Serverless Platform Architecture: Building Event-Driven Systems

Modern organizations use serverless architectures to build event-driven systems that scale seamlessly while reducing operational overhead and cost. At PADISO, we design and deliver serverless platforms across AWS and Microsoft Azure for mid-to-large organizations in Australia and the United States. This guide explains the patterns, reference architectures, and operating model you need to build robust event-driven platforms.

Why serverless and event-driven now

Serverless accelerates time-to-market by removing infrastructure management. Event-driven design decouples services, improving resilience and scalability. Pay-per-use economics reduce idle costs while aligning spend to value. Native cloud services speed delivery and eliminate undifferentiated heavy lifting.

Core building blocks

• Compute: AWS Lambda, Azure Functions for stateless execution
• Messaging: Amazon EventBridge, SNS/SQS; Azure Event Grid, Service Bus
• Storage: DynamoDB/Cosmos DB for transaction data; S3/Blob for objects
• API layer: API Gateway or Azure API Management
• Orchestration: Step Functions or Durable Functions for long-running workflows
• Observability: CloudWatch/Application Insights, OpenTelemetry

Reference architecture for an event-driven platform

• Producers emit domain events to an event bus
• Routing rules distribute events to bounded-context consumers
• Consumers trigger functions that perform business logic and persist state
• Orchestrations manage retries, compensations, and sagas
• APIs expose read models for applications and partners

Designing events and contracts

• Use canonical event schemas per domain
• Version events with additive changes; avoid breaking fields
• Include correlation and causation IDs for traceability
• Document contracts alongside code in a shared catalog

Patterns that work in production

• Outbox pattern to ensure exactly-once event publication
• Sagas for cross-service transactions
• CQRS for fast queries and independent scaling
• Idempotency keys for safe retries
• Dead-letter queues for poison messages

Security and compliance

• Enforce least privilege IAM/Role assignments
• Encrypt data in transit and at rest
• Use private networking and VPC endpoints where possible
• Log access decisions and maintain audit trails

Cost control strategies

• Right-size memory/timeouts; monitor p95 durations
• Batch messages where latency allows
• Use Provisioned Concurrency only for cold-start sensitive paths
• Archive cold data to cheaper storage tiers

Reliability engineering

• Adopt exponential backoff with jitter
• Circuit breakers for downstream dependencies
• Regional redundancy and disaster recovery runbooks
• Chaos testing for event backlogs and failure modes

Operating model and SLAs

• Define SLOs per event stream (latency, backlog depth, error budget)
• Automate runbooks for redrive and replay
• Use dashboards for throughput, failure rate, and cost per event

Migration approach

• Start with integration events around an existing core
• Carve out read models, then transactional slices
• Decompose monolith flows into orchestrated steps

Internal links to deepen learning

For more on API-first design, explore our guide: Internal Link: API-First Architecture: CTO Strategies for Modern Applications. For more on microservices boundaries, read: Internal Link: Microservices vs Monoliths: CTO Decision Framework. For cloud migration context, see: Internal Link: Cloud Migration Strategy: CTO Guide to Digital Transformation.

FAQs

How do we prevent duplicate event processing? Use idempotency keys, outbox pattern, and exactly-once consumers where supported.

How do cold starts affect latency? Mitigate with Provisioned Concurrency and language/runtime choices.

When should we choose queues vs event bus? Queues for point-to-point and backpressure; event bus for fan-out and routing rules.

How do we run long transactions? Use orchestrations with Step Functions/Durable Functions and saga compensations.

What about schema evolution? Favor additive changes and maintain versioned contracts.

How do we model failures? Adopt redrive policies, dead-letter queues, and replay tooling.

What observability is essential? Trace IDs on every event, structured logs, and metrics for lag and error rate.

Can we mix serverless with containers? Yes—use containers for long-lived or specialized workloads, functions for triggers.

How do we manage secrets? Use AWS Secrets Manager or Azure Key Vault with rotation policies.

What ROI can we expect? Typical clients see 20–40% infra cost reduction and faster feature delivery.

Conclusion

Serverless platform architecture enables event-driven systems that scale, reduce cost, and accelerate delivery. Adopt domain events, robust contracts, and strong observability to succeed with serverless and event-driven design. Ready to accelerate your digital transformation? Contact PADISO at hi@padiso.co to discover how our AI solutions and strategic leadership can drive your business forward. Visit padiso.co to explore our services and case studies.