architecture-paradigm-serverless

---
name: architecture-paradigm-serverless
description: Applies serverless FaaS patterns for event-driven workloads. Use when designing bursty workloads with minimal infrastructure and pay-per-execution cost model.
license: MIT
---

# The Serverless Architecture Paradigm


## When To Use

- Event-driven workloads with variable traffic
- Minimizing operational overhead for cloud-native apps

## When NOT To Use

- Long-running processes exceeding function timeout limits
- Applications requiring persistent connections or local state

## When to Employ This Paradigm
- When workloads are event-driven and exhibit intermittent or "bursty" traffic patterns.
- When the goal is to minimize infrastructure management and adopt a pay-per-execution cost model.
- When latency constraints from "cold starts" are acceptable for the use case or can be effectively mitigated.

## Adoption Steps
1. **Identify Functions**: Decompose workloads into small, stateless function handlers triggered by events such as HTTP requests, message queues, or scheduled timers.
2. **Externalize State**: use managed services like databases and queues for all persistent state. Design handlers to be idempotent to validate that repeated executions do not have unintended side effects.
3. **Plan Cold-Start Mitigation**: For latency-sensitive paths, keep function dependencies minimal. Employ strategies such as provisioned concurrency or "warmer" functions to reduce cold-start times.
4. **Implement Instrumentation and Security**: Enable detailed tracing and logging for all functions. Adhere to the principle of least privilege with IAM roles and set per-function budgets to control costs.
5. **Automate Deployment**: Use Infrastructure-as-Code (IaC) frameworks like SAM, CDK, or Terraform to create repeatable and reliable release processes.

## Key Deliverables
- An Architecture Decision Record (ADR) that describes function triggers, runtime choices, state management strategies, and cost projections.
- A complete Infrastructure-as-Code (IaC) and CI/CD pipeline for automatically packaging and deploying functions.
- Observability dashboards to monitor key metrics including function duration, error rates, cold-start frequency, and cost.

## Risks & Mitigations
- **Vendor Lock-in**:
  - **Mitigation**: Where feasible, abstract away provider-specific APIs behind your own interfaces or adopt portable frameworks (e.g., Serverless Framework) to reduce dependency on a single cloud vendor.
- **Debugging Challenges**:
  - **Mitigation**: Tracing execution across distributed functions can be complex. Standardize on specific instrumentation libraries and structured logging to simplify debugging.
- **Resource Limits**:
  - **Mitigation**: Actively monitor provider-imposed limits, such as concurrency and memory quotas. Design workloads to be shardable or horizontally scalable to stay within these constraints.

## Concrete Components

These vocabulary items name the concrete tools and abstractions
that show up when the paradigm is implemented. They are not
required dependencies and they are not part of the skill's
``tools:`` frontmatter (which is reserved for Claude Code tool
restrictions). Use this list to disambiguate during architecture
discussions.

- ``cloud-sdk``: AWS SDK, Google Cloud SDK, or Azure SDK; first-class platform integration
- ``serverless-framework``: Serverless Framework, SAM, or CDK; declarative function deployment
- ``IaC-tools``: Terraform, Pulumi, or platform-native IaC for shared infrastructure around functions

## Exit Criteria

- [ ] An ADR documents function triggers, runtime choices, state externalization strategy,
  cold-start mitigation approach, and cost projections before any function is deployed.
- [ ] A complete IaC definition (SAM, CDK, Terraform, or equivalent) exists for every function
  and its supporting infrastructure, enabling repeatable deploys from scratch.
- [ ] Every function handler is idempotent: repeated execution with the same event produces the
  same outcome and no duplicate side effects (verified by unit test with replayed inputs).
- [ ] Observability dashboards cover function duration, error rate, cold-start frequency, and
  cost per invocation before the function reaches production traffic.