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 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.
Learn how to route non-urgent AI tasks to Batch APIs, reducing costs by ~50% while maintaining user experience. Explore concrete steps and insights.
Explore the trade-offs between prompt caching and fine-tuning LLMs to optimize costs effectively for startups.
Learn how to set model-routing thresholds to optimize requests to frontier models, balancing cost and performance effectively.
Explore how semantic caching can reduce LLM costs by 70% while understanding the risks of inaccurate responses.
Learn how to design IDs and keys for effective sharding to enable cost-efficient scaling from day one.
Explore when pgvector outperforms dedicated vector stores for cost-effective, scalable vector DB solutions.
Learn to model scaling thresholds for microservices, avoiding pitfalls at 10x and 100x growth with actionable insights.
Understanding cost per user can reveal if your architecture scales effectively 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.