storage, Firestore
Scaling	Automatic (concurrency controls)	Automatic with Premium plans	Automatic
Pricing Model	Pay per request + duration	Consumption plan + Premium	Pay per request + duration
Integrations	Deep with AWS ecosystem	Deep with Microsoft/Azure ecosystem	Deep with Google Cloud ecosystem

👉 This table acts as a blueprint for choosing the right provider for each task.

Section 3 – Designing a Multi-Cloud Serverless Workflow

Imagine an e-commerce checkout system that uses all three clouds:

●      AWS Lambda: Validate payment → secure, high-concurrency environment.

●      Azure Function: Send invoice → integrates with Office 365 and Outlook.

●      GCP Function: Log transaction data → insert into BigQuery for analytics.

Blueprint Diagram (Described)

Visualize:

●      Left side → AWS Lambda triggered by API Gateway.

●      Middle → Azure Function triggered by Event Grid.

●      Right side → GCP Function triggered by Pub/Sub → BigQuery.

●      A flow arrow connects them sequentially → one global serverless chain.

At CuriosityTech Nagpur workshops, we build such realistic workflows step by step.

Section 4 – Multi-Cloud Triggers & Event Routing

●      Use EventBridge (AWS) → forward events to external services.

●      Use Azure Event Grid → route events between applications/clouds.

●      Use GCP Pub/Sub → cross-cloud message bus.

👉 Together, these create a multi-cloud event-driven backbone.

Section 5 – Security & IAM Considerations

●      Each provider requires separate IAM setup.

●      AWS Lambda → IAM roles with least privilege.

●      Azure Functions → Azure AD integration.

●      GCP Functions → Service Accounts with scoped roles.

Best Practice:
 Centralize secrets in HashiCorp Vault or Azure Key Vault and allow multi-cloud functions to retrieve credentials securely.

Section 6 – Multi-Cloud Observability for Serverless

Challenges: Logs are scattered across three clouds.

Solutions:

●      Export logs to a centralized monitoring platform (e.g., Datadog, Splunk).

●      Use OpenTelemetry to standardize tracing.

●      Example: AWS Lambda sends traces → OpenTelemetry → forwarded to GCP Monitoring.

At CuriosityTech labs, engineers learn how to normalize logging across providers, so debugging doesn’t feel like juggling three consoles.

Section 7 – Cost Management in Multi-Cloud Serverless

●      AWS Lambda: 1M free requests/month.

●      Azure Functions: 1M free executions/month.

●      GCP Functions: 2M free invocations/month.

👉 Multi-cloud lets you take advantage of free tiers across providers.

But danger lies in hidden network egress costs (moving data between clouds).

At CuriosityTech.in, we teach teams how to simulate workloads and calculate cross-cloud billing in advance.

Section 8 – Becoming an Expert in Multi-Cloud Serverless

True expertise comes not from just writing functions, but from:

1.    Designing event-driven architectures across providers.

2.    Mastering IAM and security in three ecosystems.

3.    Monitoring costs and performance together.

4.    Practicing with real-world projects.

Capstone Example: Build a real-time news aggregator where:

●      GCP Functions scrape feeds.

●      AWS Lambda filters keywords.

●      Azure Functions push notifications to Microsoft Teams.

Learners at CuriosityTech Nagpur complete such projects before they call themselves multi-cloud serverless engineers.

Conclusion

Serverless computing is more than just functions—it’s about building resilient, event-driven, cloud-native workflows.

In multi-cloud, the challenge is not writing code but orchestrating events, managing security, and connecting ecosystems.

AWS Lambda, Azure Functions, and GCP Functions each shine in their domains. Together, they form a powerful, cost-effective, scalable global fabric.

At CuriosityTech.in, we believe the future of serverless is not single-cloud isolation, but multi-cloud orchestration.