diff --git a/deployer/README.md b/deployer/README.md
index c6f5135e32..49548e0cb2 100644
--- a/deployer/README.md
+++ b/deployer/README.md
@@ -88,7 +88,11 @@ The `deployer.py` file is the main file, that contains all of the commands regis
 │   │   |   ├── decision.py
 │   │   |   └── jobs.py
 |   |   └── resource_allocation
+│   │       ├── daemonset_requests.py
+│   │       ├── daemonset_requests.yaml
 │   │       ├── generate_choices.py
+│   │       ├── instance_capacities.py
+│   │       ├── instance_capacities.yaml
 │   │       ├── node-capacity-info.json
 │   │       ├── resource_allocation_app.py
 │   │       └── update_nodeinfo.py
@@ -264,6 +268,22 @@ This sub-command can be used to generate the resource allocation choices for giv
 ##### `generate resource-allocation choices`
 This generates a custom number of resource allocation choices for a certain instance type, depending on a certain chosen strategy that can be used in the profile list of a hub.
 
+##### `generate resource-allocation daemonset-requests`
+Updates `daemonset_requests.yaml` with an individual cluster's DaemonSets' requests summarized.
+
+Only DaemonSet's with running pods are considered, and GPU related DaemonSets (with "nvidia" in the name) are also ignored.
+
+To run this command for all clusters, `xargs` can be used like this:
+
+    ls config/clusters | xargs -I {} deployer generate resource-allocation daemonset-requests {}
+
+##### `generate resource-allocation instance-capacities`
+Updates `instance_capacities.yaml` with an individual cluster's running instance types' total and allocatable capacity.
+
+To run this command for all clusters, `xargs` can be used like this:
+
+    ls config/clusters | xargs -I {} deployer generate resource-allocation instance-capacities {}
+
 ##### `generate resource-allocation node-info-update`
 This updates the json file `node-capacity-info.json` with info about the capacity of a node of a certain type. This file is then used for generating the resource choices.
 
diff --git a/deployer/__main__.py b/deployer/__main__.py
index 2b638f7111..782f047f34 100644
--- a/deployer/__main__.py
+++ b/deployer/__main__.py
@@ -9,7 +9,9 @@
 import deployer.commands.generate.dedicated_cluster.aws  # noqa: F401
 import deployer.commands.generate.dedicated_cluster.gcp  # noqa: F401
 import deployer.commands.generate.helm_upgrade.jobs  # noqa: F401
+import deployer.commands.generate.resource_allocation.daemonset_requests  # noqa: F401
 import deployer.commands.generate.resource_allocation.generate_choices  # noqa: F401
+import deployer.commands.generate.resource_allocation.instance_capacities  # noqa: F401
 import deployer.commands.generate.resource_allocation.update_nodeinfo  # noqa: F401
 import deployer.commands.grafana.central_grafana  # noqa: F401
 import deployer.commands.grafana.deploy_dashboards  # noqa: F401
diff --git a/deployer/commands/generate/resource_allocation/daemonset_requests.py b/deployer/commands/generate/resource_allocation/daemonset_requests.py
new file mode 100644
index 0000000000..e47ac51ed6
--- /dev/null
+++ b/deployer/commands/generate/resource_allocation/daemonset_requests.py
@@ -0,0 +1,157 @@
+import json
+import math
+import subprocess
+from pathlib import Path
+
+import typer
+from kubernetes.utils.quantity import parse_quantity
+from ruamel.yaml import YAML
+
+from deployer.infra_components.cluster import Cluster
+from deployer.utils.file_acquisition import find_absolute_path_to_cluster_file
+
+from .resource_allocation_app import resource_allocation_app
+
+HERE = Path(__file__).parent
+yaml = YAML()
+yaml.preserve_quotes = True
+yaml.indent(mapping=2, sequence=4, offset=2)
+
+
+def get_k8s_distribution():
+    """
+    Returns a 2-tuple with the guessed the k8s distribution based on the k8s
+    api-server's reported version, either Google's GKE, Amazon's EKS, or Azure's
+    AKS, and the server's reported gitVersion.
+    """
+    output = subprocess.check_output(
+        [
+            "kubectl",
+            "version",
+            "--output=json",
+        ],
+        text=True,
+    )
+    version_info = json.loads(output)
+    server_version_info = version_info["serverVersion"]["gitVersion"]
+    if "gke" in server_version_info:
+        return "gke", server_version_info
+    if "eks" in server_version_info:
+        return "eks", server_version_info
+    return "aks", server_version_info
+
+
+def get_daemon_sets_requests():
+    """
+    Returns a list of dicts with info about DaemonSets with pods desired to be scheduled on
+    some nodes the k8s cluster.
+    """
+    output = subprocess.check_output(
+        [
+            "kubectl",
+            "get",
+            "ds",
+            "--all-namespaces",
+            "--output=jsonpath-as-json={.items[*]}",
+        ],
+        text=True,
+    )
+    daemon_sets = json.loads(output)
+
+    # filter out DaemonSets that aren't desired on any node
+    daemon_sets = [ds for ds in daemon_sets if ds["status"]["desiredNumberScheduled"]]
+
+    info = []
+    for ds in daemon_sets:
+        name = ds["metadata"]["name"]
+        req_mem = req_cpu = lim_mem = lim_cpu = 0
+        for c in ds["spec"]["template"]["spec"]["containers"]:
+            resources = c.get("resources", {})
+            requests = resources.get("requests", {})
+            limits = resources.get("limits", {})
+            req_mem += parse_quantity(requests.get("memory", 0))
+            lim_mem += parse_quantity(limits.get("memory", 0))
+            req_cpu += parse_quantity(requests.get("cpu", 0))
+            lim_cpu += parse_quantity(limits.get("cpu", 0))
+
+        info.append(
+            {
+                "name": name,
+                "cpu_request": float(req_cpu),
+                "cpu_limit": float(lim_cpu),
+                "memory_request": int(req_mem),
+                "memory_limit": int(lim_mem),
+            }
+        )
+
+    return info
+
+
+def get_daemon_sets_requests_summary():
+    """
+    Returns a summary of the requests from `get_daemon_sets_requests`.
+    """
+    daemon_sets = get_daemon_sets_requests()
+    # filter out DaemonSets related to nvidia GPUs
+    daemon_sets = [ds for ds in daemon_sets if "nvidia" not in ds["name"]]
+    # separate DaemonSets without requests, as only requests are what impacts
+    # scheduling of pods and reduces a node's remaining allocatable resources
+    req_daemon_sets = [
+        ds for ds in daemon_sets if ds["cpu_request"] or ds["memory_request"]
+    ]
+    other_daemon_sets = [
+        ds for ds in daemon_sets if not ds["cpu_request"] and not ds["memory_request"]
+    ]
+
+    cpu_requests = sum([ds["cpu_request"] for ds in req_daemon_sets])
+    memory_requests = sum([ds["memory_request"] for ds in req_daemon_sets])
+    info = {
+        "requesting_daemon_sets": ",".join(
+            sorted([ds["name"] for ds in req_daemon_sets])
+        ),
+        "other_daemon_sets": ",".join(sorted([ds["name"] for ds in other_daemon_sets])),
+        "cpu_requests": str(math.ceil(cpu_requests * 1000)) + "m",
+        "memory_requests": str(math.ceil(memory_requests / 1024**2)) + "Mi",
+    }
+    return info
+
+
+@resource_allocation_app.command()
+def daemonset_requests(
+    cluster_name: str = typer.Argument(..., help="Name of cluster to operate on"),
+):
+    """
+    Updates `daemonset_requests.yaml` with an individual cluster's DaemonSets'
+    requests summarized.
+
+    Only DaemonSet's with running pods are considered, and GPU related
+    DaemonSets (with "nvidia" in the name) are also ignored.
+
+    To run this command for all clusters, `xargs` can be used like this:
+
+        ls config/clusters | xargs -I {} deployer generate resource-allocation daemonset-requests {}
+    """
+    file_path = HERE / "daemonset_requests.yaml"
+    file_path.touch(exist_ok=True)
+
+    # acquire a Cluster object
+    config_file_path = find_absolute_path_to_cluster_file(cluster_name)
+    with open(config_file_path) as f:
+        cluster = Cluster(yaml.load(f), config_file_path.parent)
+
+    # auth and inspect cluster
+    with cluster.auth():
+        k8s_dist, k8s_version = get_k8s_distribution()
+        ds_requests = get_daemon_sets_requests_summary()
+
+    # read
+    with open(file_path) as f:
+        info = yaml.load(f) or {}
+
+    # update
+    ds_requests["k8s_version"] = k8s_version
+    info.setdefault(k8s_dist, {})[cluster_name] = ds_requests
+
+    # write
+    with open(file_path, "w") as f:
+        yaml.dump(info, f)
diff --git a/deployer/commands/generate/resource_allocation/daemonset_requests.yaml b/deployer/commands/generate/resource_allocation/daemonset_requests.yaml
new file mode 100644
index 0000000000..fd45f511c7
--- /dev/null
+++ b/deployer/commands/generate/resource_allocation/daemonset_requests.yaml
@@ -0,0 +1,180 @@
+# This file contains generated information about cpu/memory requests made by
+# DaemonSets with running pods in our clusters. This information is relevant
+# when planning cpu/memory requests for other pods as the daemonsets requests
+# reduces the available allocatable capacity.
+#
+# The requests vary between cloud providers, clusters, and k8s versions for
+# reasons like:
+#
+# - Cloud providers' managed k8s provides different DaemonSets by default
+# - DaemonSets may be coupled to managed k8s features (calico-node)
+# - DaemonSets' requests may be coupled to managed k8s version (netd)
+# - DaemonSets may have a vertical autoscaler changing requests dynamically over
+#   time if needed (calico-node-vertical-autoscaler)
+# - We may deploy or change a DaemonSet's requests over time (support-cryptnono,
+#   support-prometheus-node-exporter)
+#
+# This file isn't updated by automation, but can easily be updated by manually
+# running a command once for each cluster:
+#
+#     ls config/clusters | xargs -I {} deployer generate resource-allocation daemonset-requests {}
+#
+gke:
+  2i2c:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: binder-staging-dind,binder-staging-image-cleaner,continuous-image-puller,imagebuilding-demo-binderhub-service-docker-api,netd
+    cpu_requests: 342m
+    memory_requests: 566Mi
+    k8s_version: v1.26.5-gke.2100
+  2i2c-uk:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 344m
+    memory_requests: 596Mi
+    k8s_version: v1.27.4-gke.900
+  awi-ciroh:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: netd
+    cpu_requests: 342m
+    memory_requests: 566Mi
+    k8s_version: v1.25.10-gke.2700
+  callysto:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 344m
+    memory_requests: 596Mi
+    k8s_version: v1.27.4-gke.900
+  catalystproject-latam:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,ip-masq-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 338m
+    memory_requests: 496Mi
+    k8s_version: v1.27.3-gke.100
+  cloudbank:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: continuous-image-puller,continuous-image-puller,continuous-image-puller,netd
+    cpu_requests: 342m
+    memory_requests: 566Mi
+    k8s_version: v1.26.5-gke.2100
+  hhmi:
+    requesting_daemon_sets: fluentbit-gke,gke-metadata-server,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 228m
+    memory_requests: 480Mi
+    k8s_version: v1.27.3-gke.100
+  leap:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: netd
+    cpu_requests: 342m
+    memory_requests: 566Mi
+    k8s_version: v1.25.10-gke.2700
+  linked-earth:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 344m
+    memory_requests: 596Mi
+    k8s_version: v1.27.4-gke.900
+  m2lines:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 344m
+    memory_requests: 596Mi
+    k8s_version: v1.27.4-gke.900
+  meom-ige:
+    requesting_daemon_sets: fluentbit-gke,gke-metadata-server,gke-metrics-agent,netd,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 234m
+    memory_requests: 580Mi
+    k8s_version: v1.27.4-gke.900
+  pangeo-hubs:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,gke-metrics-agent,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: netd
+    cpu_requests: 342m
+    memory_requests: 566Mi
+    k8s_version: v1.26.5-gke.2100
+  qcl:
+    requesting_daemon_sets: calico-node,fluentbit-gke,gke-metadata-server,ip-masq-agent,pdcsi-node,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: continuous-image-puller,continuous-image-puller,netd
+    cpu_requests: 336m
+    memory_requests: 466Mi
+    k8s_version: v1.25.10-gke.2700
+eks:
+  2i2c-aws-us:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  carbonplan:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.24.16-eks-2d98532
+  catalystproject-africa:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.27.4-eks-2d98532
+  gridsst:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  jupyter-meets-the-earth:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  nasa-cryo:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  nasa-ghg:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.27.4-eks-2d98532
+  nasa-veda:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  openscapes:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.24.16-eks-2d98532
+  smithsonian:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+  ubc-eoas:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.24.17-eks-f8587cb
+  victor:
+    requesting_daemon_sets: aws-node,ebs-csi-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: ""
+    cpu_requests: 170m
+    memory_requests: 250Mi
+    k8s_version: v1.25.12-eks-2d98532
+aks:
+  utoronto:
+    requesting_daemon_sets: cloud-node-manager,csi-azuredisk-node,csi-azurefile-node,kube-proxy,support-cryptnono,support-prometheus-node-exporter
+    other_daemon_sets: calico-node,continuous-image-puller,continuous-image-puller,continuous-image-puller,continuous-image-puller
+    cpu_requests: 226m
+    memory_requests: 300Mi
+    k8s_version: v1.26.3
diff --git a/deployer/commands/generate/resource_allocation/instance_capacities.py b/deployer/commands/generate/resource_allocation/instance_capacities.py
new file mode 100644
index 0000000000..5dc0df9947
--- /dev/null
+++ b/deployer/commands/generate/resource_allocation/instance_capacities.py
@@ -0,0 +1,144 @@
+import json
+import math
+import subprocess
+from pathlib import Path
+
+import typer
+from kubernetes.utils.quantity import parse_quantity
+from ruamel.yaml import YAML
+
+from deployer.infra_components.cluster import Cluster
+from deployer.utils.file_acquisition import find_absolute_path_to_cluster_file
+
+from .resource_allocation_app import resource_allocation_app
+
+HERE = Path(__file__).parent
+yaml = YAML()
+yaml.preserve_quotes = True
+yaml.indent(mapping=2, sequence=4, offset=2)
+
+
+def get_running_instance_types():
+    """
+    Returns a unique list of the k8s cluster's running nodes' instance types.
+    """
+    output = subprocess.check_output(
+        [
+            "kubectl",
+            "get",
+            "node",
+            r"--output=jsonpath-as-json={.items[*].metadata.labels['node\.kubernetes\.io/instance-type']}",
+        ],
+        text=True,
+    )
+    instance_types = list(set(json.loads(output)))
+    return instance_types
+
+
+def get_instance_capacity(instance_type: str):
+    """
+    Returns a dictionary summarizing total and allocatable capacity of
+    cpu/memory for an instance type by inspecting one in the k8s cluster.
+    """
+    output = subprocess.check_output(
+        [
+            "kubectl",
+            "get",
+            "node",
+            "--output=jsonpath-as-json={.items[*].status}",
+            f"--selector=node.kubernetes.io/instance-type={instance_type}",
+        ],
+        text=True,
+    )
+
+    # all nodes of a given instance type should report the same capacity and
+    # allocatable cpu/memory, we just pick one
+    status = json.loads(output)[0]
+
+    cpu_capacity = float(parse_quantity(status["capacity"]["cpu"]))
+    cpu_allocatable = float(parse_quantity(status["allocatable"]["cpu"]))
+    mem_capacity = int(parse_quantity(status["capacity"]["memory"]))
+    mem_allocatable = int(parse_quantity(status["allocatable"]["memory"]))
+
+    # format memory to use Gi with 3 decimal places
+    mem_capacity = str(math.floor(mem_capacity / 1024**3 * 1000) / 1000) + "Gi"
+    mem_allocatable = str(math.floor(mem_allocatable / 1024**3 * 1000) / 1000) + "Gi"
+
+    info = {
+        "cpu_capacity_low": cpu_capacity,
+        "cpu_capacity_high": cpu_capacity,
+        "cpu_allocatable_low": cpu_allocatable,
+        "cpu_allocatable_high": cpu_allocatable,
+        "mem_capacity_low": mem_capacity,
+        "mem_capacity_high": mem_capacity,
+        "mem_allocatable_low": mem_allocatable,
+        "mem_allocatable_high": mem_allocatable,
+    }
+    return info
+
+
+def get_instance_capacities():
+    """
+    Returns a dictionary with entries for each of the k8s cluster's running
+    instance types.
+    """
+    instance_types = get_running_instance_types()
+
+    info = {}
+    for it in instance_types:
+        info[it] = get_instance_capacity(it)
+    return info
+
+
+@resource_allocation_app.command()
+def instance_capacities(
+    cluster_name: str = typer.Argument(..., help="Name of cluster to operate on"),
+):
+    """
+    Updates `instance_capacities.yaml` with an individual cluster's running
+    instance types' total and allocatable capacity.
+
+    To run this command for all clusters, `xargs` can be used like this:
+
+        ls config/clusters | xargs -I {} deployer generate resource-allocation instance-capacities {}
+    """
+    file_path = HERE / "instance_capacities.yaml"
+    file_path.touch(exist_ok=True)
+
+    # acquire a Cluster object
+    config_file_path = find_absolute_path_to_cluster_file(cluster_name)
+    with open(config_file_path) as f:
+        cluster = Cluster(yaml.load(f), config_file_path.parent)
+
+    # auth and inspect cluster
+    with cluster.auth():
+        new_ics = get_instance_capacities()
+
+    # read
+    with open(file_path) as f:
+        ics = yaml.load(f) or {}
+
+    # update
+    for type, new_cap in new_ics.items():
+        cap = ics.get(type)
+
+        # add new entry
+        if not cap:
+            ics[type] = new_cap
+            continue
+
+        # update existing entry, comparing and updating the lowest low and
+        # highest high for the kind of resources
+        props = ["cpu_capacity", "cpu_allocatable", "mem_capacity", "mem_allocatable"]
+        for p in props:
+            lp = f"{p}_low"
+            if new_cap[lp] < cap[lp]:
+                cap[lp] = new_cap[lp]
+        for p in props:
+            lp = f"{p}_high"
+            if new_cap[lp] > cap[lp]:
+                cap[lp] = new_cap[lp]
+
+    # write
+    with open(file_path, "w") as f:
+        yaml.dump(ics, f)
diff --git a/deployer/commands/generate/resource_allocation/instance_capacities.yaml b/deployer/commands/generate/resource_allocation/instance_capacities.yaml
new file mode 100644
index 0000000000..ebe5eef58e
--- /dev/null
+++ b/deployer/commands/generate/resource_allocation/instance_capacities.yaml
@@ -0,0 +1,184 @@
+# Contains instances' total and allocatable capacity reported from our k8s
+# clusters via "kubectl get node".
+#
+# An instance type's reported total capacity and allocatable capacity has been
+# found to vary slightly, perhaps due to changes in the node pool's k8s
+# versions. To keep an eye on that situation, we keep track of the lowest low
+# and highest high.
+#
+# Note that n2-highmem-*, r5.*large, and Standard_E*s_v5 the same kind of
+# instances, all providing a ratio of 1:8 for CPU:memory and the 3:rd generation
+# Intel Xeon (Icy Lake) CPU.
+#
+# This file isn't updated by automation, but can easily be updated by manually
+# by running a command once for each cluster:
+#
+#     ls config/clusters | xargs -I {} deployer generate resource-allocation instance-capacities {}
+#
+# GKE instance types
+n2-highmem-2:
+  cpu_capacity_low: 2.0
+  cpu_capacity_high: 2.0
+  cpu_allocatable_low: 1.93
+  cpu_allocatable_high: 1.93
+  mem_capacity_low: 15.631Gi
+  mem_capacity_high: 15.634Gi
+  mem_allocatable_low: 12.972Gi
+  mem_allocatable_high: 12.975Gi
+n2-highmem-4:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.92
+  cpu_allocatable_high: 3.92
+  mem_capacity_low: 31.357Gi
+  mem_capacity_high: 31.357Gi
+  mem_allocatable_low: 27.738Gi
+  mem_allocatable_high: 27.738Gi
+n2-highmem-8:
+  cpu_capacity_low: 8.0
+  cpu_capacity_high: 8.0
+  cpu_allocatable_low: 7.91
+  cpu_allocatable_high: 7.91
+  mem_capacity_low: 62.809Gi
+  mem_capacity_high: 62.809Gi
+  mem_allocatable_low: 57.27Gi
+  mem_allocatable_high: 57.27Gi
+n2-highmem-16:
+  cpu_capacity_low: 16.0
+  cpu_capacity_high: 16.0
+  cpu_allocatable_low: 15.89
+  cpu_allocatable_high: 15.89
+  mem_capacity_low: 125.807Gi
+  mem_capacity_high: 125.81Gi
+  mem_allocatable_low: 116.549Gi
+  mem_allocatable_high: 116.551Gi
+n2-highmem-32:
+  cpu_capacity_low: 32.0
+  cpu_capacity_high: 32.0
+  cpu_allocatable_low: 31.85
+  cpu_allocatable_high: 31.85
+  mem_capacity_low: 251.897Gi
+  mem_capacity_high: 251.897Gi
+  mem_allocatable_low: 240.079Gi
+  mem_allocatable_high: 240.079Gi
+n1-highmem-4:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.92
+  cpu_allocatable_high: 3.92
+  mem_capacity_low: 25.451Gi
+  mem_capacity_high: 25.451Gi
+  mem_allocatable_low: 22.192Gi
+  mem_allocatable_high: 22.192Gi
+e2-highmem-16:
+  cpu_capacity_low: 16.0
+  cpu_capacity_high: 16.0
+  cpu_allocatable_low: 15.89
+  cpu_allocatable_high: 15.89
+  mem_capacity_low: 125.807Gi
+  mem_capacity_high: 125.807Gi
+  mem_allocatable_low: 116.549Gi
+  mem_allocatable_high: 116.549Gi
+n1-standard-2:
+  cpu_capacity_low: 2.0
+  cpu_capacity_high: 2.0
+  cpu_allocatable_low: 1.93
+  cpu_allocatable_high: 1.93
+  mem_capacity_low: 7.276Gi
+  mem_capacity_high: 7.276Gi
+  mem_allocatable_low: 5.483Gi
+  mem_allocatable_high: 5.483Gi
+n1-standard-8:
+  cpu_capacity_low: 8.0
+  cpu_capacity_high: 8.0
+  cpu_allocatable_low: 7.91
+  cpu_allocatable_high: 7.91
+  mem_capacity_low: 29.387Gi
+  mem_capacity_high: 29.387Gi
+  mem_allocatable_low: 25.888Gi
+  mem_allocatable_high: 25.888Gi
+n1-standard-16:
+  cpu_capacity_low: 16.0
+  cpu_capacity_high: 16.0
+  cpu_allocatable_low: 15.89
+  cpu_allocatable_high: 15.89
+  mem_capacity_low: 58.87Gi
+  mem_capacity_high: 58.87Gi
+  mem_allocatable_low: 53.571Gi
+  mem_allocatable_high: 53.571Gi
+
+# EKS instance types
+r5.xlarge:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.92
+  cpu_allocatable_high: 3.92
+  mem_capacity_low: 30.887Gi
+  mem_capacity_high: 30.907Gi
+  mem_allocatable_low: 29.917Gi
+  mem_allocatable_high: 29.937Gi
+r5.2xlarge:
+  cpu_capacity_low: 8.0
+  cpu_capacity_high: 8.0
+  cpu_allocatable_low: 7.91
+  cpu_allocatable_high: 7.91
+  mem_capacity_low: 62.011Gi
+  mem_capacity_high: 62.011Gi
+  mem_allocatable_low: 61.041Gi
+  mem_allocatable_high: 61.041Gi
+r5.4xlarge:
+  cpu_capacity_low: 16.0
+  cpu_capacity_high: 16.0
+  cpu_allocatable_low: 15.89
+  cpu_allocatable_high: 15.89
+  mem_capacity_low: 124.364Gi
+  mem_capacity_high: 124.364Gi
+  mem_allocatable_low: 121.504Gi
+  mem_allocatable_high: 121.504Gi
+m5.large:
+  cpu_capacity_low: 2.0
+  cpu_capacity_high: 2.0
+  cpu_allocatable_low: 1.93
+  cpu_allocatable_high: 1.93
+  mem_capacity_low: 7.473Gi
+  mem_capacity_high: 7.473Gi
+  mem_allocatable_low: 6.815Gi
+  mem_allocatable_high: 6.815Gi
+m5.xlarge:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.92
+  cpu_allocatable_high: 3.92
+  mem_capacity_low: 15.172Gi
+  mem_capacity_high: 15.344Gi
+  mem_allocatable_low: 14.202Gi
+  mem_allocatable_high: 14.375Gi
+g4dn.xlarge:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.92
+  cpu_allocatable_high: 3.92
+  mem_capacity_low: 15.333Gi
+  mem_capacity_high: 15.333Gi
+  mem_allocatable_low: 14.675Gi
+  mem_allocatable_high: 14.675Gi
+
+# AKS instance types
+Standard_E4s_v3:
+  cpu_capacity_low: 4.0
+  cpu_capacity_high: 4.0
+  cpu_allocatable_low: 3.86
+  cpu_allocatable_high: 3.86
+  mem_capacity_low: 31.354Gi
+  mem_capacity_high: 31.354Gi
+  mem_allocatable_low: 27.062Gi
+  mem_allocatable_high: 27.062Gi
+Standard_E8s_v3:
+  cpu_capacity_low: 8.0
+  cpu_capacity_high: 8.0
+  cpu_allocatable_low: 7.82
+  cpu_allocatable_high: 7.82
+  mem_capacity_low: 62.806Gi
+  mem_capacity_high: 62.806Gi
+  mem_allocatable_low: 56.594Gi
+  mem_allocatable_high: 56.594Gi