Serverless computing has matured from experimental technology to production-ready platform for many use cases. Functions-as-a-Service (FaaS) like AWS Lambda enables event-driven architectures that scale automatically from zero to massive throughput. However, serverless introduces constraints and patterns different from traditional server-based applications, requiring adjusted mental models.
When Serverless Makes Sense
Serverless excels for event-driven workloads with variable traffic patterns. Background processing, API backends with intermittent traffic, and scheduled jobs benefit from automatic scaling and pay-per-use pricing. Applications with unpredictable spikes avoid provisioning for peak capacity. However, long-running processes, latency-sensitive applications, and workloads requiring persistent state may suit traditional infrastructure better.
- Design functions to complete within timeout limits—typically 15 minutes maximum
- Minimize cold start impact through appropriate runtime choices and initialization optimization
- Use asynchronous invocation patterns for background processing and workflows
- Implement idempotent functions that handle duplicate invocations gracefully
- Monitor costs carefully as high-volume workloads can become expensive
State Management Patterns
Serverless functions are stateless by design, requiring external systems for state persistence. DynamoDB, S3, and managed databases store persistent state. Step Functions orchestrate complex workflows spanning multiple functions. Caching layers reduce database load and improve performance. Understanding state management options shapes effective serverless architectures.
Observability Challenges
Distributed serverless architectures create observability challenges. Correlation IDs track requests across function invocations. Structured logging enables efficient log analysis. Distributed tracing illuminates request paths through complex workflows. Cold start monitoring identifies performance issues. Comprehensive observability becomes more critical and more challenging in serverless environments.