Daily engineering insights, practical guides on architecture and AI cost, plus five real product stories — the infrastructure decisions made early, or made too late, that shaped what they became. Same lens we use on every Yogreet build: what would this have cost to get right from day one?
Learn to find and eliminate idle resources in your cloud infrastructure to cut costs by up to 30%.
Learn how to tackle autoscaling cold starts to keep P99 latency flat while minimizing idle capacity costs.
Learn how to right-size Kubernetes requests and limits effectively to prevent outages and optimize resource usage.
Explore why shared databases undermine microservices and how per-service data ownership enhances scalability and reliability.
Explore how microservices can inadvertently become tightly coupled and strategies to prevent the distributed monolith trap.
Explore when to choose event-driven vs request/response in microservices, optimizing costs and performance for your startup.
Explore effective strategies for defining service boundaries between business capabilities and technical layers in microservices architecture.
Learn how to extract your first microservice from a monolith using the strangler-fig pattern without causing outages.
Explore the nuanced trade-offs of streaming vs batching LLM responses for startups, optimizing cost and latency effectively.
Learn how to implement graceful degradation strategies for LLM rate limits, ensuring reliability and cost efficiency in your applications.
Learn how summarizing conversation history can reduce context window costs in LLM applications, enhancing efficiency and saving money.
Learn how token budgets can help enforce AI spend caps and prevent unexpected costs for startups.
The four levers that actually move an AI bill — caching, routing, batching, output discipline — ranked by impact, with the quality trade-offs spelled out.
Not a religious war — a staging decision. When a modular monolith wins, the three signals that justify a split, and how to migrate without a rewrite.
The real cost drivers — tokens, infrastructure, data — why per-user cost creeps up, and how to keep it flat from 100 to 100,000 users.
The four causes of the expensive rewrite that lands right when growth works — and how designing clean seams early avoids it entirely.
How a tiny team avoided a rewrite by designing their database to be shard-friendly before they ever needed to shard it.
WhatsApp didn't out-hire its way to scale. It out-architected everyone else's headcount with one unfashionable language choice.
A single database corruption in 2008 triggered a seven-year, full-stack rebuild — the most expensive "re-architecture spike" in tech history.
The product Tiny Speck spent years building wasn't the one that mattered. The internal tool built "just to get by" was.
The one story on this list where nobody had to learn the lesson the expensive way — because the architecture was the product.