Models
Autonomous Resource
Management for AI Models
Large language models, embedding services, and fine-tuned models demand unpredictable resources. ScaleOps continuously manages GPU allocation, replica count, and batch sizing beneath your inference and training workloads, cutting infrastructure costs without compromising latency or throughput.
Continuously optimize inference, training, and fine-tuning
Inference and training workloads consume resources unpredictably, and standard sizing gets set once for a peak that rarely arrives.
Built for the compute demands of AI/ML in production
AUTOMATED FRACTIONAL GPUs
Dynamically allocate fractional GPUs
Not a on-time recommendation.
Static configs and one-time recommendations leave idle GPU capacity on the table. ScaleOps treats GPU rightsizing as a continuous process, updating allocations as workloads change and ensuring that each workload gets the fraction needed every time.
GPU MEMORY OPTIMIZATION
Reclaim overallocated GPU memory and unlock sharing
No manual profiling.
Over-provisioned memory reservations block GPU sharing. When a workload holds more VRAM than it needs, GPUs can’t binpack, sit idle, and can’t be shared. ScaleOps corrects overprovisioned allocations automatically, unlocking GPU sharing eligibility without any manual profiling.
AUTONOMOUS POD RIGHTSIZING
CPU and memory requests that track every model version and traffic shift
No engineering effort.
Model workloads change. New versions ship, traffic patterns shift, batch sizes vary. ScaleOps continuously and autonomously understands and manages resource configurations in real time.
REAL-TIME BURST REACTION
Real-time responses to spikes
Keep latency SLOs intact when traffic surges.
Standard HPA scaling reacts on a fixed scrape interval. By the time it fires, a spike may have already impacted latency. ScaleOps closes that gap, reacting in real time so your serving infrastructure scales with demand, not behind it.
REPLICA OPTIMIZATION
Manages replica count to actual demand, not the conservative guess
No more idle replicas running at full cost.
Teams pad replica counts to avoid latency risk. ScaleOps understands the real replica needs of each model workload and manages counts to match, so you’re not paying for replicas you don’t need while keeping headroom where it matters.
See what ScaleOps recovers from your GPU fleet
Optimization across every layer of your AI/ML infrastructure
Boot-time optimization
Model serving pods need more CPU at startup than at steady state. ScaleOps raises CPU requests during the boot window and reverts automatically once the workload stabilizes.
Node Optimization
ScaleOps packs pods according to each workload’s actual resource needs, reducing the number of nodes running below capacity without touching performance.
Spot Optimization
ScaleOps identifies which workloads can safely run on spot and handles preemptions automatically, so you capture spot savings without building failover logic by hand.
Karpenter Optimization
ScaleOps works alongside Karpenter so provisioning decisions are based on actual workload needs instead of padded requests that lead to oversized nodes.
Smart Pod Placement
ScaleOps manages pod placement to co-locate workloads that benefit from proximity and ensure that more nodes are free to scale down.
Java Optimization
JVM memory behavior doesn’t map cleanly to Kubernetes resource configs. ScaleOps accounts for JVM-specific patterns to set accurate requests and limits for Java-based serving infrastructure.
Workload Troubleshooting
ScaleOps surfaces resource contention, scheduling failures, and OOM events in one view with recommended fixes, so your team spends less time diagnosing.
Cost Monitoring
ScaleOps tracks cloud spend per workload in real time, so you always know where GPU and compute costs are going and where the biggest opportunities are.
Here’s what our users have to say about ScaleOps
“Out of all the vendors we tested, ScaleOps’ rightsizing solution was by far the most advanced. Nothing else we evaluated matched the depth or control.”
“With a two-minute installation and zero effort, ScaleOps has provided Coralogix with a hands-free experience, improving performance while reducing cost. The automation has reduced overhead, allowing our engineering team to concentrate on what truly matters.”
“ScaleOps helps reduce cloud costs significantly without affecting performance”
“ScaleOps eliminates the manual effort of constantly tuning resource requests and limits in our Kubernetes clusters. It automatically adjusts workloads to the right size, helping us reduce over-provisioning and keep our cloud costs low.”
“ScaleOps makes Kubernetes resource management effortless. Its recommendations are accurate, automation is reliable, and the UI provides instant clarity on where optimization is needed. We’ve significantly reduced wasted resources and improved performance stability since adopting it.”
“ScaleOps solved the challenges we were facing with managing and scaling our Kubernetes workloads. The hands-free automation, combined with 75% cost savings, has been a real game changer for At-Bay.”
“ScaleOps’ automation continuously optimizes our containers on production to meet real-time demand. The 2-minute installation was straightforward, and the immediate value was clear. It’s significantly reduced operational overhead, freeing our teams to focus on more strategic initiatives, while reducing our cloud cost by over 50%”
“Manually tuning CPU and memory requests or limits across workloads was eating up our engineers’ time. With ScaleOps automating resource optimization at the pod level, we’ve eliminated constant config changes and cut cloud costs significantly.”
“ScaleOps’ automation optimizes our production apps in real time, cutting cloud costs and eliminating repetitive manual work so our teams can focus on core projects. The quick setup delivered immediate value.”
“ScaleOps allows us to dramatically reduce costs and keep our cloud bill in check.”
“Out of all the vendors we tested, ScaleOps’ rightsizing solution was by far the most advanced. Nothing else we evaluated matched the depth or control.”
“With a two-minute installation and zero effort, ScaleOps has provided Coralogix with a hands-free experience, improving performance while reducing cost. The automation has reduced overhead, allowing our engineering team to concentrate on what truly matters.”
“ScaleOps helps reduce cloud costs significantly without affecting performance”
“ScaleOps eliminates the manual effort of constantly tuning resource requests and limits in our Kubernetes clusters. It automatically adjusts workloads to the right size, helping us reduce over-provisioning and keep our cloud costs low.”
“ScaleOps makes Kubernetes resource management effortless. Its recommendations are accurate, automation is reliable, and the UI provides instant clarity on where optimization is needed. We’ve significantly reduced wasted resources and improved performance stability since adopting it.”
“ScaleOps solved the challenges we were facing with managing and scaling our Kubernetes workloads. The hands-free automation, combined with 75% cost savings, has been a real game changer for At-Bay.”
“ScaleOps’ automation continuously optimizes our containers on production to meet real-time demand. The 2-minute installation was straightforward, and the immediate value was clear. It’s significantly reduced operational overhead, freeing our teams to focus on more strategic initiatives, while reducing our cloud cost by over 50%”
“Manually tuning CPU and memory requests or limits across workloads was eating up our engineers’ time. With ScaleOps automating resource optimization at the pod level, we’ve eliminated constant config changes and cut cloud costs significantly.”
“ScaleOps’ automation optimizes our production apps in real time, cutting cloud costs and eliminating repetitive manual work so our teams can focus on core projects. The quick setup delivered immediate value.”
“ScaleOps allows us to dramatically reduce costs and keep our cloud bill in check.”
“ScaleOps automatically manages our resources and continuously optimizes our production workloads in response to demand. This platform has resulted in significant savings, all through a hands-free experience.”
“Before ScaleOps, managing workload scaling required constant tweaking and monitoring. Now, it’s completely automated. We spend less time managing infrastructure and more time building products. It’s made our Kubernetes environment far more efficient and predictable.”
“We came in looking to save costs, but not at the expense of performance. With ScaleOps’ automated resource optimization, we got both, and saw dramatically high cost savings without compromising on reliability.”
“I appreciate how easy it was to install ScaleOps and how it instantly reduced the cognitive load on our application teams, making it much simpler for them to deploy their workloads effectively. The troubleshooting features are excellent and have proven invaluable when assisting assisting our app teams.”
“ScaleOps automation has improved reliability, helping us maintain consistent application performance even during periods of high demand.”
“ScaleOps drove major cloud cost savings for us. The platform is reliable, easy to deploy, and the support team is exceptional.”
“ScaleOps automatically optimizes Wiz’s containers in production according to our real-time needs, improving performance even during demand spikes. While dramatically reducing our K8s costs, the hands-free automation freed our teams from dealing with ongoing configurations, which is critical in our rapidly ever-growing environment”
“Very easy to get started and implement. Great customer support, we have a dedicated Slack channel where we get responses very quickly. ScaleOps keeps adding more and more useful features.”
“Great optimization tool for EKS, awesome team to work with, and easy deployment.”
“Out of all the vendors we tested, ScaleOps’ rightsizing solution was by far the most advanced. Nothing else we evaluated matched the depth or control.”
“ScaleOps automatically manages our resources and continuously optimizes our production workloads in response to demand. This platform has resulted in significant savings, all through a hands-free experience.”
“Before ScaleOps, managing workload scaling required constant tweaking and monitoring. Now, it’s completely automated. We spend less time managing infrastructure and more time building products. It’s made our Kubernetes environment far more efficient and predictable.”
“We came in looking to save costs, but not at the expense of performance. With ScaleOps’ automated resource optimization, we got both, and saw dramatically high cost savings without compromising on reliability.”
“I appreciate how easy it was to install ScaleOps and how it instantly reduced the cognitive load on our application teams, making it much simpler for them to deploy their workloads effectively. The troubleshooting features are excellent and have proven invaluable when assisting assisting our app teams.”
“ScaleOps automation has improved reliability, helping us maintain consistent application performance even during periods of high demand.”
“ScaleOps drove major cloud cost savings for us. The platform is reliable, easy to deploy, and the support team is exceptional.”
“ScaleOps automatically optimizes Wiz’s containers in production according to our real-time needs, improving performance even during demand spikes. While dramatically reducing our K8s costs, the hands-free automation freed our teams from dealing with ongoing configurations, which is critical in our rapidly ever-growing environment”
“Very easy to get started and implement. Great customer support, we have a dedicated Slack channel where we get responses very quickly. ScaleOps keeps adding more and more useful features.”
“Great optimization tool for EKS, awesome team to work with, and easy deployment.”
“Out of all the vendors we tested, ScaleOps’ rightsizing solution was by far the most advanced. Nothing else we evaluated matched the depth or control.”
Optimized infrastructure. Unchanged models.
No model or code changes, ever
ScaleOps operates entirely at the infrastructure layer. It doesn’t touch serving frameworks, model weights, pipeline code, or container images. Optimization is invisible to model teams.
Works across inference, training & fine-tuning
Each workload type gets the policy built for how it actually runs. Inference gets burst handling. Training gets memory-aware rightsizing. Fine-tuning gets spot safety. One platform, all workload types.
Every workload gets its own policy, automatically
ScaleOps understands the resource behavior of each model workload individually and generates per-workload optimization policies at scale.
Run ScaleOps on any Kubernetes environment
Instant Value with Seamless Automation