The Savings Report answers one question: how much money has Cast AI saved you, and how? For every cluster, and for your organization as a whole over time, it compares what you're actually paying today against a modeled estimate of what you'd be paying without Cast AI.

Two views

The report is available at two levels, each reflecting the same underlying calculations grouped differently:

• Organization-level savings report — shows savings trends across your entire organization or a filtered set of clusters, with a per-cluster breakdown table.
• Cluster-level savings report — drills into a single cluster, showing capacity and cost metrics split by node autoscaler and workload autoscaler sections.

Key concepts

A few terms in this report have specific meanings. Understanding them makes the rest of the report much easier to read.

Provisioned is the amount of a resource — CPU cores or RAM — that's actually allocated on your nodes. This is the capacity you're paying for.

Requested is the amount of a resource your workloads ask for, via Kubernetes pod resource requests, after Cast AI's workload autoscaler has rightsized them. Original requested is what workloads asked for before rightsizing — the difference between the two is the effect of rightsizing.

Lifecycle refers to how a node is purchased: on-demand, spot, or fallback (a substitute instance type used when a preferred one isn't available). The report combines all three into single totals for provisioned resources and cost — it doesn't break savings down by lifecycle.

Realized savings are savings realized primarily through node autoscaler: the difference between projected and actual cost, driven by bin-packing workloads more efficiently, adopting spot instances, and choosing cheaper node types. In case when both, node autoscaler and workload autoscaler are enabled the impact of workload autscaler is included in the realized savings.

Workload autoscaler savings come from rightsizing — reducing workloads' CPU and RAM requests to match what they actually use, so fewer nodes are needed to serve the same workloads. These are modeled estimates. If node autoscaler is enabled, they are already included in realized savings, otherwise they are potential savings not reflected in actual spend.

Baseline is the reference point Cast AI uses to model what your cluster would have cost without its optimizations. It's covered in detail in Savings baseline.

The two sources of savings

Cast AI saves you money in two structurally different ways, and the report attributes each separately so you can see which is doing the work.

Realized savings (node-level)

The cluster autoscaler reduces the cost of the capacity you provision: it packs pods onto fewer nodes, adopts spot instances where appropriate, and picks cheaper node types for the same workloads. The report calculates this as:

realized savings = projected cost − actual cost

where projected cost reconstructs what you'd be paying if you were still provisioning at your old overprovisioning ratio, at your old unit price.

Workload autoscaler savings (rightsizing)

The workload autoscaler reduces demand itself: it lowers CPU and RAM requests that were set too high to begin with, so workloads ask for closer to what they actually use. Smaller requests mean fewer nodes are needed. The report calculates this as:

workload autoscaler savings = (original requests − current requests) × unit cost

counted only on days when workload autoscaling (VPA) was actually active on the cluster.

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Note

These two numbers are not redundant. Autoscaler savings come from satisfying demand more efficiently. Workload autoscaler savings come from reducing demand itself. A cluster can show strong savings from one and little from the other, depending on how it's configured.

When savings start appearing

A cluster needs a baseline before it can show any savings at all, and savings only start accruing once the baseline period ends. If you're looking at a very new cluster, or a time range that falls entirely inside a cluster's baseline period, you may see projected cost equal actual cost and zero autoscaler savings — that's an expected behavior. See Savings baseline to the exact conditions.

Related resources

• Organization-level savings report
• Cluster-level savings report
• Savings baseline — how Cast AI establishes the "before Cast AI" reference point your savings are measured against.
• Savings report calculations — the exact formulas behind every number in the report, plus known edge cases and limitations.