Avoid Serverless Pitfalls: Key Anti‑Patterns and Proven Design Patterns

Introduction

In 2014 we launched the Lambda service, sparking the Serverless revolution. Today, more than half of our own applications run on Lambda, and an increasing number of customers are adopting Serverless to maximize cloud value. This article discusses not only Lambda, API Gateway, Step Functions, or EventBridge, but also how Serverless enables rapid prototyping, cost control, high availability, auto‑scaling, and efficient operations—key factors when choosing an initial application architecture.

Serverless as a Powerful Driver for Experimentation, Learning, and Competitive Advantage

Design patterns are the foundation of architecture; each pattern addresses a recurring problem with a reusable solution. By leveraging these patterns, architects worldwide can build on prior experience and make informed design choices. This article attempts to summarize common Serverless application design patterns.

Anti‑Pattern Examples

Before analyzing Serverless design patterns, let’s first discuss anti‑patterns—what something is not can be easier to grasp than what it is.

1. Lambda Functions Become Monoliths

Writing a bulky Lambda that contains logic for many event triggers may be quick at first, but as complexity grows the code package enlarges, cold‑start latency increases, execution slows, IAM roles become overly permissive, and testing and team scaling become difficult. Single‑task Lambda functions should be the starting point for defining boundaries.

2. Lambda Functions Become Orchestrators

Embedding an entire workflow inside a Lambda makes the code hard to read, maintain, and test, and forces developers to handle error‑handling and retries manually. Using Step Functions to define state machines (or EventBridge for event‑driven coordination) provides versioned, observable, and retry‑capable orchestration.

3. Lambda Calls Lambda

Synchronous Lambda‑to‑Lambda calls block the caller, increase cost, complicate error handling, and create concurrency bottlenecks. To avoid this, decouple functions with an SQS queue or move orchestration to Step Functions, keeping each Lambda focused on business logic.

4. Event Infinite Loops

Because Lambdas are event‑driven, a function that emits new events can inadvertently create a loop, causing uncontrolled scaling and resource consumption. Break loops by throttling concurrency, using forward triggers, and monitoring with CloudWatch alarms.

For more details, see James Beswick’s article “Operating Lambda: Anti‑patterns in event‑driven architectures – Part 3”.

Common Design Patterns

We are building increasingly complex platforms while meeting rapidly changing business needs. Serverless is ideal for fast, continuous delivery without managing infrastructure, allowing code to be broken into small, understandable units that are easy to change and deploy. Design patterns promote best practices and reusable solutions for common cloud challenges.

Peter Sbarski identified five common Serverless patterns in his book *Serverless Architectures on AWS*; Serverless Land expands this collection with templates and community contributions (see http://serverlessland.com/patterns/).

1. Command Pattern

The Command pattern encapsulates a request as an object, allowing it to be passed as a parameter, queued, or undone. In Serverless, this decouples API Gateway from specific request types, enables versioning, and reduces coupling between callers and receivers.

2. Messaging Pattern

Asynchronous messaging is the backbone of many service integrations, providing loose coupling and resilience. Using SQS (or SNS + SQS) to queue messages allows producers to continue operating while consumers process at their own pace, improving scalability and fault tolerance.

3. Priority Queue Pattern

When different messages require different processing urgency, separate SNS topics, Kinesis streams, or SQS queues can be used. High‑priority messages may be routed to more expensive, higher‑capacity APIs, while lower‑priority work follows a slower workflow.

4. Fan‑Out Pattern

Fan‑out uses an SNS topic to broadcast a single event to multiple subscribers (e.g., several Lambdas, email, or SQS queues). This enables parallel processing of a single trigger, improving throughput and fault isolation.

5. Pipe and Filter Pattern

This pattern breaks a complex task into a series of fine‑grained Lambda functions (filters) connected by a pipeline. Each function should follow the single‑task principle, be idempotent, and have clearly defined inputs and outputs.

Ensure Lambda functions adhere to the single‑task principle

Make functions idempotent so the same input always yields the same output

Define clear interfaces for inputs and outputs

Consumers need only know how to invoke the function and what result to expect

Conclusion

This article highlighted essential Serverless anti‑patterns and common design patterns that should be considered before building an initial architecture. The anti‑patterns discussed are:

Lambda functions become monoliths

Lambda functions become orchestrators

Lambda calls Lambda

Event infinite loops

The design patterns presented are:

Command pattern

Messaging pattern

Priority queue pattern

Fan‑out pattern

Pipe and filter pattern