Qovery's top 10 blog posts of 2021 to read in 2022
- Heroku vs AWS: What to choose in 2022. Learn the key differences between Heroku and AWS and find out which one is better to use from the startup perspective.
- Best Heroku Alternatives. A look at the most comprehensive and modern alternatives to Heroku to follow in 2022.
- Blazingly fast Preview Environments for NextJS, NodeJS, and MongoDB on AWS. Preview Environments is the most remarkable and most used feature we released in 2021. Here is an article showing how to take advantage of it with NextJS, NodeJS, and MongoDB.
- Zero to Hero - How to deploy your apps on AWS in 30 minutes. A step-by-step guide on deploying your apps on AWS in 30 minutes (no AWS knowledge is required).
- Best Practices and Tips for Writing a Dockerfile. Learn about the key characteristics of Dockerfiles and some best practices to be aware of when you’re writing your own Dockerfiles.
- Terraform vs Pulumi: What to Use in 2022?. Learn about infrastructure as code and the relevance of Pulumi and Terraform as two of the most popular tools in the industry.
- Heroku vs AWS: What is the cheapest for your startup?. Should you choose Heroku or AWS as a more affordable option? We explain in this article the key things to consider when it comes to moving.
- Feedback: Improving Developer Experience with Data Science. A look at how (and why) our engineers embrace a scientific approach to collecting data, making measurements, and taking data-driven actions to improve the Developer Experience.
- Terraform is Not the Golden Hammer. A deep dive into our own experience using Terraform, explaining where, when, and how you should use it.
- Announcement: Pleco - the open-source Kubernetes and Cloud Services garbage collector. Look at how (and why) our engineers have open-sourced an internal tool to save tons of time and money to clean up unused Kubernetes and Cloud resources automatically.
Stick around! We’re excited about what is coming up this year.
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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.
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