ScaleOps

90 seconds is not enough to show what a team of A-Players this is. We tried anyway. ScaleOps just wrapped its annual Scalers Club abroad - a weekend away from the day-to-day to recharge, bond, and enjoy being together. We're building something big at ScaleOps. Weekends like this are a reminder of what makes it possible - teams who actually like spending time together build better, bigger things together. And we're just getting started. Want to be on the next trip? We're hiring across all teams: https://lnkd.in/deVU_p64 Yassou 🇬🇷

We are at the #AWSSummit Tel Aviv 🇮🇱. The ScaleOps team is at Booth G6 ready to show you how we help organizations autonomously manage their AI infrastructure. See you at Booth G6 for a chat!

Big day for ScaleOps 🚀 Gilad Elyashar is joining us as Chief Product Officer. Together with Gilad and all the Scalers, we will continue to build the Autonomous Infrastructure Orchestration Platform for Enterprise AI in Production. Gilad, welcome aboard. Can't wait to build together! 💪

ScaleOps automates Kubernetes resource management. Over-provisioned pods, idle GPU memory, replicas nobody needs. Most teams fix this by hand, sometimes, when someone has time. ScaleOps does it autonomously instead: Real-Time Pod Rightsizing, Smart Pod Placement, and Node Optimization, running continuously in production. Less manual tuning, more consistent performance, and lower cloud costs as a result.

We're live on the ground at ContainerDays in Hamburg today and tomorrow! Come find our team in ScaleOps shirts for a chat. #ContainerDays #Kubernetes

Almost every Kubernetes cluster has at least one pod that can never be evicted, and most teams do not know it is there. New video: the 3 patterns that quietly block Karpenter and Cluster Autoscaler, and how to check for them. Watch below ⬇️

Always-on AI agents have a real cost. Most of it is a GPU sitting idle behind them, 77% of the time on the fleet we measured. Horizontal Pod Autoscaler scales on CPU and memory by design. It has no native GPU signal, so it once collapsed this fleet to one replica while the GPU sat pegged at 100%. Read the blog to see how we killed the idle GPU burn on this fleet, cold starts and all, without adding latency. Link in comments ⬇️

Last month, we wrapped up our strongest quarter to date. Celebrating growth and records is the easy part. The number that tells the real story is ARR per Scaler. We grew the team a lot over the last 12 months, and ARR per Scaler still grew 1.95x. That says something about the market, the product, and mostly - about the people building ScaleOps. AI workloads are everywhere now - inference, LLMs, AI agents, MCP servers, tools. All of it requires heavy compute, and all of it is very bursty. Companies cannot manage their resources manually at that scale. This is creating massive demand for autonomous AI infrastructure management, the problem we have spent four years building for. It drove our growth over the last few quarters, and demand is bigger than ever. We run AI-first as a principle. We research with AI, build with AI, provide value to our customers with AI, and decide with AI. And we have A players who are customer obsessed. AI makes a great team faster. That combination is the whole story. Proud to be building ScaleOps in this era, with this team. Nir Cohen, Taylor Grabus, Zak Blawie ☁☸️, Yarden Weber, Ben Grady, Joey Balázs, Rotem Ben Hamou, Eyal Zilberberg, Adi Steiner, and all the Scalers! This is just the beginning 🚀

Manual GPU tuning doesn't scale with AI workloads that change by the minute. ScaleOps autonomously observes how each AI workload actually uses its GPU, compute and memory, in real time. From there, it automatically assigns the right fractional GPU policy and keeps adjusting allocation as usage shifts. No static slicing. No manual retuning. Just GPUs running at the density your workloads actually need.