diff --git a/keps/sig-autoscaling/5053-hpa-fallback/README.md b/keps/sig-autoscaling/5053-hpa-fallback/README.md
new file mode 100644
index 00000000000..16b18c1b603
--- /dev/null
+++ b/keps/sig-autoscaling/5053-hpa-fallback/README.md
@@ -0,0 +1,893 @@
+<!--
+**Note:** When your KEP is complete, all of these comment blocks should be removed.
+
+To get started with this template:
+
+- [ ] **Merge early and iterate.**
+  Avoid getting hung up on specific details and instead aim to get the goals of
+  the KEP clarified and merged quickly. The best way to do this is to just
+  start with the high-level sections and fill out details incrementally in
+  subsequent PRs.
+
+Just because a KEP is merged does not mean it is complete or approved. Any KEP
+marked as `provisional` is a working document and subject to change. You can
+denote sections that are under active debate as follows:
+
+```
+<<[UNRESOLVED optional short context or usernames ]>>
+Stuff that is being argued.
+<<[/UNRESOLVED]>>
+```
+
+When editing KEPS, aim for tightly-scoped, single-topic PRs to keep discussions
+focused. If you disagree with what is already in a document, open a new PR
+with suggested changes.
+
+One KEP corresponds to one "feature" or "enhancement" for its whole lifecycle.
+You do not need a new KEP to move from beta to GA, for example. If
+new details emerge that belong in the KEP, edit the KEP. Once a feature has become
+"implemented", major changes should get new KEPs.
+
+The canonical place for the latest set of instructions (and the likely source
+of this file) is [here](/keps/NNNN-kep-template/README.md).
+
+**Note:** Any PRs to move a KEP to `implementable`, or significant changes once
+it is marked `implementable`, must be approved by each of the KEP approvers.
+If none of those approvers are still appropriate, then changes to that list
+should be approved by the remaining approvers and/or the owning SIG (or
+SIG Architecture for cross-cutting KEPs).
+-->
+# KEP-5053: Fallback for HPA on failure to retrieve metrics
+
+<!--
+This is the title of your KEP. Keep it short, simple, and descriptive. A good
+title can help communicate what the KEP is and should be considered as part of
+any review.
+-->
+
+<!--
+A table of contents is helpful for quickly jumping to sections of a KEP and for
+highlighting any additional information provided beyond the standard KEP
+template.
+
+Ensure the TOC is wrapped with
+  <code>&lt;!-- toc --&rt;&lt;!-- /toc --&rt;</code>
+tags, and then generate with `hack/update-toc.sh`.
+-->
+
+<!-- toc -->
+- [Release Signoff Checklist](#release-signoff-checklist)
+- [Summary](#summary)
+- [Motivation](#motivation)
+  - [Goals](#goals)
+  - [Non-Goals](#non-goals)
+- [Proposal](#proposal)
+  - [Risks and Mitigations](#risks-and-mitigations)
+- [Design Details](#design-details)
+  - [Test Plan](#test-plan)
+      - [Prerequisite testing updates](#prerequisite-testing-updates)
+      - [Unit tests](#unit-tests)
+      - [Integration tests](#integration-tests)
+      - [e2e tests](#e2e-tests)
+  - [Graduation Criteria](#graduation-criteria)
+    - [Alpha](#alpha)
+  - [Upgrade / Downgrade Strategy](#upgrade--downgrade-strategy)
+  - [Version Skew Strategy](#version-skew-strategy)
+- [Production Readiness Review Questionnaire](#production-readiness-review-questionnaire)
+  - [Feature Enablement and Rollback](#feature-enablement-and-rollback)
+  - [Rollout, Upgrade and Rollback Planning](#rollout-upgrade-and-rollback-planning)
+  - [Monitoring Requirements](#monitoring-requirements)
+  - [Dependencies](#dependencies)
+  - [Scalability](#scalability)
+  - [Troubleshooting](#troubleshooting)
+- [Implementation History](#implementation-history)
+- [Drawbacks](#drawbacks)
+- [Alternatives](#alternatives)
+- [Infrastructure Needed (Optional)](#infrastructure-needed-optional)
+<!-- /toc -->
+
+## Release Signoff Checklist
+
+<!--
+**ACTION REQUIRED:** In order to merge code into a release, there must be an
+issue in [kubernetes/enhancements] referencing this KEP and targeting a release
+milestone **before the [Enhancement Freeze](https://git.k8s.io/sig-release/releases)
+of the targeted release**.
+
+For enhancements that make changes to code or processes/procedures in core
+Kubernetes—i.e., [kubernetes/kubernetes], we require the following Release
+Signoff checklist to be completed.
+
+Check these off as they are completed for the Release Team to track. These
+checklist items _must_ be updated for the enhancement to be released.
+-->
+
+Items marked with (R) are required *prior to targeting to a milestone / release*.
+
+- [ ] (R) Enhancement issue in release milestone, which links to KEP dir in [kubernetes/enhancements] (not the initial KEP PR)
+- [ ] (R) KEP approvers have approved the KEP status as `implementable`
+- [ ] (R) Design details are appropriately documented
+- [ ] (R) Test plan is in place, giving consideration to SIG Architecture and SIG Testing input (including test refactors)
+  - [ ] e2e Tests for all Beta API Operations (endpoints)
+  - [ ] (R) Ensure GA e2e tests meet requirements for [Conformance Tests](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/conformance-tests.md) 
+  - [ ] (R) Minimum Two Week Window for GA e2e tests to prove flake free
+- [ ] (R) Graduation criteria is in place
+  - [ ] (R) [all GA Endpoints](https://github.com/kubernetes/community/pull/1806) must be hit by [Conformance Tests](https://github.com/kubernetes/community/blob/master/contributors/devel/sig-architecture/conformance-tests.md) 
+- [ ] (R) Production readiness review completed
+- [ ] (R) Production readiness review approved
+- [ ] "Implementation History" section is up-to-date for milestone
+- [ ] User-facing documentation has been created in [kubernetes/website], for publication to [kubernetes.io]
+- [ ] Supporting documentation—e.g., additional design documents, links to mailing list discussions/SIG meetings, relevant PRs/issues, release notes
+
+<!--
+**Note:** This checklist is iterative and should be reviewed and updated every time this enhancement is being considered for a milestone.
+-->
+
+[kubernetes.io]: https://kubernetes.io/
+[kubernetes/enhancements]: https://git.k8s.io/enhancements
+[kubernetes/kubernetes]: https://git.k8s.io/kubernetes
+[kubernetes/website]: https://git.k8s.io/website
+
+## Summary
+
+The [Horizontal Pod Autoscaler (HPA)][] relies on the controller manager to 
+fetch metrics from either the resource metrics API (for per-pod resource metrics) 
+or the custom metrics API (for other types of metrics). When these APIs experience 
+downtime, the HPA becomes unable to make scaling decisions, potentially leaving 
+workloads unmanaged.
+
+This proposal introduces a new configuration parameter for the HPA, enabling 
+users to define behavior in the event of metric retrieval failures. For example, 
+users can opt to scale the target resource to the maximum number of replicas 
+specified in the HPA, ensuring safer operation during metrics unavailability.
+
+[Horizontal Pod Autoscaler (HPA)]: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
+
+## Motivation
+
+The Horizontal Pod Autoscaler (HPA) is a critical component for scaling Kubernetes 
+workloads based on resource utilization or custom metrics. However, the current 
+implementation depends entirely on the availability of the resource metrics API 
+or custom metrics API to make scaling decisions. If these APIs experience 
+downtime or degradation, the HPA cannot take any scaling actions, leaving 
+workloads potentially overprovisioned, underprovisioned, or entirely unmanaged.
+
+In contrast, other autoscalers like [KEDA][] already provide mechanisms to define 
+fallback strategies in the event of metric retrieval failures. These strategies 
+mitigate the impact of API unavailability, enabling the autoscaler to maintain 
+a functional scaling strategy even when metrics are temporarily inaccessible.
+
+By allowing users to configure fallback behavior in HPA, this proposal aims to 
+reduce the criticality of the metrics APIs and improve the overall robustness 
+of the autoscaling system. This change allows users to define safe scaling 
+actions, both as scaling to a predefined maximum or holding the current scale 
+(current behavior), ensuring workloads remain operational and better aligned 
+with user-defined requirements during unexpected disruptions.
+
+Additionally, the community has also expressed interest in addressing this
+limitation in the past. ([#109214][])
+
+[KEDA]: https://keda.sh/docs/2.15/reference/scaledobject-spec/#fallback
+[#109214]: https://github.com/kubernetes/kubernetes/issues/109214
+
+### Goals
+
+- Allow users to optionally define the number of replicas to scale in the case of metric retrieval failure.
+
+### Non-Goals
+
+- N/A
+
+## Proposal
+
+Heavily inspired by [KEDA][] propose to add a new field to the existing [`HorizontalPodAutoscalerBehavior`][] object:
+
+- `fallback`: an optional new object containing the following fields:
+  - `failureThreshold`: (integer) the number of failures fetching metrics to trigger the fallback behavior. Must be a value greater than 0. This field is optional and defaults to 3 if not specified.
+  - `replicas`: (integer) the number of replicas to scale to in case of fallback. Must be greater than 0 and it's mandatory.
+
+To allow for tracking of failures to fetch metrics a new field should be added to the existing [`HorizontalPodAutoscalerStatus`][] object:
+- `consecutiveMetricRetrievalFailureCount`: (integer) tracks the number of consecutive failures in retrieving metrics.
+
+When the `behavior` field on the [`HorizontalPodAutoscalerSpec`][] or the `fallback` field in the [`HorizontalPodAutoscalerBehavior`][] 
+are not specified, the current behavior is preserved, meaning no scaling operations will occur in the event of a metrics retrieval failure.
+
+[KEDA]: https://keda.sh/docs/2.15/reference/scaledobject-spec/#fallback
+[HorizontalPodAutoscalerBehavior]: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.31/#horizontalpodautoscalerbehavior-v2-autoscaling
+[HorizontalPodAutoscalerStatus]: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.31/#horizontalpodautoscalerstatus-v2-autoscaling
+[HorizontalPodAutoscalerSpec]: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.31/#horizontalpodautoscalerspec-v2-autoscaling
+
+### Risks and Mitigations
+
+There should be minimal risk introduced by the proposed changes:
+- The new field is optional, and its absence results in no changes to the current autoscaling behavior
+- If a change to the new field results in undesirable behavior, the change can be reverted by deploying the previous version of the HPA resource, or removing the `fallback` field entirely.
+
+## Design Details
+
+The `HorizontalPodAutoscaler` API is updated to have a new object `HPAFallback`:
+
+```golang
+type HPAFallback struct {
+    // failureThreshold is the number of failures fetching metrics to trigger the 
+    // fallback behavior.
+    // +optional
+    FailureThreshold *int32
+    
+    // failureThreshold is the number of replicas to scale to in case of fallback.
+    Replicas int32
+}
+```
+
+The `HorizontalPodAutoscaler` API is updated to add a new `fallback` field to the `HorizontalPodAutoscalerBehavior` object:
+
+```golang
+type HorizontalPodAutoscalerBehavior struct {
+    // fallback specifies the number of replicas to scale the object to during a 
+    // fallback state and defines the threshold for errors required to enter the 
+    // fallback state.
+    //+optional
+    Fallback *HPAFallback
+
+    // Existing fields.
+    ScaleUp *HPAScalingRules
+    ScaleDown *HPAScalingRules
+}
+```
+
+The `HorizontalPodAutoscaler` API is updated to have a new description of the `behavior` field on the `HorizontalPodAutoscalerSpec` object:
+
+```golang
+type HorizontalPodAutoscalerSpec struct {
+    // behavior configures the scaling behavior of the target, including 
+	// scale-up and scale-down policies, as well as fallback behavior in case
+	// of metric retrieval failures. If not set, the default HPAScalingRules 
+	// are used for scaling decisions, and no scaling operation will occur 
+	// when metrics retrieval fails.
+    // +optional
+    Behavior *HorizontalPodAutoscalerBehavior
+
+    // Existing fields.
+    ScaleTargetRef CrossVersionObjectReference
+    MinReplicas *int32
+    MaxReplicas int32
+    Metrics []MetricSpec
+}
+```
+
+The `HorizontalPodAutoscaler` API is updated to add a new `fallback` field to the `HorizontalPodAutoscalerStatus` object:
+
+```golang
+type HorizontalPodAutoscalerStatus struct {
+    // consecutiveMetricRetrievalFailureCount tracks the number of consecutive failures in retrieving metrics. 
+    //+optional
+    ConsecutiveMetricRetrievalFailureCount int32
+    
+    // Existing fields.
+    ObservedGeneration *int64
+    LastScaleTime *metav1.Time
+    CurrentReplicas int32
+    DesiredReplicas int32
+    CurrentMetrics []MetricStatus
+    Conditions []HorizontalPodAutoscalerCondition
+}
+```
+The `HorizontalPodAutoscaler` API is updated to introduce a new FallbackActive condition to the `HorizontalPodAutoscalerConditionType`:
+
+```golang
+const (
+    // FallbackActive indicates that the HPA has entered the fallback state due to repeated
+    // metric retrieval failures and is applying the configured fallback behavior.
+    FallbackActive HorizontalPodAutoscalerConditionType = "FallbackActive"
+
+    // Existing conditions
+    ScalingActive HorizontalPodAutoscalerConditionType = "ScalingActive"
+    AbleToScale HorizontalPodAutoscalerConditionType = "AbleToScale"
+    ScalingLimited HorizontalPodAutoscalerConditionType = "ScalingLimited"
+)
+```
+
+The new fallback field will be used in the autoscaling controller
+[horizontal.go][]. The current logic is:
+
+```golang
+if err != nil && metricDesiredReplicas == -1 {
+    a.setCurrentReplicasAndMetricsInStatus(hpa, currentReplicas, metricStatuses)
+    if err := a.updateStatusIfNeeded(ctx, hpaStatusOriginal, hpa); err != nil {
+        utilruntime.HandleError(err)
+    }
+    a.eventRecorder.Event(hpa, v1.EventTypeWarning, "FailedComputeMetricsReplicas", err.Error())
+    return fmt.Errorf("failed to compute desired number of replicas based on listed metrics for %s: %v", reference, err)
+}
+```
+
+It will be replaced by:
+
+```golang
+if err != nil && metricDesiredReplicas == -1 {
+    a.increaseConsecutiveMetricRetrievalFailureCount(hpa)
+    a.eventRecorder.Event(hpa, v1.EventTypeWarning, "FailedComputeMetricsReplicas", err.Error())
+    
+    var inFallback bool
+    
+    if hpa.Spec.Fallback != nil {
+        var failureThreshold int32
+        
+        if hpa.Spec.Fallback.FailureThreshold != nil {
+            failureThreshold = *hpa.Spec.Fallback.FailureThreshold
+        } else {
+            // Default value
+            failureThreshold = 3
+        }
+        
+        if failureThreshold < hpa.Status.ConsecutiveMetricRetrievalFailureCount {
+            inFallback = true
+            metricDesiredReplicas = hpa.Spec.Fallback.Replicas
+            a.eventRecorder.Event(hpa, v1.EventTypeWarning, "FallbackThresholdReached", err.Error())
+            setCondition(hpa, autoscalingv2.FallbackActive, v1.ConditionTrue, "FallbackThresholdReached", "%s", err.Error())
+        } else {
+            setCondition(hpa, autoscalingv2.FallbackActive, v1.ConditionFalse, "FallbackThresholdNotReached", "Threshold is set to %d failures. Current failure count is %d", failureThreshold, hpa.Status.ConsecutiveMetricRetrievalFailureCount)
+            inFallback = false
+        }
+    } else {
+        setCondition(hpa, autoscalingv2.FallbackActive, v1.ConditionFalse, "NoFallbackDefined", "No fallback behavior is defined")
+        inFallback = false
+    }
+    
+    if !inFallback {
+        a.setCurrentReplicasAndMetricsInStatus(hpa, currentReplicas, metricStatuses)
+        if err := a.updateStatusIfNeeded(ctx, hpaStatusOriginal, hpa); err != nil {
+            utilruntime.HandleError(err)
+        }
+        return fmt.Errorf("failed to compute desired number of replicas based on listed metrics for %s: %v", reference, err)
+    }
+}
+setCondition(hpa, autoscalingv2.FallbackActive, v1.ConditionFalse, "SucceededToComputeDesiredReplicas", "the HPA controller was able to compute the desired replicas")
+```
+
+[horizontal.go]: https://github.com/kubernetes/kubernetes/blob/master/pkg/controller/podautoscaler/horizontal.go
+
+### Test Plan
+
+<!--
+**Note:** *Not required until targeted at a release.*
+The goal is to ensure that we don't accept enhancements with inadequate testing.
+
+All code is expected to have adequate tests (eventually with coverage
+expectations). Please adhere to the [Kubernetes testing guidelines][testing-guidelines]
+when drafting this test plan.
+
+[testing-guidelines]: https://git.k8s.io/community/contributors/devel/sig-testing/testing.md
+-->
+
+[ ] I/we understand the owners of the involved components may require updates to
+existing tests to make this code solid enough prior to committing the changes necessary
+to implement this enhancement.
+
+##### Prerequisite testing updates
+
+<!--
+Based on reviewers feedback describe what additional tests need to be added prior
+implementing this enhancement to ensure the enhancements have also solid foundations.
+-->
+
+##### Unit tests
+
+<!--
+In principle every added code should have complete unit test coverage, so providing
+the exact set of tests will not bring additional value.
+However, if complete unit test coverage is not possible, explain the reason of it
+together with explanation why this is acceptable.
+-->
+
+<!--
+Additionally, for Alpha try to enumerate the core package you will be touching
+to implement this enhancement and provide the current unit coverage for those
+in the form of:
+- <package>: <date> - <current test coverage>
+The data can be easily read from:
+https://testgrid.k8s.io/sig-testing-canaries#ci-kubernetes-coverage-unit
+
+This can inform certain test coverage improvements that we want to do before
+extending the production code to implement this enhancement.
+-->
+
+- `<package>`: `<date>` - `<test coverage>`
+
+##### Integration tests
+
+<!--
+Integration tests are contained in k8s.io/kubernetes/test/integration.
+Integration tests allow control of the configuration parameters used to start the binaries under test.
+This is different from e2e tests which do not allow configuration of parameters.
+Doing this allows testing non-default options and multiple different and potentially conflicting command line options.
+-->
+
+<!--
+This question should be filled when targeting a release.
+For Alpha, describe what tests will be added to ensure proper quality of the enhancement.
+
+For Beta and GA, add links to added tests together with links to k8s-triage for those tests:
+https://storage.googleapis.com/k8s-triage/index.html
+-->
+
+- <test>: <link to test coverage>
+
+##### e2e tests
+
+<!--
+This question should be filled when targeting a release.
+For Alpha, describe what tests will be added to ensure proper quality of the enhancement.
+
+For Beta and GA, add links to added tests together with links to k8s-triage for those tests:
+https://storage.googleapis.com/k8s-triage/index.html
+
+We expect no non-infra related flakes in the last month as a GA graduation criteria.
+-->
+
+Will the follow [e2e autoscaling tests]:
+
+- Failure of retrieving metrics over the threshold scales the resource with the configured replicas
+- Success in retrieving metrics should reset the `ConsecutiveMetricRetrievalFailureCount` in the `HorizontalPodAutoscalerStatus`
+- When `fallback` is not set the resource should not scale when failing to retrieve metrics
+
+[e2e autoscaling tests]: https://github.com/kubernetes/kubernetes/tree/master/test/e2e/autoscaling
+
+### Graduation Criteria
+
+<!--
+**Note:** *Not required until targeted at a release.*
+
+Define graduation milestones.
+
+These may be defined in terms of API maturity, [feature gate] graduations, or as
+something else. The KEP should keep this high-level with a focus on what
+signals will be looked at to determine graduation.
+
+Consider the following in developing the graduation criteria for this enhancement:
+- [Maturity levels (`alpha`, `beta`, `stable`)][maturity-levels]
+- [Feature gate][feature gate] lifecycle
+- [Deprecation policy][deprecation-policy]
+
+Clearly define what graduation means by either linking to the [API doc
+definition](https://kubernetes.io/docs/concepts/overview/kubernetes-api/#api-versioning)
+or by redefining what graduation means.
+
+In general we try to use the same stages (alpha, beta, GA), regardless of how the
+functionality is accessed.
+
+[feature gate]: https://git.k8s.io/community/contributors/devel/sig-architecture/feature-gates.md
+[maturity-levels]: https://git.k8s.io/community/contributors/devel/sig-architecture/api_changes.md#alpha-beta-and-stable-versions
+[deprecation-policy]: https://kubernetes.io/docs/reference/using-api/deprecation-policy/
+
+Below are some examples to consider, in addition to the aforementioned [maturity levels][maturity-levels].
+
+#### Alpha
+
+- Feature implemented behind a feature flag
+- Initial e2e tests completed and enabled
+
+#### Beta
+
+- Gather feedback from developers and surveys
+- Complete features A, B, C
+- Additional tests are in Testgrid and linked in KEP
+
+#### GA
+
+- N examples of real-world usage
+- N installs
+- More rigorous forms of testing—e.g., downgrade tests and scalability tests
+- Allowing time for feedback
+
+**Note:** Generally we also wait at least two releases between beta and
+GA/stable, because there's no opportunity for user feedback, or even bug reports,
+in back-to-back releases.
+
+**For non-optional features moving to GA, the graduation criteria must include
+[conformance tests].**
+
+[conformance tests]: https://git.k8s.io/community/contributors/devel/sig-architecture/conformance-tests.md
+
+#### Deprecation
+
+- Announce deprecation and support policy of the existing flag
+- Two versions passed since introducing the functionality that deprecates the flag (to address version skew)
+- Address feedback on usage/changed behavior, provided on GitHub issues
+- Deprecate the flag
+-->
+
+#### Alpha
+
+- Feature implemented behind a `HPAFallback` feature flag
+- Initial e2e tests completed and enabled
+
+### Upgrade / Downgrade Strategy
+
+When the feature flag is enabled, the `kube-controller-manager` should begin 
+counting concurrent failures starting from 0. If the feature flag is disabled, 
+the status should always reflect `MetricRetrievalFailureCount` as 0.
+
+All logic related to metric retrieval failure and `MetricRetrievalFailureCount` 
+evaluation must be gated by the same feature flag. This means that if the feature 
+flag is rolled back, any ongoing metrics retrieval failures will not affect scaling 
+behavior, and the resource will continue with the same scale as it did prior to 
+the feature being disabled.
+
+<!--
+If applicable, how will the component be upgraded and downgraded? Make sure
+this is in the test plan.
+
+Consider the following in developing an upgrade/downgrade strategy for this
+enhancement:
+- What changes (in invocations, configurations, API use, etc.) is an existing
+  cluster required to make on upgrade, in order to maintain previous behavior?
+- What changes (in invocations, configurations, API use, etc.) is an existing
+  cluster required to make on upgrade, in order to make use of the enhancement?
+-->
+
+### Version Skew Strategy
+
+<!--
+If applicable, how will the component handle version skew with other
+components? What are the guarantees? Make sure this is in the test plan.
+
+Consider the following in developing a version skew strategy for this
+enhancement:
+- Does this enhancement involve coordinating behavior in the control plane and nodes?
+- How does an n-3 kubelet or kube-proxy without this feature available behave when this feature is used?
+- How does an n-1 kube-controller-manager or kube-scheduler without this feature available behave when this feature is used?
+- Will any other components on the node change? For example, changes to CSI,
+  CRI or CNI may require updating that component before the kubelet.
+-->
+
+## Production Readiness Review Questionnaire
+
+<!--
+
+Production readiness reviews are intended to ensure that features merging into
+Kubernetes are observable, scalable and supportable; can be safely operated in
+production environments, and can be disabled or rolled back in the event they
+cause increased failures in production. See more in the PRR KEP at
+https://git.k8s.io/enhancements/keps/sig-architecture/1194-prod-readiness.
+
+The production readiness review questionnaire must be completed and approved
+for the KEP to move to `implementable` status and be included in the release.
+
+In some cases, the questions below should also have answers in `kep.yaml`. This
+is to enable automation to verify the presence of the review, and to reduce review
+burden and latency.
+
+The KEP must have a approver from the
+[`prod-readiness-approvers`](http://git.k8s.io/enhancements/OWNERS_ALIASES)
+team. Please reach out on the
+[#prod-readiness](https://kubernetes.slack.com/archives/CPNHUMN74) channel if
+you need any help or guidance.
+-->
+
+### Feature Enablement and Rollback
+
+<!--
+This section must be completed when targeting alpha to a release.
+-->
+
+###### How can this feature be enabled / disabled in a live cluster?
+
+- [x] Feature gate (also fill in values in `kep.yaml`)
+  - Feature gate name: HPAFallback
+  - Components depending on the feature gate: `kube-controller-manager`
+
+###### Does enabling the feature change any default behavior?
+
+No.
+
+###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?
+
+Yes.
+
+###### What happens if we reenable the feature if it was previously rolled back?
+
+###### Are there any tests for feature enablement/disablement?
+
+<!--
+The e2e framework does not currently support enabling or disabling feature
+gates. However, unit tests in each component dealing with managing data, created
+with and without the feature, are necessary. At the very least, think about
+conversion tests if API types are being modified.
+
+Additionally, for features that are introducing a new API field, unit tests that
+are exercising the `switch` of feature gate itself (what happens if I disable a
+feature gate after having objects written with the new field) are also critical.
+You can take a look at one potential example of such test in:
+https://github.com/kubernetes/kubernetes/pull/97058/files#diff-7826f7adbc1996a05ab52e3f5f02429e94b68ce6bce0dc534d1be636154fded3R246-R282
+-->
+
+### Rollout, Upgrade and Rollback Planning
+
+<!--
+This section must be completed when targeting beta to a release.
+-->
+
+###### How can a rollout or rollback fail? Can it impact already running workloads?
+
+<!--
+Try to be as paranoid as possible - e.g., what if some components will restart
+mid-rollout?
+
+Be sure to consider highly-available clusters, where, for example,
+feature flags will be enabled on some API servers and not others during the
+rollout. Similarly, consider large clusters and how enablement/disablement
+will rollout across nodes.
+-->
+
+###### What specific metrics should inform a rollback?
+
+<!--
+What signals should users be paying attention to when the feature is young
+that might indicate a serious problem?
+-->
+
+###### Were upgrade and rollback tested? Was the upgrade->downgrade->upgrade path tested?
+
+<!--
+Describe manual testing that was done and the outcomes.
+Longer term, we may want to require automated upgrade/rollback tests, but we
+are missing a bunch of machinery and tooling and can't do that now.
+-->
+
+###### Is the rollout accompanied by any deprecations and/or removals of features, APIs, fields of API types, flags, etc.?
+
+<!--
+Even if applying deprecation policies, they may still surprise some users.
+-->
+
+### Monitoring Requirements
+
+<!--
+This section must be completed when targeting beta to a release.
+
+For GA, this section is required: approvers should be able to confirm the
+previous answers based on experience in the field.
+-->
+
+###### How can an operator determine if the feature is in use by workloads?
+
+<!--
+Ideally, this should be a metric. Operations against the Kubernetes API (e.g.,
+checking if there are objects with field X set) may be a last resort. Avoid
+logs or events for this purpose.
+-->
+
+###### How can someone using this feature know that it is working for their instance?
+
+<!--
+For instance, if this is a pod-related feature, it should be possible to determine if the feature is functioning properly
+for each individual pod.
+Pick one more of these and delete the rest.
+Please describe all items visible to end users below with sufficient detail so that they can verify correct enablement
+and operation of this feature.
+Recall that end users cannot usually observe component logs or access metrics.
+-->
+
+- [ ] Events
+  - Event Reason: 
+- [ ] API .status
+  - Condition name: 
+  - Other field: 
+- [ ] Other (treat as last resort)
+  - Details:
+
+###### What are the reasonable SLOs (Service Level Objectives) for the enhancement?
+
+<!--
+This is your opportunity to define what "normal" quality of service looks like
+for a feature.
+
+It's impossible to provide comprehensive guidance, but at the very
+high level (needs more precise definitions) those may be things like:
+  - per-day percentage of API calls finishing with 5XX errors <= 1%
+  - 99% percentile over day of absolute value from (job creation time minus expected
+    job creation time) for cron job <= 10%
+  - 99.9% of /health requests per day finish with 200 code
+
+These goals will help you determine what you need to measure (SLIs) in the next
+question.
+-->
+
+###### What are the SLIs (Service Level Indicators) an operator can use to determine the health of the service?
+
+<!--
+Pick one more of these and delete the rest.
+-->
+
+- [ ] Metrics
+  - Metric name:
+  - [Optional] Aggregation method:
+  - Components exposing the metric:
+- [ ] Other (treat as last resort)
+  - Details:
+
+###### Are there any missing metrics that would be useful to have to improve observability of this feature?
+
+<!--
+Describe the metrics themselves and the reasons why they weren't added (e.g., cost,
+implementation difficulties, etc.).
+-->
+
+### Dependencies
+
+<!--
+This section must be completed when targeting beta to a release.
+-->
+
+###### Does this feature depend on any specific services running in the cluster?
+
+<!--
+Think about both cluster-level services (e.g. metrics-server) as well
+as node-level agents (e.g. specific version of CRI). Focus on external or
+optional services that are needed. For example, if this feature depends on
+a cloud provider API, or upon an external software-defined storage or network
+control plane.
+
+For each of these, fill in the following—thinking about running existing user workloads
+and creating new ones, as well as about cluster-level services (e.g. DNS):
+  - [Dependency name]
+    - Usage description:
+      - Impact of its outage on the feature:
+      - Impact of its degraded performance or high-error rates on the feature:
+-->
+
+### Scalability
+
+<!--
+For alpha, this section is encouraged: reviewers should consider these questions
+and attempt to answer them.
+
+For beta, this section is required: reviewers must answer these questions.
+
+For GA, this section is required: approvers should be able to confirm the
+previous answers based on experience in the field.
+-->
+
+###### Will enabling / using this feature result in any new API calls?
+
+<!--
+Describe them, providing:
+  - API call type (e.g. PATCH pods)
+  - estimated throughput
+  - originating component(s) (e.g. Kubelet, Feature-X-controller)
+Focusing mostly on:
+  - components listing and/or watching resources they didn't before
+  - API calls that may be triggered by changes of some Kubernetes resources
+    (e.g. update of object X triggers new updates of object Y)
+  - periodic API calls to reconcile state (e.g. periodic fetching state,
+    heartbeats, leader election, etc.)
+-->
+
+###### Will enabling / using this feature result in introducing new API types?
+
+<!--
+Describe them, providing:
+  - API type
+  - Supported number of objects per cluster
+  - Supported number of objects per namespace (for namespace-scoped objects)
+-->
+
+###### Will enabling / using this feature result in any new calls to the cloud provider?
+
+<!--
+Describe them, providing:
+  - Which API(s):
+  - Estimated increase:
+-->
+
+###### Will enabling / using this feature result in increasing size or count of the existing API objects?
+
+<!--
+Describe them, providing:
+  - API type(s):
+  - Estimated increase in size: (e.g., new annotation of size 32B)
+  - Estimated amount of new objects: (e.g., new Object X for every existing Pod)
+-->
+
+###### Will enabling / using this feature result in increasing time taken by any operations covered by existing SLIs/SLOs?
+
+<!--
+Look at the [existing SLIs/SLOs].
+
+Think about adding additional work or introducing new steps in between
+(e.g. need to do X to start a container), etc. Please describe the details.
+
+[existing SLIs/SLOs]: https://git.k8s.io/community/sig-scalability/slos/slos.md#kubernetes-slisslos
+-->
+
+###### Will enabling / using this feature result in non-negligible increase of resource usage (CPU, RAM, disk, IO, ...) in any components?
+
+<!--
+Things to keep in mind include: additional in-memory state, additional
+non-trivial computations, excessive access to disks (including increased log
+volume), significant amount of data sent and/or received over network, etc.
+This through this both in small and large cases, again with respect to the
+[supported limits].
+
+[supported limits]: https://git.k8s.io/community//sig-scalability/configs-and-limits/thresholds.md
+-->
+
+###### Can enabling / using this feature result in resource exhaustion of some node resources (PIDs, sockets, inodes, etc.)?
+
+<!--
+Focus not just on happy cases, but primarily on more pathological cases
+(e.g. probes taking a minute instead of milliseconds, failed pods consuming resources, etc.).
+If any of the resources can be exhausted, how this is mitigated with the existing limits
+(e.g. pods per node) or new limits added by this KEP?
+
+Are there any tests that were run/should be run to understand performance characteristics better
+and validate the declared limits?
+-->
+
+### Troubleshooting
+
+<!--
+This section must be completed when targeting beta to a release.
+
+For GA, this section is required: approvers should be able to confirm the
+previous answers based on experience in the field.
+
+The Troubleshooting section currently serves the `Playbook` role. We may consider
+splitting it into a dedicated `Playbook` document (potentially with some monitoring
+details). For now, we leave it here.
+-->
+
+###### How does this feature react if the API server and/or etcd is unavailable?
+
+###### What are other known failure modes?
+
+<!--
+For each of them, fill in the following information by copying the below template:
+  - [Failure mode brief description]
+    - Detection: How can it be detected via metrics? Stated another way:
+      how can an operator troubleshoot without logging into a master or worker node?
+    - Mitigations: What can be done to stop the bleeding, especially for already
+      running user workloads?
+    - Diagnostics: What are the useful log messages and their required logging
+      levels that could help debug the issue?
+      Not required until feature graduated to beta.
+    - Testing: Are there any tests for failure mode? If not, describe why.
+-->
+
+###### What steps should be taken if SLOs are not being met to determine the problem?
+
+## Implementation History
+
+<!--
+Major milestones in the lifecycle of a KEP should be tracked in this section.
+Major milestones might include:
+- the `Summary` and `Motivation` sections being merged, signaling SIG acceptance
+- the `Proposal` section being merged, signaling agreement on a proposed design
+- the date implementation started
+- the first Kubernetes release where an initial version of the KEP was available
+- the version of Kubernetes where the KEP graduated to general availability
+- when the KEP was retired or superseded
+-->
+
+## Drawbacks
+
+<!--
+Why should this KEP _not_ be implemented?
+-->
+
+## Alternatives
+
+<!--
+What other approaches did you consider, and why did you rule them out? These do
+not need to be as detailed as the proposal, but should include enough
+information to express the idea and why it was not acceptable.
+-->
+
+## Infrastructure Needed (Optional)
+
+<!--
+Use this section if you need things from the project/SIG. Examples include a
+new subproject, repos requested, or GitHub details. Listing these here allows a
+SIG to get the process for these resources started right away.
+-->
diff --git a/keps/sig-autoscaling/5053-hpa-fallback/kep.yaml b/keps/sig-autoscaling/5053-hpa-fallback/kep.yaml
new file mode 100644
index 00000000000..fce1b70e160
--- /dev/null
+++ b/keps/sig-autoscaling/5053-hpa-fallback/kep.yaml
@@ -0,0 +1,38 @@
+title: Fallback for HPA on failure to retrieve metrics
+kep-number: 5053
+authors:
+  - "@be0x74a"
+owning-sig: sig-autoscaling
+status: provisional
+creation-date: 2025-01-20
+reviewers:
+  - TBD
+approvers:
+  - TBD
+
+# The target maturity stage in the current dev cycle for this KEP.
+stage: alpha
+
+# The most recent milestone for which work toward delivery of this KEP has been
+# done. This can be the current (upcoming) milestone, if it is being actively
+# worked on.
+latest-milestone: TBD
+
+# The milestone at which this feature was, or is targeted to be, at each stage.
+milestone:
+  alpha: TBD
+  beta: TBD
+  stable: TBD
+
+# The following PRR answers are required at alpha release
+# List the feature gate name and the components for which it must be enabled
+feature-gates:
+  - name: HPAFallback
+    components:
+      - kube-apiserver
+      - kube-controller-manager
+disable-supported: true
+
+# The following PRR answers are required at beta release
+#metrics:
+#  - my_feature_metric