About scaleops.ai

scaleops.ai publishes long-form technical writing about the operation and economics of cloud infrastructure. The scope is narrower than it sounds: five areas — Kubernetes operations, FinOps practice, platform engineering, AI infrastructure, and cloud security — connected by a common concern with what infrastructure decisions cost, in money and in operational burden, once they meet production.

What this covers

The material sits in a specific gap. Vendor documentation tells you how a feature works. Conference talks tell you it worked somewhere. Neither reliably tells you what happens on month nine, when the abstraction leaks, the bill arrives, or the autoscaler does something nobody modelled.

That is the territory here. How to pick a CPU request when the workload has a bimodal latency profile. What a service mesh costs when you include the engineer who maintains it. Why a cost allocation model that sums correctly can still be rejected by every team it reports on. Which GPU sharing mechanism you can safely put in front of another team, and which three you cannot.

Who it is for

Principally, engineers who own an infrastructure bill and have to justify it — platform engineers, SREs, infrastructure leads, and the engineering managers who sign off on their capacity plans. The writing assumes you know what a Deployment is, can read a Terraform module without a tutorial, and have opinions about at least one autoscaler.

It is also written for the smaller group of people doing FinOps work inside engineering organisations rather than inside finance ones, because that role has very little serious technical literature and a great deal of vendor material.

It is not written for executives looking for strategy summaries, or for people evaluating whether to adopt Kubernetes. Both are legitimate needs; neither is served here.

Editorial approach

Every article takes a position. A survey of six options with a balanced conclusion is easy to write and worthless to read. If there is a recommendation, it is stated, with the conditions under which it is wrong. Where the evidence genuinely does not support a position, that is said explicitly rather than dressed up as balance.

Specifics or nothing. Real tool names, real version constraints, real numbers, working configuration. Where a number comes from observation rather than published data, it is given as a range and identified as such. Where something is uncertain, it is hedged honestly rather than asserted confidently.

Costs are named. Any recommendation to adopt something includes what adopting it costs — not just in licence fees or compute, but in the ongoing operational attention it will require. A recommendation that omits the carrying cost is not a recommendation, it is an advertisement.

No vendor relationships. This site takes no sponsorship, accepts no paid placement, and has no affiliate arrangements with any tool or vendor discussed. Products are named because they are the ones people actually use. Display advertising is served programmatically through Google AdSense and is not connected to editorial decisions in any way — advertisers have no visibility into or influence over what gets published.

Corrections are made in place and marked. Technical writing ages, and some of it is wrong on the day it publishes. Substantive errors are corrected in the article body with a dated note rather than quietly edited.

On the writing itself

Articles run 1,200 to 2,000 words, which is long enough to make an argument with evidence and short enough to finish. There is no publishing schedule and no attempt to cover news. A piece appears when there is something specific to say that is not already well covered elsewhere.

Configuration examples are written to be read, not copied blind. They are illustrative of a pattern and will need adjustment for your environment — particularly anything involving resource values, which depend entirely on measurements from your own workloads.

Getting in touch

Corrections, disagreements, and requests for topics are all welcome, and technical disagreement in particular is useful — several articles here are better than their first drafts because someone pointed out a case they got wrong. See the contact page.