Research & practice
Better questions. Better stack decisions.
Original guides, worked examples and source-backed methods for teams, founders and independent builders.
Start With the Workload, Not the Model
A practical brief turns an overwhelming model market into a testable shortlist.
Read the guide →Evaluation · 3 min readFive Core Dimensions of an AI Stack Decision
Read cost, capability, speed, context, and latency with reliability as the operating constraint.
Read the guide →Economics · 3 min readThe Token Price Is Only the Beginning
Build an AI cost model that includes accepted work, infrastructure, and operating effort.
Read the guide →Evaluation · 3 min readDesign an Evaluation That Can Disprove Your Favorite Choice
A fair experiment separates impressive examples from dependable task performance.
Read the guide →Infrastructure · 3 min readChoose the Cloud for the Workload You Actually Run
Separate application hosting, managed inference, and GPU operations before comparing providers.
Read the guide →Operations · 3 min readReliability Begins Where the Demo Ends
Measure successful work, control retries, and design a recovery path users can understand.
Read the guide →Governance · 3 min readA Decision Trace Makes an AI Recommendation Accountable
Record inputs, evidence, trade-offs, and review triggers so a shortlist can be challenged and reproduced.
Read the guide →Cloud Economics · 6 min readCloud GPU Cost Architecture: From Hourly Price to Useful Work
A rigorous way to translate GPU rates, utilization, memory, storage, networking, and operational overhead into cost per accepted workload outcome.
Read the guide →Architecture · 6 min readAPI or Self-Hosted AI? Build the Break-Even Model Before You Choose
A workload-led framework for comparing managed AI APIs with dedicated inference, including fixed cost, variable cost, control, risk, and team capacity.
Read the guide →Infrastructure · 6 min readGPU Inference Capacity Economics: Utilization, Batching, and Resilience
How traffic shape, queueing, batching, context, autoscaling, and failure recovery determine the real economics of production AI inference.
Read the guide →Prepared with AI assistance and linked primary sources. Hypothetical examples are labeled. Our editorial policy