Insights
Perspectives on platform engineering, Kubernetes, and the operating decisions that shape modern engineering organisations.
The CTO's AI Platform Inventory: 8 Questions You Should Be Able to Answer in 30 Seconds
If you are an engineering executive and your platform now runs AI workloads, there are eight questions a regulator, customer, or board member could ask that you should be able to answer instantly. Most CTOs cannot answer four of them.
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Why FinOps Just Became an Engineering Leader Problem
FinOps used to be a function you could delegate. AI workloads, platform sprawl, and architectural decisions that have direct cost consequences have changed that. Cloud cost is no longer something engineering leaders can keep at arm's length.
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Digital Sovereignty Is Now a Platform Engineering Problem
Sovereignty used to be a procurement concern. European regulation, geopolitical pressure, and customer scrutiny have turned it into an engineering decision. Most platform teams are running a sovereignty model they have not deliberately chosen.
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VMware to Kubernetes: What Most Migrations Get Wrong
The VMware-to-Kubernetes migration wave is the largest enterprise platform shift since cloud adoption. Most organisations are underestimating what it takes to do it well, and the cost of doing it badly compounds for years.
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AI Coding Tools Won't Fix a Broken Delivery Platform
AI coding tools amplify the systems they sit on. On a strong platform, that produces faster delivery. On a broken one, it produces more broken work, faster. Most organisations are about to discover which one they have.
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Your Kubernetes Platform Is About to Become the AI Control Plane
Every major industry signal in 2026 points to the same conclusion: Kubernetes platforms are becoming the operational layer for AI delivery. Most platforms were not built for this, and the gap between ready and not ready is widening fast.
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Backstage Is Not Your Platform
Backstage is the most widely adopted developer portal in the world, and its adoption is producing a predictable and expensive misconception: that buying Backstage gives you a platform. It does not. It gives you a catalogue.
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What to Check Before You Trust an Inherited Kubernetes Estate
You've just taken over a Kubernetes platform you didn't build. Before you sign off on its risk, cost, or roadmap, here's what an honest assessment needs to look at - and the red flags worth slowing down for.
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When Your Platform Team Becomes a Tax, Not an Asset
Platform teams are meant to multiply engineering output. Some don't. Here's how to tell whether yours is creating leverage or consuming it - and what to do when the answer is uncomfortable.
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If Your Platform Team Is Producing Roadmaps, Something Is Wrong
Platform teams that operate like project teams produce roadmaps. Platform teams that operate like product teams produce something else. The difference matters more than most engineering leaders realise.
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The Org Chart Problem: When Platform Engineering Reports to the Wrong Person
Where the platform team sits in your org chart predicts whether it will succeed more reliably than any technical decision. Here's what each reporting line produces, and how to know when yours is wrong.
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How to Talk About Platform Engineering to a Non-Technical Board
Most platform engineering investments don't fail in production. They fail in the boardroom. Here's how to frame platform work for executives and directors who don't care about Kubernetes.
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Platform Maturity Model: From ClickOps to Self-Service
Most platform teams don't have a tooling problem. They have a maturity problem. Here's a practical five-stage model for where your platform sits and what it takes to move forward.
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There Is No Correct Platform Team Size
The right platform team size isn't a ratio. It's a function of how much complexity the platform carries. Here's why some small teams support hundreds of engineers while large teams struggle with fifty.
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Platform Teams Don't Get Cut Because They're Not Valuable
Platform teams get cut because they can't prove value, not because they lack it. How product ownership drives adoption, the metrics that matter, and how to make platform engineering visible to leadership.
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Nobody Decided to Have 100 Kubernetes Clusters
Kubernetes cluster sprawl is one of the most expensive problems in platform engineering. A decision framework for multi-cluster management, consolidation, and when a new cluster is actually justified.
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How Platform Teams End Up With Six-Figure Observability Bills
A practical comparison of Datadog vs the open-source LGTM stack (Loki, Grafana, Tempo, Mimir). How observability costs spiral, what the migration looks like, and when open source is the right move.
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If You Rotated Every Credential Today, Something Would Break
Secrets sprawl makes credential rotation dangerous instead of routine. A practical guide to consolidating Kubernetes secrets management, building a rotation strategy, and eliminating credentials scattered across five systems.
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GitOps Is the Right Model - But Not Before Your Platform Is Ready
GitOps gives you auditable, drift-free Kubernetes deployments. But scaled too early, it enforces inconsistency. A phased guide to GitOps adoption, repo structure, secrets management, and when to use Argo CD vs Flux.
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Why AI Inference Is Creating a New Kind of Platform Engineer
Companies are hiring for AI inference platform work under a dozen different titles. The role exists - it just doesn't have a name yet. Here's what it looks like, why it's emerging, and why the label matters.
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Most AI Platform Work Starts in the Wrong Layer
Most organisations building AI platforms start with the model layer and hope the infrastructure sorts itself out. It doesn't. Here's why starting with the platform layer produces better outcomes.
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GPU Spend Is a Platform Problem, Not a Model Problem
GPU costs spiral when there's no platform governance. The fix isn't cheaper models - it's the same discipline platform teams already apply to CPU and memory: quotas, right-sizing, visibility, and accountability.
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Your Kubernetes Platform Isn't Ready for AI Inference Workloads
Most Kubernetes platforms weren't designed for AI inference workloads. GPU scheduling, latency-sensitive serving, cost governance, and operational models all need rethinking before inference hits production.
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Welcome to the KubeWright Blog
Lessons from real platform engineering engagements - the decisions that matter, the mistakes that cost, and what actually works at scale.