From the Engineering Team
What we have learned building GenAI apps, cutting cloud costs, and getting AI systems into production.
The Cloud Cost Playbook: How We Cut AWS Bills by 28%
AI workloads are the fastest-growing line item on most cloud bills. Here is the FinOps playbook we use to find 20-30% savings without touching performance.
RAG vs Fine-Tuning: When to Use Which (With Real Examples)
We have built six LLM-powered products in the past year. Three used RAG, two used fine-tuning, one used both. Here is how we decide which approach fits which problem.
Migrating from Monolith to Microservices Without Breaking Everything
A step-by-step migration strategy based on four monolith-to-microservices projects we have completed. Includes the strangler fig pattern, what to extract first, and common mistakes.
Building an AI Copilot That Employees Actually Use
We built an internal AI assistant for a 2,000-person company. Here is the architecture, the integration points, and how we got adoption from 12% to 68% in three months.
5 Kubernetes Mistakes That Are Doubling Your Cloud Bill
Over-provisioned resource requests, no autoscaling, always-on dev clusters. These are the Kubernetes cost mistakes we see on nearly every audit, and the fixes that save 30-50%.
Data Lakehouse vs Data Warehouse: We Have Built Both
When to use a lakehouse, when to stick with a traditional warehouse, and the real cost and performance differences based on five data platform projects.
Where Does Your ML Team Sit on the MLOps Maturity Scale?
A practical 5-level maturity model for ML operations. Most teams are at Level 1. Here is what each level looks like and how to move up without over-engineering.
Shipping AI in Healthcare: HIPAA, FHIR, and What Actually Matters
A practical guide to building AI systems that handle protected health information. Covers HIPAA requirements, FHIR interoperability, BAAs with cloud providers, and the compliance checklist we use.
Build vs Buy Your AI Platform: A Decision Framework
A structured framework for deciding whether to build or buy your AI/ML platform. Includes a scoring matrix, hidden cost analysis, and our recommendations by company size.