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By Mélanie Dallé
Training a model in a notebook is easy. What breaks teams is the step after, serving it reliably without haemorrhaging cloud budget or burying your SREs in YAML. The common trap: picking a platform that handles the model but not the surrounding stack. An AI deployment platform should orchestrate the full application graph (inference endpoints, vector databases, caching layers, and frontends) inside a single VPC, with GPU autoscaling that doesn't require a dedicated platform engineer to babysit.
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By William Occelli
Migrating from nginx Ingress to Envoy Gateway? Discover how Alan migrated 100+ services in one month, the technical hurdles they faced (like Content-Length normalization), and why staging isn't always enough.
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By Mélanie Dallé
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.
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By Morgan Perry
Optimizing Kubernetes on AWS is less about raw compute and more about surviving Day-2 operations. A standard failure mode occurs when teams scale the control plane while ignoring Amazon VPC IP exhaustion. When the cluster autoscaler triggers, nodes provision but pods fail to schedule due to IP depletion. Effective scaling requires network foresight before compute allocation.
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By Morgan Perry
Kubernetes is an open-source container orchestration engine. At enterprise scale, it abstracts infrastructure to automate deployment, scaling, and networking. However, managing hundreds of clusters introduces severe Day-2 operational toil, requiring agentic control planes to enforce global governance, security policies, and cost optimizations across multi-cloud fleets.
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By Mélanie Dallé
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.
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By Mélanie Dallé
The shift from AI copilots to autonomous agents is redefining infrastructure requirements. Discover how to build secure, stateful, and compliant Agentic AI systems using Kubernetes, sandboxing, and observability while meeting EU AI Act standards.
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By Mélanie Dallé
A Kubernetes single pane of glass is a centralized management layer that unifies visibility, access control, cost allocation, and policy enforcement across § cluster in an enterprise fleet for all cloud providers. It replaces the fragmented practice of switching between AWS, GCP, and Azure consoles to govern infrastructure, giving platform teams a single source of truth for multi-cloud Kubernetes operations.
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By Mélanie Dallé
At enterprise scale, managing provider-specific Kubernetes YAML across multiple clouds creates crippling configuration drift and operational toil. By adopting an agentic Kubernetes management platform, infrastructure teams abstract cloud-specific configurations (like ingress controllers and storage classes) into a single, declarative intent that automatically reconciles across 1,000+ clusters.
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By Qovery
Watch our interactive session to discover all the new Qovery features released Q4 2025 and learn how to fully leverage them with your team. Get hands-on insights and gain visibility into our upcoming roadmap.
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By Qovery
Qovery is a DevOps automation platform that lets developers deploy and scale applications on their cloud by simplifying and automating infrastructure so tech teams can focus on what matters most: building great products. Designed for modern and innovative companies, Qovery delivers the ease of a PaaS with the flexibility and control of your own cloud. Qovery takes the toil out of DevOps, freeing engineers to focus on what matters, while providing a refined experience and full control at every stage of scaling.
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By Qovery
Monitor your deployments with Qovery Observe: real-time metrics, logs, and events, directly integrated with your AWS applications and containers.
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By Qovery
It's been 4 months since our last demo day - it's time to show you what we have done and shipped during that time. In this 1-hour live session, I show you what's out now for the best Internal Developer Platform out there Qovery.
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By Qovery
How to turn Kubernetes into an Internal Developer Platform that your developers will love - it’s my new article, and it’s based on my experience building Qovery I share a few tips on how we iterate with our users, and this is gold for any Platform Engineering teams building or in the process to build their own platform. Enjoy the ride, and happy to have your feedback.
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By Qovery
Ever feel like your release process is a slow, clunky old car? It's time to turbocharge that machine! Join me for a fun and interactive 1-hour LinkedIn Live Event where we’ll unlock the magic of ephemeral environments to get your releases zooming along the fast lane using Kubernetes, Qovery, and GitHub Actions.
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By Qovery
Learn how to combine Neon and Qovery to get a full-stack Preview Environments system running on Kubernetes and the cloud provider of your choice.
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By Qovery
Romaric Philogène, CEO and Co-founder of Qovery - walks you through creating a complete, cost-effective Ephemeral Testing Environments pipeline in just 1 hour! Learn how to integrate GitHub Actions, K6, and Qovery to set up a seamless pipeline that enables you to deploy and test applications at speed, without breaking the bank.
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By Qovery
Hey guys, it's Romaric from Qovery. In this video, I'll show you how to combine Neon, a Postgres serverless solution, with Qovery to easily create and clone Postgres serverless instances. I'll walk you through the process step by step, demonstrating how to spin up a new serverless instance from Neon and connect it to a to-do application. The key point is that with Neon, you can create a branch from the original environment, make changes in the branch, and those changes will only affect that branch, not the parent environment. It's a powerful feature that allows for easy experimentation and isolation. So let's dive in and see how it works!
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Qovery is the first Container as a Service (CaaS) platform that allows anyone to develop and deploy an application in just a few minutes. We help growing tech companies to accelerate and scale application development cycle —with zero infrastructure management investment. Qovery is fully integrated to Github, Bitbucket and Gitlab. Once you have pushed your code, Qovery launch all the necessaries steps to make your application available online.
We know that today's applications do not just have just one piece of code and a database, but multiple applications (micro-services) and multiple databases (SQL, NoSQL, Cache..). Qovery has been designed for these complex cases and manages it in an incredible way.
Built for developers:
- CLI: We provide an amazing Command Line Interface (CLI) to manage all your services when needed. The interface has been designed to be instinctively usable.
- Support micro-services: Projects with multiple applications (micro-services) are supported natively by Qovery. We take care of all the plumbing for you (network, resiliency, deployment).
- Built on AWS: Qovery rely on the best services (Databases, VPC, Security..) provided by Amazon Web Services. But of course, we take care of all of the complexity of these services for you.
Deploy complex application, seamlessly. Just push your code, we handle the rest.