CAP Theorem
In distributed systems, network partitions are a reality. Architecture choices often define how consistency and availability behave when those failures occur.
Architecture
Architecture is a series of trade-offs.
There is no universal “best” architecture. The right system design depends on product goals, scale, consistency needs, team maturity, operational constraints and delivery speed.
First principles
Start with principles, not buzzwords.
Scalability
Design for growth where it actually happens — users, data, traffic and team — without distributing the system too early.
Reliability
Failure is expected. Timeouts, retries, isolation and recovery paths keep products usable when parts of the system are not.
Security
Identity, least privilege and data protection are architecture concerns, not a layer added after launch.
Observability
Logs, metrics and traces should make production behaviour visible before users have to report it.
Maintainability
Clear boundaries and boring operational paths matter more than fashionable diagrams.
Patterns
Patterns we apply based on the problem.
Modular Monolith
A strong choice for early and mid-stage products that need fast delivery without unnecessary distributed complexity.
- Best when
- Single product, one team, fast iteration.
- Watch out for
- Blurred module boundaries that later resist extraction.
Microservices
Useful when domain boundaries, scaling characteristics and team structure justify independent services and deployments.
- Best when
- Clear domains, independent scale, multiple teams.
- Watch out for
- Network chatter, data duplication and operational load.
Event-Driven Architecture
Well suited for asynchronous workflows, decoupled processing and system integrations.
- Best when
- Async workflows, fan-out, integration-heavy products.
- Watch out for
- Eventual consistency and harder end-to-end tracing.
Multi-Tenant SaaS
A practical model for serving multiple customers from a shared platform with tenant-aware boundaries.
- Best when
- Shared product, many customers, cost efficiency.
- Watch out for
- Noisy neighbours and leaky tenant isolation.
Layered Enterprise Systems
Structured systems with clear separation across presentation, business, integration and data layers.
- Best when
- Complex domains, long-lived enterprise processes.
- Watch out for
- Rigid layers that slow simple change.
Cloud-Native Platforms
Containerized and operationally mature systems built for automation, scaling and resilience.
- Best when
- Elastic load, automation-ready teams, multi-env delivery.
- Watch out for
- Platform cost and complexity without the operating model.
Decision framework
How we choose the right architecture.
Architecture should fit the product and organisation — not the other way around.
Product stage
Prototype, growth or established operations.
Team size
Who owns, deploys and supports the system.
Scale expectations
Traffic, data volume and geographic reach.
Consistency needs
What must be immediately true vs eventually true.
Integration complexity
How many systems the product must talk to.
Security needs
Identity, tenancy, data classification and audit.
Operational maturity
Monitoring, on-call and release discipline.
Budget and time-to-market
What the organisation can run this year.
System design
System design pillars
Scalability
Grow the parts that actually constrain the product.
Resilience
Contain failure so the rest of the system can continue.
Data Design
Model ownership, consistency and access with intent.
Security
Protect identity, tenancy and data at the architecture layer.
Observability
Make production behaviour measurable and explainable.
Cost Efficiency
Pay for scale you need, not ceremony you don’t.
Delivery Speed
Keep the path from change to production short and safe.
Maintainability
Leave a system a team can still reason about in two years.
Architecture
Built for Scale
A straightforward path from product surfaces to cloud infrastructure — clear layers, explicit contracts, and room to grow.
- Web / Mobile
- API Layer
- ServicesAuthCoreBillingNotify
- Messaging
- Data
- Cloud
- Monitoring
Philosophy
We don’t force one architecture on every problem.
We don’t over-engineer simple systems.
We don’t under-design systems that need scale and resilience.
We choose architecture based on product, people and operational reality.
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