Meetup - The Present and The Future of Infrastructure as Code
Tell Me More!
The goal is to discuss Infrastructure as Code with several experts and lightning talks by the community (no boring sales pitches).
This HUG meetup will be in English and will talk about the Present and The Future of Infrastructure as Code:
- Pulumi, Terraform, CDK, other ?
- What do you think about Components and Higher-level constructs?
- Why is the DX experience an afterthought?
- Is type safety overrated?
- How many IaC tools does it take to change a light bulb?
- Ask your question beforehand here
Join us if you are curious about DevOps, Infrastructure as Code, Terraform, Pulumi, CDKs, and everything in between!
Agenda
â° Meetup on Tuesday 11th of October from 6 PM.
đ đ„€ 18:00 - Snacks and non-alcoholic drinks (sponsored by Couchbase). Time to mingle.
âĄïž 19:00 - Lightning talk - 10 minutes max, by the community, no sales pitches, straight to the point. Call for speakers is open!
đ€ 19:30 - Round table (in English) - the Present and The Future of Infrastructure as Code - with industry experts:
âïž Paul Stack works as an engineering manager at Pulumi Corporation and is an expert in DevOps, IaC, and many other areas.
âïž Matthieu Napoli is an AWS Hero, former PM of Serverless Framework, and author of Lift, integrating AWS CDK with Serverless Framework.
âïž Sebastian Korfmann is an AWS Hero and the original creator of Terraform for CDK.
âïž Anton Babenko (moderator) is an AWS Hero, Terraform influencer, and open-source maintainer of AWS Terraform modules.
Want to Join Us?
You need to be in Paris on the 11th of October and register by following this link!
.svg)
.svg)
.svg)
Suggested articles
The cluster coming up is the easy part. What catches teams off guard is what happens six months later: certificates expire without a single alert, node pools run at 40% over-provisioned because nobody revisited the initial resource requests, and a manual kubectl patch applied during a 2am incident is now permanent state. Agentic control planes enforce declared state continuously. Monitoring tools just report the problem.
The instinct when setting up Kubernetes observability is to instrument everything and send it all to your APM vendor. That works fine at ten nodes. At a hundred, the bill becomes a board-level conversation. The less obvious problem is the fix most teams reach for: aggressive sampling. That is how intermittent failures affecting 1% of requests disappear from your monitoring entirely.
Scaling your deployments to zero is only half the battle. If your cluster autoscaler does not aggressively bin-pack and terminate the underlying worker nodes, you are still paying for idle metal. True environment sleeping requires tight integration between your ingress layer and your node provisioner to actually realize FinOps savings.
The structure, table, tool list, and code blocks are all worth keeping. The main work is fixing AI-isms in the prose, updating the case study to real metrics, correcting the FAQ format, and replacing the CTAs with the proper HTML blocks. The tool descriptions need the "Core strengths / Potential weaknesses" headers made less template-y, and the intro needs a sharper human voice.
For years, Red Hat OpenShift has been the safe choice for heavily regulated, on-premise environments. It operates as a secure fortress. But in the public cloud, that fortress acts as an expensive prison. Paying proprietary per-core licensing fees on top of your standard AWS or GCP compute bill is a redundant "middleware tax." Escaping OpenShift requires decoupling your infrastructure from your developer experience by running standard, vanilla Kubernetes paired with an agentic control plane.
Agentic Kubernetes resource reclamation is the practice of using an autonomous control plane to continuously identify, suspend, and delete idle infrastructure across a multi-cloud Kubernetes fleet. It replaces manual cleanup and reactive autoscaling with intent-based policies that act on business state, eliminating the configuration drift and cloud waste typical of unmanaged fleets.
Kubernetes focuses on container orchestration, but the reality on the ground is far less forgiving. Provisioning a single cluster is a trivial Day-1 exercise. The true operational nightmare begins on Day 2. Teams that treat multi-cloud fleets like isolated pets inevitably face crushing YAML configuration drift, runaway AWS bills, and severe scaling bottlenecks.
.webp)