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?
Explore prompt caching and fine-tuning for LLM cost reduction, with a decision framework and break-even analysis for startups.
Learn how to effectively choose model-routing thresholds for AI requests, optimizing costs and performance in your infrastructure.
Explore semantic caching for LLM apps, cutting costs by 70% but risking answer accuracy. Learn implementation steps and trade-offs.
Learn how to design IDs and keys for sharding readiness to ensure cost-efficient scalability from day one.
Explore when pgvector beats dedicated vector stores for cost-effective vector DB solutions at scale.
Learn how to model scaling thresholds effectively, ensuring your infrastructure withstands 10x and 100x growth without breaking.
Understanding cost per user reveals insights into your architecture's scalability. Learn to optimize for growth effectively.
Explore multi-tenancy data isolation patterns for B2B SaaS backends to optimize security and performance.
Explore backpressure and rate limiting techniques for AI endpoints handling burst traffic effectively.
Learn when to introduce a queue to decouple slow AI calls from your request path for improved backend performance.
Learn a contract-first approach to API versioning that prevents breaking changes and ensures client compatibility.
Explore how idempotency keys can make your APIs resilient against retries, improving reliability and performance 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.