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When engineering teams take on a new project, they often optimize for performance, availability, or fault tolerance. More experienced teams can optimize for these properties simultaneously. We can add an additional property: feature velocity. Organizations often try to optimize for feature velocity through process improvements and engineering hierarchy, but some optimize for feature velocity through explicit architectural decisions. These decisions increase the complexity of the system.
Mental models of approaching availability help us understand the tension between these engineering properties. For example, understanding the distinction between accidental complexity and essential complexity can help you decide whether to invest engineering effort into simplifying your stack or expanding the surface area of functional output. Incident remediation, approaches to automation, and diversity of engineering can all be understood through the perspective of these mental models. With insight and diligence, these models can be applied to improve availability over time and drift into a safer state for the system.