Notes from the build.
Practical guides on shipping production AI and cloud infrastructure on AWS — written by practitioners, for the teams doing the work.
Why Fractional Cloud Engineering Beats a Full-Time Hire for Many Teams
Hiring full-time cloud engineers is expensive for temporary problems. Discover why fractional cloud engineering—expert AWS practitioners on a part-time basis—delivers faster results and costs less for teams shipping on AWS.
Data Privacy and Generative AI: Keeping Customer Data Safe with Amazon Bedrock
Amazon Bedrock offers built-in data privacy controls including non-retention guarantees, encryption, and audit trails for production AI workloads. Learn how to architect for compliance while keeping sensitive customer data safe.
ECS vs. EKS vs. Lambda: Choosing Compute for Your Next Application
When choosing between ECS, EKS, and Lambda, the right answer depends on your workload, not trends. This guide cuts through the confusion with a practical framework: when each service wins, real costs, and how to make the decision with confidence.
Observability for AI Applications: What to Monitor and Why
AI systems fail differently than traditional software. Discover the five pillars of AI application observability—output quality, data drift, cost, reliability, and versioning—plus a practical roadmap to build monitoring that catches problems before your customers do.
From Prototype to Production: Hardening an LLM Application on AWS
Moving an LLM application from prototype to production requires more than deployment—it demands token budget controls, cost guardrails, data isolation, and observability. Discover the concrete technical decisions that separate demos from reliable systems on AWS.
Amazon Bedrock Pricing Explained: What You'll Actually Pay
Amazon Bedrock uses pay-per-token pricing that seems simple until you ship to production. This guide explains actual costs, token consumption patterns, and the architectural decisions that determine whether your AI application is profitable or costly at scale.
Fine-Tuning vs. RAG vs. Prompt Engineering: Choosing the Right Approach
When building AI applications on AWS, teams must choose between fine-tuning, RAG, and prompt engineering. Each approach has different economics, latency profiles, and operational complexity. This guide provides a decision framework to help CTOs and engineering leaders choose the right path.
How to Evaluate an AWS Partner: Questions to Ask Before You Sign
Choosing the wrong AWS partner is costly—wasted budget, delayed timelines, and systems your team can't operate. This guide walks you through the hard questions that separate practitioners from resellers.
IAM Best Practices for Small Teams Building on AWS
Implement AWS IAM best practices that let small teams move fast without sacrificing security. Learn account separation, federation, and automation patterns from practitioners who've built on AWS.
AWS Cost Optimization: A Practical Checklist for Engineering Leaders
AWS cost optimization requires more than occasional audits—it's an operating practice. This guide shares actionable strategies for engineering leaders to embed cost discipline into architecture and operations, typically achieving 25-40% savings within 12 months.
Serverless vs. Containers for AI Workloads on AWS
Serverless and containers each offer distinct trade-offs for AI workloads on AWS. This guide cuts through the hype with concrete cost modeling, latency analysis, and practical decision frameworks for engineering leaders.
AI Agents on AWS: A Practical Guide to Bedrock Agents and Custom Orchestration
Discover how to build and deploy AI agents on AWS with Bedrock and custom orchestration. Learn architecture patterns, cost governance, and operational patterns for production agentic workloads.
RAG on AWS: Architecture Patterns That Hold Up in Production
Building production RAG systems on AWS requires more than connecting services. Learn the architectural patterns, AWS service combinations, and operational disciplines that scale relieval-augmented generation systems reliably.
How Much Does It Cost to Build an AI Application on AWS?
Building AI on AWS involves multiple cost drivers—from foundation model inference to vector databases and infrastructure. This guide breaks down realistic monthly costs across three scenarios and shares optimization strategies to control spending.
AWS Landing Zone Best Practices for Startups and Small Teams
A pragmatic AWS landing zone isn't just for enterprises. Learn the core principles, implementation roadmap, and practical guardrails that let small teams scale securely without overengineering.
Amazon Bedrock vs. the OpenAI API: Which Should Your Business Build On?
When choosing between Amazon Bedrock and the OpenAI API for your AI application, data residency, model availability, and AWS integration are make-or-break factors. We break down the real trade-offs—and when each genuinely makes sense.
What It Really Takes to Ship a Production AI Application on AWS
Shipping production AI on AWS requires far more than model code and good intentions. Learn the infrastructure, observability, cost control, and operational patterns that separate successful deployments from expensive failures.
How to Choose an AWS Consulting Partner for AI Projects in 2026
Shipping AI on AWS requires the right partner. This guide walks you through how to evaluate AWS consulting partners for production AI workloads—what questions to ask, red flags to watch for, and what separates experienced firms from the rest.