Your Valkey setup just got a serious upgrade. Aiven for Valkey now supports Valkey 9, and the performance gains are real. We're seeing significant throughput improvements, better memory efficiency, and hash field expiration for more precise data control. But that's not all, we're providing multi-version support. This flexibility allows you to safely test new features or maintain environment stability on a schedule that works for your team. See what's new: https://lnkd.in/ev2yFYQE #Valkey #Databases #CloudInfrastructure #PerformanceEngineering #DevOps
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When you’re building an infrastructure roadmap, the noise about "sovereign clouds" can be confusing. You have real work to do. You need performance, your team needs standard APIs, and you need data guarantees that actually stand up in court. Swipe through the roundup to see what the market is saying about us. 🫱 These quotes provide a candid look at how we are executing on that mission, balancing technical speed with 100% European jurisdiction. . . . #kubernetes #karpenter #sovereigncloud #EUTech #dataprotection
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The Hidden Complexity.. Observability stacks have quietly become… overengineered. Forwarders. Indexers. Pipelines. Licensing layers. What if you could run the same workloads with a fraction of the moving parts? We’ve been helping teams simplify—happy to share what that looks like with OpenObserve Prabhat Sharma, Manas Sharma #openObserve
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Dan Sullivan runs through the basics of Kubernetes, how we use it at Hydrolix, and how the decisions we’ve made allow us to run hundreds of customer clusters. https://lnkd.in/eJaeYuVa
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🚀 Karpenter v1.10.0 is out — and this release brings something platform teams really care about: 💰 Better cost visibility + safer lifecycle handling Two updates worth highlighting 👇 📊 New: NodePool cost metric Karpenter now introduces a NodePool-level cost metric, giving teams better visibility into how much each NodePool contributes to cluster cost. 👉 This is a big step toward cost-aware autoscaling, especially in environments mixing spot/on-demand or multiple workloads. Karpenter already optimizes node provisioning dynamically based on workload needs (AWS Documentation) — and this adds another layer: understanding cost impact per NodePool. 🛠️ Fix: Safer NodeClaim handling during deletion Fixed an issue where static capacity controllers could modify NodeClaims during NodePool deletion. 👉 This matters because NodeClaims control the lifecycle of nodes (karpenter.sh) — and unexpected mutations during deletion could lead to inconsistent state or cleanup issues. These are the kinds of changes that don’t just improve features — they improve operational safety and cost control. 🔎 Full release breakdown: https://lnkd.in/gKMwbjbX For teams running Karpenter — What matters more to you today: cost visibility or lifecycle safety? #Karpenter #Kubernetes #FinOps #PlatformEngineering #DevOps #SRE
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If you are currently evaluating #ClusterAPI , #BigConfig should be on your radar. Positioned as a 'Helm-style' package manager for any stack, it provides the essential primitives required to abstract the complexities of EKS, AKS, and GKE. While a dedicated Kubernetes cluster package is in development, BigConfig already offers the foundation needed to build a truly seamless multi-cloud solution. Link in the comments.
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𝗪𝗵𝘆 𝗺𝗮𝗻𝗮𝗴𝗲 𝗮 𝗳𝗿𝗮𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘀𝘁𝗮𝗰𝗸 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘂𝗻𝗶𝗳𝘆 𝗶𝘁 𝘄𝗶𝘁𝗵 𝗲𝗕𝗣𝗙? ⚙️ Separating CNIs, kube-proxy, and load balancers made sense at one point. Today, it often means more coordination, more hidden state, and more ways for things to break. #eBPF offers a different model where traffic handling lives directly in the data plane, making behavior more consistent and easier to reason about. Less about adding components. More about removing boundaries. 👉 Take a closer look at what this shift looks like in practice: https://lnkd.in/gguFtzvC #Kubernetes #eBPF #CloudNative #PlatformEngineering #DevOps #Networking
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Stateful apps deserve storage as elastic and resilient as your cluster — here’s how to get it right. In our new guide, "Ceph Kubernetes 2026: Powerful Guide to Scalable Storage," we unpack practical patterns for running durable, high-performance storage on Kubernetes. Key takeaways: - When to choose Ceph (RBD, CephFS, RGW) for stateful workloads - CSI/operator-led deployment and scaling strategies - Performance tuning, monitoring, and failure-recovery best practices - Real-world tradeoffs and cost/complexity considerations Whether you’re designing production stateful services or migrating legacy data layers, this guide gives actionable steps to scale storage with confidence. Read the full guide and tell me: what’s your biggest storage challenge on Kubernetes? #Kubernetes #Ceph #CloudNative
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Many businesses hit a wall with n8n performance as their automation scales. When webhooks time out or the editor becomes unresponsive during peak hours, it's a clear sign you need to consider n8n's queue mode. This architecture separates workflow execution from the main instance, leveraging Redis and worker processes to distribute the workload efficiently. It's not just about speed; it's about ensuring reliability and preventing resource contention as your operations grow. I've seen firsthand how crucial this is for maintaining seamless business processes. #n8n #WorkflowAutomation #AIImplementation #Scalability #TechStrategy https://lnkd.in/eiQwgi8T
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🚨 Metrics tell you something is wrong. Profiling tells you why. In this case study, teams used eBPF, Pyroscope, and OpenTelemetry to uncover real performance bottlenecks in Kubernetes workloads. By combining kernel-level profiling with tracing, they were able to identify inefficient code paths before customers experienced issues. A great example of modern observability in action. 🎥 Watch here → https://lnkd.in/dZW2TYsV 👉 Do you rely more on metrics, logs, or profiling? #Kubernetes #Observability #SRE #PerformanceEngineering
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We made our free plan 5x bigger because 1 cluster was never the real world. When we launched, we offered free scanning for 1 cluster. It was a good starting point. But it wasn’t how real teams operate. Most companies don’t have just one cluster. They have: - Production - Staging - A second region - A testing environment - And one cluster nobody wants to admit still exists :) So we changed it. You can now scan up to 5 clusters for free. Not a stripped-down trial. Not a demo. Not “free” until you hit the first useful feature. Just real visibility across the environments that actually matter. That means you can use KorPro to identify: - Orphaned Kubernetes resources - Cloud waste and savings opportunities - Cluster lifecycle / extended support exposure - Security and hygiene issues across more than a single environment I wanted our free tier to be valuable enough that an engineering team could connect their real setup and immediately get useful signal back. Because if the product only works on one perfectly clean cluster, it’s not solving the real problem. If you’re running Kubernetes and want to understand what’s being wasted, what’s outdated, and what’s been forgotten, you can now do that across 5 clusters for free. -> app.korpro.io -> sign up -> scan your first clusters #Startup #Kubernetes #DevOps #FounderMode #SaaS #CloudCostOptimization #PlatformEngineering #FinOps
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Great to see team