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?
Discover how to identify and eliminate N+1 queries to enhance database performance and ensure a successful product launch.
Discover how to manage data egress fees, a hidden cloud cost that startups often overlook, and optimize your cloud expenses effectively.
Learn how to determine the ideal baseline for committed-use savings plans, maximizing cloud cost efficiency for your startup.
Discover how an idle-resource audit can reveal hidden costs in your startup's cloud bill, enabling significant savings.
Explore how to manage autoscaling cold starts while keeping p99 latency flat without incurring idle costs.
Learn how to right-size Kubernetes requests and limits to optimize performance without triggering outages.
Explore per-service data ownership to prevent shared database pitfalls in microservices architecture.
Explore the distributed monolith trap in microservices and how to prevent tight coupling in your architecture.
Explore event-driven and request/response architectures for microservices, and learn how to choose the right approach for your service boundaries.
Explore how to define service boundaries between business capabilities and technical layers for optimal performance and cost efficiency.
Learn how to implement Strangler Fig migration for microservices extraction without outages, optimizing your startup's infrastructure.
Explore the trade-offs between streaming and batching LLM responses to optimize costs and latency for your startup.
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.