Infrastructure Architecture

Infrastructure architecture is the set of decisions that determine how your systems actually run: how they network, where they compute, how data is stored, who can access what, how changes ship, and how you observe it all. These choices shape reliability, security, and cost long before any feature is written.

The throughline of good infrastructure is repeatability and ownership: favor managed services and infrastructure as code over hand-built servers, and make security and observability first-class parts of the design rather than afterthoughts.

TL;DR

Quick Example

The layers every system architecture has to answer for — decide each deliberately:

Core Building Blocks

Best Practices

Common Mistakes

"Shared everything" environments

No cost governance until the bill arrives

FAQ

How do I choose between VMs, containers, and serverless?

By workload. Serverless suits spiky, event-driven, or low-ops workloads; containers suit long-running services that need portability and orchestration; VMs suit specialized or legacy workloads needing full OS control. Many architectures mix all three.

What does "managed services first" mean and why?

Prefer cloud-managed offerings (managed databases, queues, load balancers) over self-hosting equivalents. They remove undifferentiated operational work — patching, backups, failover — so your team spends effort on the product, not on keeping infrastructure alive. Self-host only when you have a specific reason.

How many environments do I need?

At minimum dev, staging, and production, built from the same IaC so they differ by configuration, not drift. Staging should resemble production closely enough to catch problems before release. Add ephemeral preview environments per pull request if your workflow benefits.

Where do security and observability fit in architecture?

In from the start. Identity/least-privilege, network segmentation, and encryption are architectural decisions, as are logs/metrics/traces and alerting. Bolting them on later is far harder and leaves gaps — design them alongside compute and data.

Related Topics

References