You usually discover the limits of a microservice system the hard way. Not when you split the monolith. Not when the first service goes live. You discover it six months
You start with the assumption that more LLM model options equal more flexibility. It feels like good architecture. Abstract: The provider, keep your options open, route dynamically based on cost
You’ve seen this pattern before. A team ships an AI-powered feature fast, proves value in weeks, and suddenly it becomes business critical before it ever becomes platform compliant. No observability
You’ve seen this play out. A quick workaround ships under pressure, wrapped in a comment that says “we’ll clean this up later.” Six quarters later, that workaround is now a
Most reliability failures do not begin with a dramatic outage. They begin with design choices that looked reasonable during the first six months of growth: a timeout value nobody revisited,
Asynchronous workflows look clean on architecture diagrams. A user places an order, a queue picks it up, a payment service charges the card, inventory reserves stock, shipping prints a label,
You usually do not notice a broken domain model when you design it. It shows up later, in awkward service boundaries, brittle integrations, and feature work that feels harder than
At a small scale, an API gateway feels like a convenience. It cleans up routing, centralizes auth, and gives you one place to hang rate limits. At a large scale,
You don’t notice resilience when everything works. You notice it when things break, and your system doesn’t. Picture this: your API depends on a payment service. That service slows down.