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Saas for Startups
Playbooks and case studies covering saas for startups.
AI Evaluation Metrics Cheatsheet: Pick the Right One
Most teams pick an AI evaluation metric because it was easy to instrument, then discover months later that the number looked fine while a key account churned. This cheatsheet maps the metrics to the business decisions they actually encode.
How to Monitor an AI Feature After It Ships
Your APM dashboard will tell you the inference endpoint is healthy right up until users start churning. Here's the playbook for catching AI feature degradation before it costs you revenue.
Retrieval-Augmented Generation: What It Is and When It Breaks
Your competitor demoed an AI knowledge assistant and now the board wants one. Before you greenlight a RAG build, here is what it actually does, where it silently fails, and when fine-tuning or plain search beats it.
AI Feature Flags Cheatsheet: Rollout, Rollback, Observe
A reference for engineering teams who learned the hard way that UI feature flag tools don't catch model regressions. Gating rules, rollback triggers, and the signals worth wiring up.
LangChain vs. LlamaIndex: Pick One for Your AI Product
Your prototype works. Now you have to decide whether to bet a real production system on LangChain or LlamaIndex. This is the comparison that focuses on the dimensions that actually break under load, not toy benchmarks.
Managed AI Infra vs. Self-Hosted: Pick One
Your managed inference bill tripled and self-hosting looks cheaper on paper. It usually isn't. Here's the framework that actually decides it — and why the answer is a staffing question, not a compute one.
How to Run an A/B Test on an AI Feature Without Lying to Yourself
Your AI feature's A/B test showed a lift. Then it flatlined after rollout. Here's a playbook for running experiments on adaptive systems without lying to yourself about what the numbers mean.
Guardrails for LLMs: Why Output Validation Is Its Own Layer
Prompt engineering tells the model what you want. Guardrails enforce what your system will actually accept. The distinction matters more than most teams realise until a bad output reaches a paying customer.
Vector DB vs. Postgres pgvector: Pick One for Your AI Product
Your infra lead says pgvector won't scale and you need Pinecone or Weaviate. They might be right. They're also probably wrong for the reasons they think. Here's the framework that actually matters.
Fine-Tune an Embedding Model on Your Own Docs in 6 Steps
Your RAG pipeline keeps returning confidently wrong passages, and you've already exhausted chunking and re-ranking tricks. The defect is in the embedding model itself — here's how to fix it with 500 pairs from your query logs.
How to Cut AI Inference Costs Without Touching Your Model
Most AI inference overspend is not a model-size problem — it's a request-routing problem. Here's the playbook for fixing it without touching your model or losing output quality.
Your AI Feature Doesn't Need More Data. It Needs a Harder Objective.
Most AI feature stagnation is not a data quantity problem. It's an objective mismatch — your model is perfectly optimizing a proxy metric that quietly diverged from the outcome users actually care about.