OneLens vs. Kubex

OneLens is an all-encompassing cloud financial management and cost optimization platform specifically designed for engineering, finance, FinOps, and platform teams. It consolidates billing and usage data from a variety of cloud services, Kubernetes configurations, GPU resources, AI applications, and other tailor-made cost inputs into a single, coherent interface. This functionality allows teams to analyze their spending from numerous perspectives, including account, service, cluster, namespace, workload, model, team, or business unit; administer cost allocations for showback and chargeback; and efficiently manage budgets, forecasts, and any anomalies that may arise. The platform also identifies potential enhancements for infrastructure and workload efficiency, concentrating on methods such as rightsizing and detecting unused capacity. Through policy-driven governance, alerts, approvals, and efficient workflow automation, it ensures that key findings are communicated to the right stakeholders for prompt corrective actions. Furthermore, OneLens keeps track of AI token and inference usage alongside the related cloud infrastructure costs, which empowers teams to gain a clearer understanding of and allocate expenses associated with AI applications, ultimately refining financial decision-making throughout the organization. By adopting this holistic strategy, OneLens ensures that teams not only monitor their expenditures but also significantly optimize their resource utilization and operational efficiency. This dual focus on tracking and optimization fosters a more intelligent approach to financial management in the cloud environment.

Kubex is an agentic Kubernetes and AI infrastructure optimization platform that automates resource allocation, workload scaling, and cloud cost management across complex computing environments. The software combines deterministic machine learning, predictive workload analysis, and policy-controlled automation to optimize infrastructure without requiring continuous manual intervention. Its resource optimization engine analyzes historical utilization patterns to determine appropriate CPU and memory requests and limits for individual containers. Automated rightsizing helps reduce overprovisioning while accounting for peak demand, memory exhaustion risks, CPU throttling, and application reliability requirements. Kubex also optimizes Kubernetes node selection and cluster capacity by identifying suitable instance types, consolidating workloads, and coordinating infrastructure provisioning through tools such as Karpenter and Cluster Autoscaler. Its bin packing capabilities improve pod placement and node utilization while respecting affinity rules, topology constraints, and disruption budgets. Horizontal Pod Autoscaler optimization coordinates replica counts and utilization targets with container rightsizing to improve application scaling efficiency. For AI and machine learning workloads, Kubex supports GPU fractioning through time-slicing, NVIDIA MIG, and MPS, allowing multiple models to share accelerator resources efficiently. Additional inference optimization capabilities include KV cache sizing, memory utilization analysis, and continuous vLLM configuration tuning to improve throughput and reduce GPU infrastructure costs. The Kubex AI Agent provides natural language infrastructure analysis, scheduled optimization tasks, reusable agent skills, and Model Context Protocol integrations with tools such as Claude, Cursor, and ChatGPT. Its automation engine supports read-only assessments, human approval workflows, configurable policies, in-place resource adjustments, etc.