Scaleops
Autonomous resource management for Kubernetes and GPU workloads
About Scaleops
ScaleOps automates allocation and scaling of CPU, GPU and memory for Kubernetes workloads and AI inference without manual tuning, to reduce cost and improve reliability.
Common use cases
- Cut Kubernetes cost
- Optimize GPU use
- Automate resource tuning
Products and services
Core Platform
Pod, node and replica optimization
GPU Platform
Fractional GPUs and inference optimization
ScaleOps AI
AI SRE agent and MCP interface
Key features
- Pod rightsizing
- GPU optimization
- Spot optimization
- Cost monitoring
- Karpenter optimization
- Air-gapped option
Integrations
- Kubernetes
- Karpenter
- AWS
- Google Cloud
- Microsoft Azure
Scaleops alternatives
Cast AIKubernetes automation and optimization platform
- PEPerfectScale
- KUKubecost
- STStormForge
FAQs
What does it optimize?
Kubernetes and GPU resource use.
Deployment options?
Self-hosted, cloud and air-gapped.
Does it need code changes?
The company says no.
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