Cto Advisor

# Architecture Decision Records (ADR) Framework

## What is an ADR?

Architecture Decision Records capture important architectural decisions made along with their context and consequences. They help maintain institutional knowledge and explain why systems are built the way they are.

## ADR Template

### ADR-[NUMBER]: [TITLE]

**Date**: YYYY-MM-DD  
**Status**: [Proposed | Accepted | Deprecated | Superseded]  
**Deciders**: [List of people involved in decision]  
**Technical Story**: [Ticket/Issue reference]

#### Context and Problem Statement

[Describe the context and problem that needs to be solved. What are we trying to achieve?]

#### Decision Drivers

- [Driver 1: e.g., Performance requirements]
- [Driver 2: e.g., Time to market]
- [Driver 3: e.g., Team expertise]
- [Driver 4: e.g., Cost constraints]

#### Considered Options

1. **Option 1: [Name]**
2. **Option 2: [Name]**
3. **Option 3: [Name]**

#### Decision Outcome

**Chosen option**: "[Option Name]", because [justification]

##### Positive Consequences
- [Consequence 1]
- [Consequence 2]

##### Negative Consequences
- [Risk 1 and mitigation]
- [Risk 2 and mitigation]

#### Pros and Cons of Options

##### Option 1: [Name]
- **Pros**:
  - [Advantage 1]
  - [Advantage 2]
- **Cons**:
  - [Disadvantage 1]
  - [Disadvantage 2]

##### Option 2: [Name]
[Repeat structure]

#### Links
- [Related ADRs]
- [Documentation]
- [Research/PoCs]

---

## Example ADRs

### ADR-001: Microservices Architecture

**Date**: 2024-01-15  
**Status**: Accepted  
**Deciders**: CTO, VP Engineering, Tech Leads  
**Technical Story**: ARCH-001

#### Context and Problem Statement

Our monolithic application is becoming difficult to scale and deploy. Different teams are stepping on each other's toes, and deployment cycles are getting longer. We need to decide on our architectural approach for the next 3-5 years.

#### Decision Drivers

- Need for independent team deployment
- Requirement to scale different components independently
- Different components have different performance characteristics
- Team size growing from 25 to 75+ engineers
- Need to support multiple technology stacks

#### Considered Options

1. **Keep Monolith**: Continue with current architecture
2. **Modular Monolith**: Break into modules but single deployment
3. **Microservices**: Full service-oriented architecture
4. **Serverless**: Function-as-a-Service approach

#### Decision Outcome

**Chosen option**: "Microservices", because it best supports our team autonomy needs and scaling requirements, despite added complexity.

##### Positive Consequences
- Teams can deploy independently
- Services can scale based on individual needs
- Technology diversity is possible
- Fault isolation improved

##### Negative Consequences
- Increased operational complexity - Mitigated by investing in DevOps
- Network latency between services - Mitigated by careful service boundaries
- Data consistency challenges - Mitigated by event sourcing patterns

---

### ADR-002: Container Orchestration Platform

**Date**: 2024-02-01  
**Status**: Accepted  
**Deciders**: CTO, DevOps Lead, Platform Team  
**Technical Story**: INFRA-045

#### Context and Problem Statement

With the move to microservices (ADR-001), we need a container orchestration platform to manage deployment, scaling, and operations of application containers.

#### Decision Drivers

- Need for automated deployment and scaling
- High availability requirements (99.9% SLA)
- Multi-cloud strategy (avoid vendor lock-in)
- Team familiarity and ecosystem maturity
- Cost considerations

#### Considered Options

1. **Kubernetes**: Industry standard, self-managed
2. **Amazon ECS**: AWS-native solution
3. **Docker Swarm**: Simpler alternative
4. **Nomad**: HashiCorp solution

#### Decision Outcome

**Chosen option**: "Kubernetes", because of its maturity, ecosystem, and multi-cloud support.

##### Positive Consequences
- Industry standard with huge ecosystem
- Multi-cloud compatible
- Strong community su