Joining a tech community?! Check out how Qovery community is rocking it!
Learning new technologies and their applications in today’s world is not always easy. That’s why at Qovery, we are developing a different approach towards the tech community. Join this adventure in all the different ways; we are reaching the tech community!
Community Calls
Once a month, the Qovery team holds a group call! Talking about:
- What we did this month
- What we are going for the next month
- Presenting some demos
- Live Q&A
This is an important space since we listen from first instance the needs of developers and their dreams for an amazing tech tool that will help them deploy better and faster.
Got questions? Found bugs? Getting started?
This is the place where your voice is heard, and your suggestions are more than welcome!
Qovery Forum has different categories: General, News, Questions and Answers, Feedback, Bugs, Beta v2.
Ask your questions, find answers, reply and support others in their path! We are learning and growing together.
Building together is important! In this “Roadmap” space, you have the opportunity to submit an idea for the product we are building at Qovery!
Got an idea? Just click “Submit an idea”, then a new card will show up at the Under Consideration section!
You can also vote for other ideas,
Also, check the other sections: PLANNED, IN PROGRESS, LAUNCHED.
Join all our adventures in building the best way to make deployment easy!
Blogging! Of course! Tech life lessons, best tips on tech performance, how Qovery works, and more! Blogs are written not only by our team but also by our Ambassadors and other people from the tech community!
Want to contribute? Check out more here.
One of my favorite first blogs is this one - The Qovery inside story at Techstars
Learning how to create and build a great product 🙌
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So many others to mention about the community: Guides, Tutorials, Goodies, Engineering, GitHub! Are you already joining us? What are you trying first?
Also, we are thrilled to announce that we have an “Ambassadors program”. More details in another blog!
Join us!
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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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