In many platforms, users only see the cost at the end of the billing cycle. They run multiple experiments and only later receive an invoice. Often, the total cost is much higher than expected, especially when experiments are scaled, repeated, or run with large datasets.
Yeedu solves this problem with a Cost-Aware UI that provides cost visibility during and after each run, using YCU (Yeedu Compute Unit) as the unit of measurement. This ensures that users can manage spending effectively and avoid surprises through real-time cloud cost tracking embedded directly into the experimentation workflow.
When a user creates a job or notebook inside a workspace and runs an experiment, Yeedu surfaces cost information at multiple stages:
This approach helps users understand the financial impact of each experiment immediately, rather than relying on delayed monthly summaries. Cost becomes part of the workflow not an afterthought enabling compute cost tracking during job execution rather than post-billing analysis.
The Workspace -> Runs section shows the YCU for each run.
Workspace → Runs
The Runs table includes a column labeled Approx YCU, which shows the estimated compute cost for each run. This gives users immediate visibility into how much each experiment costs, making it easy to compare runs and spot expensive or inefficient configurations within a cost-aware UI for cloud platforms.
By showing cost directly alongside experiment results, Yeedu enables users to evaluate runs based on both performance and cost, encouraging smarter and more efficient experimentation and supporting ongoing cloud cost optimization initiatives.
In addition to run-level insights, Yeedu also provides cost visibility through the Clusters screen.
On the Clusters screen, users can see the Approx YCU for each cluster, indicating how much compute cost is being consumed by individual clusters. This helps users understand how YCU usage is distributed across their infrastructure, not just across experiments, strengthening overall cloud cost optimization at the infrastructure layer.
Cluster-level cost visibility allows users to:
By combining run-level and cluster-level cost insights, Yeedu gives users a complete and transparent view of where their compute budget is going, helping them prevent cloud billing surprises before they occur.
While a job is running, Yeedu tracks YCU usage in real time. Users can monitor cost growth as the run progresses and decide whether to continue, optimize, or stop the job if it becomes too expensive.
This is especially valuable for long-running or large-scale experiments, where costs can escalate quickly. Real-time visibility prevents runaway spending and gives users direct control over compute usage, allowing teams to prevent cloud billing surprises through proactive cost intervention.
Each run displays its Approx YCU, making it easy to compare the cost of different experiments. This is particularly useful for teams testing multiple models, configurations, or datasets.
With run-level cost comparison, users can quickly identify:
These insights help teams plan future experiments more effectively and reduce overall YCU consumption as part of a broader cloud cost optimization strategy.
When cost is visible throughout the workflow, users no longer have to wait until the end of the month to understand their spending. Continuous cost tracking eliminates surprises and makes billing predictable and explainable by integrating real-time cloud cost tracking directly into execution workflows.
This is especially critical for teams running many experiments in parallel, where costs can accumulate rapidly without clear visibility.
Cost transparency naturally encourages optimization. When users can see the YCU impact of every run and cluster, they are more likely to:
The result is lower spend, better resource planning, and higher productivity driven by a truly cost-aware UI for cloud platforms.
Yeedu’s Cost-Aware UI is built on a few core principles:
These principles ensure that cost information is not just available, but genuinely useful.
Yeedu’s Cost-Aware UI brings true cost transparency into the experimentation workflow using YCU. With Approx YCU visible at both the run level (Workspace → Runs) and the infrastructure level (Clusters screen), users can track, compare, and control compute costs at every stage through compute cost tracking during job execution.
This eliminates billing surprises, builds trust, and empowers users to make better decisions about how they use compute resources while continuously improving cloud cost optimization outcomes.
In a world where compute costs can rise quickly, Yeedu ensures users stay informed and in control every step of the way.
