Cost Observability for AI: Transparency That Lowers Costs
Building scalable machine learning projects is challenging enough without the added burden of hidden or unpredictable infrastructure costs. This lack of cost observability can often lead to inefficiencies, overspending, and hurdles in scaling effectively.
Union’s AI workflow and inference platform tackles the challenge of cost management by unifying data, models, and compute with execution workflows. The Cost Allocation Dashboard provides a single pane of glass into AI workflow and infrastructure expenses. Expanding on Union’s task-level resource management, the dashboard empowers teams to make smarter, data-driven budget decisions.
Increasingly, management needs to understand the cost investment of AI. This manual guesswork usually falls to the engineer, taking valuable time away from product work.
Without transparency, you’re leaving money on the table
In machine learning—especially deep learning—successful experimentation and deployment is tied to efficient resource allocation, but tracking costs is a difficult, manual process.
Without proper cost observability, organizations face significant challenges:
- Budget Spikes: Scaling compute resources often leads to unexpected invoices.
- Inefficient Resource Utilization: Over-provisioning or underutilizing nodes can waste money and delay workflows.
- Lack of Accountability: Teams may struggle to attribute costs to specific projects or tasks, making it hard to justify investments or cut inefficiencies.
Savvy engineering leaders understand how expensive this can be – not just for their budgets, but also for team productivity.
Union’s Cost Allocation Dashboard
Our Cost Allocation Dashboard is designed to provide complete observability into your AI infrastructure expenses.
1. Workload Costs
Get a cost overview for individual workflows, broken down by projects/teams, workflows, and individual execution IDs. Discover the most expensive steps in your pipelines and prioritize optimizations where they matter most.
2. Compute Costs
Drill down into node-level resource utilization with metrics such as uptime, CPU, GPU, and memory usage. This reveals where inefficiencies lie so you can optimize workloads to save money without compromising performance. For example, if a training job consistently uses less than 50% of the allocated GPU, you can adjust configurations to save on compute costs.
How to use Union’s Cost Allocation Dashboard
Getting started is as simple as clicking the “Cost” button in the upper-right menu of your Union interface. From there, you’ll access detailed dashboards that break down costs by project, task, and execution.
- Integrated, Effortless Tracking
Union’s AI workflow layer automatically tracks the cost of all executions, providing native, real-time insights without additional tools or fees. - Complete Cost Transparency
Gain complete visibility into where and how budgets are being spent, from project-level costs to individual task executions. - Optimized AI Workflow Efficiency
Identify and reduce inefficiencies, such as over-provisioned AI workflows or underutilized resources, to streamline operations and minimize waste. - Enhanced Accountability
Attribute expenses to specific teams, projects, or workflows, enabling better budget tracking, forecasting, and alignment with organizational goals. - Smart Cost Optimization
Leverage insights like spot instance usage and node utilization to improve ROI and right-size AI architecture for recurring workflows, reducing unnecessary compute costs.
With these benefits, Union empowers teams to manage costs effectively, scale confidently, and maximize the return on their AI investments.
Unlock cost observability for your AI workflows
Ready to bring clarity to your AI infrastructure costs? With Union, managing AI at scale is simpler, smarter, and more cost-effective than ever before.
Book a demo to learn more.
