Allocation API

Throughout our API documentation, we use localhost:9090 as the default Kubecost URL, but your Kubecost instance may be exposed by a service or ingress. To reach Kubecost at port 9090, run: kubectl port-forward deployment/kubecost-cost-analyzer -n kubecost 9090. When querying the cost-model container directly (ex. localhost:9003), the /model part of the URI should be removed.

Allocation API

GET http://<your-kubecost-address>/model/allocation

The Allocation API is the preferred way to query for costs and resources allocated to Kubernetes workloads and optionally aggregated by Kubernetes concepts like namespace, controller, and label.

Path Parameters

Name	Type	Description
window*	string	Duration of time over which to query. Accepts multiple different formats of time (see this Using the window parameter section for more info).
aggregate	string	Field by which to aggregate the results. Accepts: cluster, namespace, controllerKind, controller, service, node, pod, label:<name>, and annotation:<name>. Also accepts comma-separated lists for multi-aggregation, like namespace,label:app.
accumulate	boolean	If true, sum the entire range of time intervals into a single set. Default value is false. Also supports accumulation by specific intervals of time including hour, day, and week. Does not accumulate idle costs, which must be configured separately.
idle	boolean	If true, include idle cost (i.e. the cost of the un-allocated assets) as its own allocation. (See special types of allocation.) Default is true.
external	boolean	If true, include external, or out-of-cluster costs in each allocation. Default is false.
offset	int	Refers to the number of line items you are offsetting. Pairs with limit. See the section on Using offset and limit parameters to parse payload results for more info.
limit	int	Refers to the number of line items per page. Pair with the offset parameter to filter your payload to specific pages of line items. You should also set accumulate=true to obtain a single list of line items, otherwise you will receive a group of line items per interval of time being sampled.
filter	string	Filter your results by any category which you can aggregate by, can support multiple filterable items in the same category in a comma-separated list. For example, to filter results by clusters A and B, use filter=cluster:clusterA,clusterB. See our Filter Parameters doc for a complete explanation of how to use filters and what categories are supported.
format	string	Set to csv to download an accumulated version of the allocation results in CSV format. Set to pdf to download an accumulated version of the allocation results in PDF format. By default, results will be in JSON format.
costMetric	string	Cost metric format. Learn about cost metric calculations in our Allocations Dashboard doc. Supports cumulative, hourly, daily, and monthly. Default is cumulative.
shareIdle	boolean	If true, idle cost is allocated proportionally across all non-idle allocations, per-resource. That is, idle CPU cost is shared with each non-idle allocation's CPU cost, according to the percentage of the total CPU cost represented. Default is false.
splitIdle	boolean	If true, and shareIdle == false, idle allocations are created on a per cluster or per node basis rather than being aggregated into a single "idle" allocation. Default is false.
idleByNode	boolean	If true, idle allocations are created on a per node basis. Which will result in different values when shared and more idle allocations when split. splitIdle should only be configured to true when aggregate is configured to node or cluster. Default is false.
includeSharedCostBreakdown	boolean	Provides breakdown of cost metrics for any shared resources. Default is true.
reconcile	boolean	If true, pulls data from the Assets cache and corrects prices of Allocations according to their related Assets. The corrections from this process are stored in each cost category's cost adjustment field. If the integration with your cloud provider's billing data has been set up, this will result in the most accurate costs for Allocations. Default is true.
shareTenancyCosts	boolean	If true, share the cost of cluster overhead assets such as cluster management costs and node attached volumes across tenants of those resources. Results are added to the sharedCost field. Cluster management and attached volumes are shared by cluster. Default is true.
shareNamespaces	string	Comma-separated list of namespaces to share; e.g. kube-system, kubecost will share the costs of those two namespaces with the remaining non-idle, unshared allocations.
shareLabels	string	Comma-separated list of labels to share; e.g. env:staging, app:test will share the costs of those two label values with the remaining non-idle, unshared allocations.
shareCost	float	Floating-point value representing a monthly cost to share with the remaining non-idle, unshared allocations; e.g. 30.42 ($1.00/day == $30.42/month) for the query yesterday (1 day) will split and distribute exactly $1.00 across the allocations. Default is 0.0.
shareSplit	string	Determines how to split shared costs among non-idle, unshared allocations. By default, the split will be weighted; i.e. proportional to the cost of the allocation, relative to the total. The other option is even; i.e. each allocation gets an equal portion of the shared cost. Default is weighted.

200: OK

{
    "code": 200,
    "data": [
        {
            "aws-dev-1-niko/ip-192-168-12-152.us-east-2.compute.internal/kubecost/kubecost-cost-analyzer-5b84f94b7f-9lxx5/cost-model": {
                "name": "aws-dev-1-niko/ip-192-168-12-152.us-east-2.compute.internal/kubecost/kubecost-cost-analyzer-5b84f94b7f-9lxx5/cost-model",
                "properties": {
                    "cluster": "aws-dev-1-niko",
                    "node": "ip-192-168-12-152.us-east-2.compute.internal",
                    "container": "cost-model",
                    "controller": "kubecost-cost-analyzer",
                    "controllerKind": "deployment",
                    "namespace": "kubecost",
                    "pod": "kubecost-cost-analyzer-5b84f94b7f-9lxx5",
                    "services": [
                        "kubecost-frontend",
                        "kubecost-cost-analyzer"
                    ],
                    "providerID": "i-0f227c52893c7957f",
                    "labels": {
                        "app": "cost-analyzer",
                        "app_kubernetes_io_instance": "kubecost",
                        "app_kubernetes_io_name": "cost-analyzer",
                        "kubernetes_io_metadata_name": "kubecost",
                        "node_kubernetes_io_instance_type": "m5.large",
                        "pod_template_hash": "5b84f94b7f"
                    }
                },
                "window": {
                    "start": "2023-01-27T23:00:00Z",
                    "end": "2023-01-28T00:00:00Z"
                },
                "start": "2023-01-27T23:00:00Z",
                "end": "2023-01-27T23:16:00Z",
                "minutes": 16.000000,
                "cpuCores": 0.050000,
                "cpuCoreRequestAverage": 0.050000,
                "cpuCoreUsageAverage": 0.000964,
                "cpuCoreHours": 0.013333,
                "cpuCost": 0.000426,
                "cpuCostAdjustment": 0.000000,
                "cpuEfficiency": 0.019287,
                "gpuCount": 0.000000,
                "gpuHours": 0.000000,
                "gpuCost": 0.000000,
                "gpuCostAdjustment": 0.000000,
                "networkTransferBytes": 797941.832389,
                "networkReceiveBytes": 1243829.277373,
                "networkCost": 0.000000,
                "networkCrossZoneCost": 0.000000,
                "networkCrossRegionCost": 0.000000,
                "networkInternetCost": 0.000000,
                "networkCostAdjustment": 0.000000,
                "loadBalancerCost": 0.003333,
                "loadBalancerCostAdjustment": 0.000000,
                "pvBytes": 16169288643.764706,
                "pvByteHours": 4311810305.003922,
                "pvCost": 0.000550,
                "pvs": {
                    "cluster=aws-dev-1-niko:name=pvc-16465f04-6464-483e-89af-9e68b4a59e72": {
                        "byteHours": 4311810305.0039215,
                        "cost": 0.0005500940102068225
                    }
                },
                "pvCostAdjustment": 0.000000,
                "ramBytes": 330301440.000000,
                "ramByteRequestAverage": 330301440.000000,
                "ramByteUsageAverage": 202833175.272727,
                "ramByteHours": 88080384.000000,
                "ramCost": 0.000351,
                "ramCostAdjustment": 0.000000,
                "ramEfficiency": 0.614085,
                "sharedCost": 0.000000,
                "externalCost": 0.000000,
                "totalCost": 0.004661,
                "totalEfficiency": 0.288087
            },
            "...etc": {}
        },
        {
            "...etc": {}
        }
    ]
}

Allocation schema

Field	Description
name	Name of each relevant Kubernetes concept described by the allocation, delimited by slashes, e.g. "cluster/node/namespace/pod/container"
properties	Map of name-to-value for all relevant property fields, including: cluster, node, namespace, controller, controllerKind, pod, container, labels, annotation, etc. Note: Prometheus only supports underscores (_) in label names. Dashes (-) and dots (.), while supported by Kubernetes, will be translated to underscores by Prometheus. This may cause the merging of labels, which could result in aggregated costs being charged to a single label.
window	Period of time over which the allocation is defined.
start	Precise starting time of the allocation. By definition must be within the window.
end	Precise ending time of the allocation. By definition must be within the window.
minutes	Number of minutes running; i.e. the minutes from start until end.
cpuCores	Average number of CPU cores allocated while running.
cpuCoreRequestAverage	Average number of CPU cores requested while running.
cpuCoreUsageAverage	Average number of CPU cores used while running.
cpuCoreHours	Cumulative CPU core-hours allocated.
cpuCost	Cumulative cost of allocated CPU core-hours.
cpuCostAdjustment	Change in cost after allocated CPUs have been reconciled with updated node cost
cpuEfficiency	Ratio of cpuCoreUsageAverage-to-cpuCoreRequestAverage, meant to represent the fraction of requested resources that were used.
gpuCount	Number of GPUs allocated to the workload.
gpuHours	Cumulative GPU-hours allocated.
gpuCost	Cumulative cost of allocated GPU-hours.
gpuCostAdjustment	Change in cost after allocated GPUs have been reconciled with updated node cost
networkTransferBytes	Total bytes sent from the workload
networkReceiveBytes	Total bytes received by the workload
networkCost	Cumulative cost of network usage.
networkCrossZoneCost	Cumulative cost of Cross-zone network egress usage.
networkCrossRegionCost	Cumulative cost of Cross-region network egress usage.
networkInternetCost	Cumulative cost of internet egress usage.
networkCostAdjustment	Updated network cost
loadBalancerCost	Cumulative cost of allocated load balancers.
loadBalancerCostAdjustment	Updated load balancer cost.
pvBytes	Average number of bytes of PersistentVolumes allocated while running.
pvByteHours	Cumulative PersistentVolume byte-hours allocated.
pvCost	Cumulative cost of allocated PersistentVolume byte-hours.
pvs	Map of PersistentVolumeClaim costs that have been allocated to the workload
pvCostAdjustment	Updated persistent volume cost.
ramBytes	Average number of RAM bytes allocated. An allocated resource is the source of cost, according to Kubecost - regardless of if a requested resource is used.
ramByteRequestAverage	Average of the RAM requested by the workload. Requests are a Kubernetes tool for preallocating/reserving resources for a given container.
ramByteUsageAverage	Average of the RAM used by the workload. This comes from moment-to-moment measurements of live RAM byte usage of each container. This is roughly the number you see under RAM if you pull up Task Manager (Windows), top on Linux, or Activity Monitor (MacOS).
ramByteHours	Cumulative RAM byte-hours allocated.
ramCost	Cumulative cost of allocated RAM byte-hours.
ramEfficiency	Ratio of ramByteUsageAverage-to-ramByteRequestAverage, meant to represent the fraction of requested resources that were used.
sharedCost	Cumulative cost of shared resources, including shared namespaces, shared labels, shared overhead.
externalCost	Cumulative cost of external resources.
totalCost	Total cumulative cost
totalEfficiency	Cost-weighted average of cpuEfficiency and ramEfficiency. In equation form: ((cpuEfficiency * cpuCost) + (ramEfficiency * ramCost)) / (cpuCost + ramCost)
rawAllocationOnly	Object with fields cpuCoreUsageMax and ramByteUsageMax, which are the maximum usages in the window for the Allocation. If the Allocation query is aggregated or accumulated, this object will be null because the meaning of maximum is ambiguous in these situations.

Quick start

Request allocation data for each 24-hour period in the last three days, aggregated by namespace:

Request

$ curl http://localhost:9090/model/allocation \
  -d window=3d \
  -d aggregate=namespace \
  -d accumulate=false \
  -d shareIdle=false \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "__idle__": { ... },
      "default": { ... },
      "kube-system": { ... },
      "kubecost": { ... }
    },
    {
      "__idle__": { ... },
      "default": { ... },
      "kube-system": { ... },
      "kubecost": { ... }
    },
    {
      "__idle__": { ... },
      "default": { ... },
      "kube-system": { ... },
      "kubecost": { ... }
    },
    {
      "__idle__": { ... },
      "default": { ... },
      "kube-system": { ... },
      "kubecost": { ... }
    }
  ]
}

Querying for window=3d should return a range of four sets because the queried range will overlap with four precomputed 24-hour sets, each aligned to the configured time zone. For example, querying window=3d on 2021/01/04T12:00:00 will return:

• 2021/01/04 00:00:00 until 2021/01/04T12:00:00 (now)

• 2021/01/03 00:00:00 until 2021/01/04 00:00:00

• 2021/01/02 00:00:00 until 2021/01/03 00:00:00

• 2021/01/01 00:00:00 until 2021/01/02 00:00:00

Special types of allocation

• __idle__ refers to resources on a cluster that were not dedicated to a Kubernetes object (e.g. unused CPU core-hours on a node). An idle resource can be shared (proportionally or evenly) with the other allocations from the same cluster. (See the argument shareIdle.)

• __unallocated__ refers to aggregated allocations without the selected aggregate field; e.g. aggregating by label:app might produce an __unallocated__ allocation composed of allocations without the app label.

• __unmounted__ (or "Unmounted PVs") refers to the resources used by PersistentVolumes that aren't mounted to a pod using a PVC, and thus cannot be allocated to a pod.

Query examples

Allocation data for today unaggregated:

Request

$ curl http://localhost:9090/model/allocation \
-d window=today \
-G

Response

{
  "code": 200,
  "data": [
    {
      "__idle__": { ... },
      "cluster-one/gke-niko-pool-2-9182dfa7-okb2/kubecost/kubecost-cost-analyzer-94dc86fc-lwvrm/cost-model": { ... },
      "cluster-one/gke-niko-pool-2-9182dfa7-okb2/kubecost/kubecost-cost-analyzer-94dc86fc-lwvrm/cost-analyzer-frontend": { ... },
      "cluster-one/gke-niko-pool-2-9182dfa7-okb2/kubecost/kubecost-grafana-6df5cc66b6-dzszt/grafana": { ... }
    }
  ]
}

Allocation data for last week, per day, aggregated by cluster:

Request

$ curl http://localhost:9090/model/allocation \
  -d window=lastweek \
  -d aggregate=cluster \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "__idle__": { ... },
      "cluster-one": { ... },
      "cluster-two": { ... }
    }
  ]
}

Allocation data for the last 30 days, aggregated by the "app" label, sharing idle allocation, sharing allocations from two namespaces, sharing $100/mo in overhead, and accumulated into one allocation for the entire window:

Request

$ curl http://localhost:9090/model/allocation \
  -d window=30d \
  -d aggregate=label:app \
  -d accumulate=true \
  -d shareIdle=weighted \
  -d shareNamespaces=kube-system,kubecost \
  -d shareCost=100 \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "__unallocated__": { ... },
      "app=redis": { ... },
      "app=cost-analyzer": { ... },
      "app=prometheus": { ... },
      "app=grafana": { ... },
      "app=nginx": { ... },
      "app=helm": { ... }
    }
  ]
}

Allocation data for 2021-03-10T00:00:00 to 2021-03-11T00:00:00 (i.e. 24h), multi-aggregated by namespace and the "app" label, filtering by properties.cluster == "cluster-one", and accumulated into one allocation for the entire window.

Request

$ curl http://localhost:9090/model/allocation \
  -d window=2021-03-10T00:00:00Z,2021-03-11T00:00:00Z \
  -d aggregate=namespace,label:app \
  -d accumulate=true \
  -d filterClusters=cluster-one \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "default/app=redis": { ... },
      "kubecost/app=cost-analyzer": { ... },
      "kubecost/app=prometheus": { ... },
      "kubecost/app=grafana": { ... },
      "kubecost/app=prometheus": { ... },
      "kube-system/app=helm": { ... }
    }
  ]
}

Allocation data for today, aggregated by annotation. See Enabling Annotation Emission to enable annotations.

Request

$ curl http://localhost:9090/model/allocation \
  -d window=today \
  -d aggregate=annotation:my_annotation \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "__unallocated__": { ... },
      "my_annotation=foo": { ... },
      "my_annotation=bar": { ... }
    }
  ]
}

Querying with /summary endpoint to view condensed payload per line item

/summary is an optional API endpoint which can be added to your Allocation query via .../model/allocation/summary?window=... to provide a condensed list of your cost metrics per line item. Instead of returning the full list of schema values listed above, your query will return something like:

"allocation-line-item": {
                        "name": "allocation-line-tem",
                        "start": "",
                        "end": "",
                        "cpuCoreRequestAverage": ,
                        "cpuCoreUsageAverage": ,
                        "cpuCost": ,
                        "gpuCost": ,
                        "networkCost": ,
                        "loadBalancerCost": ,
                        "pvCost": ,
                        "ramByteRequestAverage": ,
                        "ramByteUsageAverage": ,
                        "ramCost": ,
                        "sharedCost": ,
                        "externalCost": ,
                        "totalEfficiency": ,
                        "totalCost":
                    },

Querying with /topline endpoint to view cost totals across query

/topline is an optional API endpoint which can be added to your /summary Allocation query via .../model/allocation/summary/topline?window=... to provide a condensed overview of your total cost metrics including all line items sampled. You will receive a single list which sums the values per all items queried, formatted similar to a regular /summary query, where numResults displays the total number of items sampled. Idle costs still need to be configured separately.

{
    "code": 200,
    "data": {
        "numResults": ,
        "combined": {
            "allocations": {
                "total": {
                    "name": "total",
                    "start": "",
                    "end": "",
                    "cpuCoreRequestAverage": ,
                    "cpuCoreUsageAverage": ,
                    "cpuCost": ,
                    "gpuCost": ,
                    "networkCost": ,
                    "loadBalancerCost": ,
                    "pvCost": ,
                    "ramByteRequestAverage": ,
                    "ramByteUsageAverage": ,
                    "ramCost": ,
                    "sharedCost": ,
                    "externalCost": 
                }
            },
            "window": {
                "start": "",
                "end": ""
            }
        }
    }
}

Allocation of asset costs

Both the reconcile and shareTenancyCosts flags start processes that distribute the costs of Assets to Allocations related to them. For the reconcile flag, these connections can be straightforward like the connection between a node Asset and an Allocation where the CPU, GPU, and RAM usage can be used to distribute a proportion of the node's cost to the Allocations that run on it. For Assets and Allocations where the connection is less well-defined, such as network Assets we have opted for a method of distributing the cost that we call Distribution by Usage Hours.

Distribution by Usage Hours takes the usage of the windows (start time and end time) of an Asset and all the Allocations connected to it and finds the number of hours that both the Allocation and Asset were running. The number of hours for each Allocation related to an Asset is called Alloc_Usage_Hours. The sum of all Alloc_Usage_Hours for a single Assets is Total_Usage_Hours. With these values, an Assets cost is distributed to each connected Allocation using the formula Asset_Cost * Alloc_Usage_Hours/Total_Usage_Hours. Depending on the Asset type an Allocation can receive proportions of multiple Asset Costs.

Asset types that use this distribution method include:

• Network (reconcile): When the network pod is not enabled cost is distributed by usage hours. If the network pod is enabled cost is distributed to Allocations proportionally to usage.

• Load Balancer (reconcile)

• Cluster Management (shareTenancyCosts)

• Attached disks (shareTenancyCosts): Does not include PVs, which are handled by reconcile

Querying on-demand (experimental)

Querying on-demand with high resolution for long windows can cause serious Prometheus performance issues, including OOM errors. Start with short windows (1d or less) and proceed with caution.

Computing allocation data on-demand allows for greater flexibility with respect to step size and accuracy-versus-performance. (See resolution and error bounds for details.) Unlike the standard endpoint, which can only serve results from precomputed sets with predefined step sizes (e.g. 24h aligned to the UTC time zone), asking for a "7d" query will almost certainly result in 8 sets, including "today" and the final set, which might span 6.5d-7.5d ago. With this endpoint, however, you will be computing everything on-demand, so "7d" will return exactly seven days of data, starting at the moment the query is received. (You can still use window keywords like "today" and "lastweek", of course, which should align perfectly with the same queries of the standard ETL-driven endpoint.)

Additionally, unlike the standard endpoint, querying on-demand will not use reconciled asset costs. Therefore, the results returned will show all adjustments (e.g. CPU, GPU, RAM) to be 0.

Allocation On-Demand API

GET http://<kubecost>/model/allocation/compute

Path Parameters

Name	Type	Description
window*	string	Duration of time over which to query. Accepts words like today, week, month, yesterday, lastweek, lastmonth; durations like 30m, 12h, 7d; RFC3339 date pairs like 2021-01-02T15:04:05Z,2021-02-02T15:04:05Z; Unix timestamps like 1578002645,1580681045.
resolution	string	Duration to use as resolution in Prometheus queries. Smaller values (i.e. higher resolutions) will provide better accuracy, but worse performance (i.e. slower query time, higher memory use). Larger values (i.e. lower resolutions) will perform better, but at the expense of lower accuracy for short-running workloads. See error bounds for details. Default is 1m .
step	string	Duration of a single allocation set. If unspecified, this defaults to the window, so that you receive exactly one set for the entire window. If specified, it works chronologically backward, querying in durations of step until the full window is covered.
aggregate	string	Field by which to aggregate the results. Accepts: cluster, namespace,controllerKind, controller, service, label:<name>, and annotation:<name>. Also accepts comma-separated lists for multi-aggregation, like namespace,label:app.
accumulate	boolean	If true, sum the entire range of sets into a single set. Default value is false.

200: OK

{
                "name": "cluster-one//integration/integration-unmounted-pvcs/__unmounted__",
                "properties": {
                    "cluster": "cluster-one",
                    "container": "__unmounted__",
                    "namespace": "integration",
                    "pod": "integration-unmounted-pvcs"
                },
                "window": {
                    "start": "2023-01-17T15:00:00Z",
                    "end": "2023-01-17T16:00:00Z"
                },
                "start": "2023-01-17T15:00:00Z",
                "end": "2023-01-17T16:00:00Z",
                "minutes": 60.000000,
                "cpuCores": 0.000000,
                "cpuCoreRequestAverage": 0.000000,
                "cpuCoreUsageAverage": 0.000000,
                "cpuCoreHours": 0.000000,
                "cpuCost": 0.000000,
                "cpuCostAdjustment": 0.000000,
                "cpuEfficiency": 0.000000,
                "gpuCount": 0.000000,
                "gpuHours": 0.000000,
                "gpuCost": 0.000000,
                "gpuCostAdjustment": 0.000000,
                "networkTransferBytes": 0.000000,
                "networkReceiveBytes": 0.000000,
                "networkCost": 0.000000,
                "networkCrossZoneCost": 0.000000,
                "networkCrossRegionCost": 0.000000,
                "networkInternetCost": 0.000000,
                "networkCostAdjustment": 0.000000,
                "loadBalancerCost": 0.000000,
                "loadBalancerCostAdjustment": 0.000000,
                "pvBytes": 0.000000,
                "pvByteHours": 0.000000,
                "pvCost": 0.000003,
                "pvs": {
                    "cluster=cluster-one:name=pvc-demo": {
                        "byteHours": 0.000000,
                        "cost": 0.000000
                },
                "pvCostAdjustment": 0.000000,
                "ramBytes": 0.000000,
                "ramByteRequestAverage": 0.000000,
                "ramByteUsageAverage": 0.000000,
                "ramByteHours": 0.000000,
                "ramCost": 0.000000,
                "ramCostAdjustment": 0.000000,
                "ramEfficiency": 0.000000,
                "sharedCost": 0.000000,
                "externalCost": 0.000000,
                "totalCost": 0.000000,
                "totalEfficiency": 0.000000,
                "rawAllocationOnly": null
}

On-demand query examples

Allocation data for the last 60m, in steps of 10m, with resolution 1m, aggregated by cluster.

Request

$ curl http://localhost:9090/model/allocation/compute \
  -d window=60m \
  -d step=10m \
  -d resolution=1m \
  -d aggregate=cluster \
  -d accumulate=false \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "cluster-one": { ... },
      "cluster-two": { ... }
    },
    {
      "cluster-one": { ... },
      "cluster-two": { ... }
    }
  ]
}

Allocation data for the last 9d, in steps of 3d, with a 10m resolution, aggregated by namespace.

Request

$ curl http://localhost:9090/model/allocation/compute \
  -d window=9d \
  -d step=3d \
  -d resolution=10m
  -d aggregate=namespace \
  -d accumulate=false \
  -G

Response

{
  "code": 200,
  "data": [
    {
      "default": { ... },
      "kubecost": { ... },
      "kube-system": { ... }
    },
    {
      "default": { ... },
      "kubecost": { ... },
      "kube-system": { ... }
    },
    {
      "default": { ... },
      "kubecost": { ... },
      "kube-system": { ... }
    }
  ]
}

Theoretical error bounds

Tuning the resolution parameter allows the querier to make tradeoffs between accuracy and performance. For long-running pods (>1d) resolution can be tuned aggressively low (>10m) with relatively little effect on accuracy. However, even modestly low resolutions (5m) can result in significant accuracy degradation for short-running pods (<1h).

Here, we provide theoretical error bounds for different resolution values given pods of differing running durations. The tuple represents lower- and upper-bounds for accuracy as a percentage of the actual value. For example:

• 1.00, 1.00 means that results should always be accurate to less than 0.5% error

• 0.83, 1.00 means that results should never be high by more than 0.5% error, but could be low by as much as 17% error

• -1.00, 10.00 means that the result could be as high as 1000% error (e.g. 30s pod being counted for 5m) or the pod could be missed altogether, i.e. -100% error.

resolution	30s pod	5m pod	1h pod	1d pod	7d pod
1m	-1.00, 2.00	0.80, 1.00	0.98, 1.00	1.00, 1.00	1.00, 1.00
2m	-1.00, 4.00	0.80, 1.20	0.97, 1.00	1.00, 1.00	1.00, 1.00
5m	-1.00, 10.00	-1.00, 1.00	0.92, 1.00	1.00, 1.00	1.00, 1.00
10m	-1.00, 20.00	-1.00, 2.00	0.83, 1.00	0.99, 1.00	1.00, 1.00
30m	-1.00, 60.00	-1.00, 6.00	0.50, 1.00	0.98, 1.00	1.00, 1.00
60m	-1.00, 120.00	-1.00, 12.00	-1.00, 1.00	0.96, 1.00	0.99, 1.00

Troubleshooting

Incomplete cost data for short window queries when using Thanos

While using Thanos, data can delayed from 1 to 3 hours, which may result in allocation queries retrieving inaccurate or incomplete data when using short window intervals. Avoid using values for window smaller than 5h as a best practice.