Deep-Dive Engineering Articles
In-depth architectural breakdowns, real cost-reduction math, and hard-earned production lessons from scaling MLOps, LLM serving, and enterprise RAG systems.
How I Cut LLM Serving & Inference Costs by 40% on Bare Metal
Moving from managed cloud model APIs to bare-metal GPU clusters cut monthly inference spend by 63% while dropping p95 latency from 1.45s to 420ms. Here is the exact architectural blueprint, memory profiling, and deployment configuration.
Multi-Stage RAG with Azure AI Search: Real Production Lessons & Architecture
Why naive vector search fails on dense enterprise documents, and how a 5-layer multi-stage retrieval architecture with Azure AI Search, BM25, Reciprocal Rank Fusion, and Cohere Rerank achieved 0.98 faithfulness and 0.94 context precision.
Cloud Cost Optimization for AI Startups (2026): The AWS & GPU FinOps Guide
Enterprise cloud waste is a $44.5B/year problem, with 1 in 4 AI dollars lost to unmanaged token usage and over-provisioned infrastructure. Here is a practical FinOps roadmap to eliminate cloud waste on AWS and GCP.
The ML Leader's Guide: Don't Let Your Cloud Budget Burn Your Series A
How engineering leaders and CTOs can take back control of their AI cloud budget, enforce SLO-driven infrastructure governance, and protect runway without slowing engineering velocity.
Karpenter for EKS Cost Optimization: A Deep Dive with Real Config
Learn how to cut EKS compute costs up to 70% with Karpenter. A real, copy-pasteable YAML config, spot vs. on-demand strategy, and the exact math — tradeoffs included.