I1Insights
Field notes from production.
Technical deep-dives on production AI, RAG vs. fine-tuning, AI cost management, internal assistants, cloud security, IAM, and DevOps. No hype — practical guidance for modern infrastructure.
Cloud security hardening: a practical baseline
A no-hype baseline for hardening cloud environments: identity, network segmentation, encryption, and logging.
8 min readIAM mistakes startups make (and how to fix them)
Common identity and access misconfigurations in early-stage cloud setups and how to correct them without blocking velocity.
6 min readAI integration risks: what to control before going to production
Data leakage, prompt injection, cost drift, and compliance: what to address when moving from AI demos to production systems.
7 min readDevOps automation maturity: from manual to repeatable
A simple maturity lens for deployment and operations: where you are and what to improve next without over-engineering.
6 min readProduction AI vs. demos: what it takes to ship
Why most AI demos never become production systems—and what separates teams that ship from those that stall.
6 min readChoosing AI models for production: a practical framework
How to select models based on use case, cost, latency, and data sensitivity—without chasing the latest release.
7 min readInternal AI assistants: when they make sense
Company knowledge copilots, document search, and workflow assistants—when they add value and when they don't.
6 min readAI cost management in production
Quotas, caching, model routing, and governance patterns to keep LLM spend predictable and under control.
6 min readRAG vs. fine-tuning: when to use which
Retrieval-augmented generation and fine-tuning solve different problems. Here's a practical guide to choosing.
7 min readCost optimization strategies that don’t rely on guesswork
Right-sizing, reservations, waste detection, and governance: a structured approach to cloud cost without hype.
7 min read
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