Notes from the work.
Practical writing on DevOps, MLOps and AI engineering — cost models, hiring trade-offs, stack recommendations. About one post a month, no marketing fluff.
Choosing a backend stack in 2026: .NET, Django, Node, Laravel or Go
There’s no single best backend in 2026. Here’s how to actually choose between .NET, Django, Node, Laravel and Go — matched to your team, your problem and your hiring reality.
AIManaged or self-hosted AI models: Azure AI Foundry, NVIDIA NIM, or your own
When to use a managed model on Azure AI Foundry or NVIDIA NIM versus self-hosting your own open-weight model — a practical guide on cost, control and compliance.
MLOpsMLOps starter stack for a 5-person data team in 2026
If you’re a small data team graduating from notebooks, here’s the minimum viable stack — experiment tracking, model registry, pipelines, monitoring — without buying a platform.
DevOpsHow much does it cost to migrate a small SaaS to Kubernetes?
A grounded breakdown for a 10–30 person SaaS team: cloud costs, consulting costs, engineering time, and the hidden ones — with two real numbers you can sanity-check against.
HiringFractional DevOps consultant vs full-time hire: when each makes sense
A clear framework for engineering leaders deciding between hiring a platform engineer, retaining a fractional consultant, or putting a one-off engagement on the calendar.
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