diff --git a/keps/prod-readiness/sig-autoscaling/2021.yaml b/keps/prod-readiness/sig-autoscaling/2021.yaml
new file mode 100644
index 00000000000..7f77331cf17
--- /dev/null
+++ b/keps/prod-readiness/sig-autoscaling/2021.yaml
@@ -0,0 +1,3 @@
+kep-number: 2021
+alpha:
+  approver: "@johnbelamaric"
diff --git a/keps/sig-autoscaling/2021-scale-from-zero/README.md b/keps/sig-autoscaling/2021-scale-from-zero/README.md
new file mode 100644
index 00000000000..2190de5b574
--- /dev/null
+++ b/keps/sig-autoscaling/2021-scale-from-zero/README.md
@@ -0,0 +1,971 @@
+<!--
+**Note:** When your KEP is complete, all of these comment blocks should be removed.
+
+To get started with this template:
+
+- [x] **Pick a hosting SIG.**
+  Make sure that the problem space is something the SIG is interested in taking
+  up. KEPs should not be checked in without a sponsoring SIG.
+- [x] **Create an issue in kubernetes/enhancements**
+  When filing an enhancement tracking issue, please make sure to complete all
+  fields in that template. One of the fields asks for a link to the KEP. You
+  can leave that blank until this KEP is filed, and then go back to the
+  enhancement and add the link.
+- [x] **Make a copy of this template directory.**
+  Copy this template into the owning SIG's directory and name it
+  `NNNN-short-descriptive-title`, where `NNNN` is the issue number (with no
+  leading-zero padding) assigned to your enhancement above.
+- [x] **Fill out as much of the kep.yaml file as you can.**
+  At minimum, you should fill in the "Title", "Authors", "Owning-sig",
+  "Status", and date-related fields.
+- [x] **Fill out this file as best you can.**
+  At minimum, you should fill in the "Summary" and "Motivation" sections.
+  These should be easy if you've preflighted the idea of the KEP with the
+  appropriate SIG(s).
+- [x] **Create a PR for this KEP.**
+  Assign it to people in the SIG who are sponsoring this process.
+- [ ] **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-2021: HPA supports scaling to/from zero pods for object/external 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)
+  - [User Stories (Optional)](#user-stories-optional)
+    - [Story 1: Scale a heavy queue consumer on-demand](#story-1-scale-a-heavy-queue-consumer-on-demand)
+  - [Notes/Constraints/Caveats (Optional)](#notesconstraintscaveats-optional)
+  - [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)
+    - [Beta](#beta)
+    - [GA](#ga)
+  - [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/website]: https://git.k8s.io/website
+
+## Summary
+
+[Horizontal Pod Autoscaler][] (HPA) automatically scales the number of pods in any resource which supports the `scale` subresource based on observed CPU or memory utilization
+(or, with custom metrics support, on some other application-provided metrics) from one to many replicas. This proposal adds support for scaling from zero to many replicas and back to zero for object and external metrics.
+
+Scaling to zero is particularly effective for cost reduction when individual pods demand substantial resource requests, such as dedicated CPUs or GPUs. Since CPU and memory utilization can only be measured on running pods, scaling to zero will be limited to object and external metrics.
+
+[Horizontal Pod Autoscaler]: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/
+
+<!--
+This section is incredibly important for producing high-quality, user-focused
+documentation such as release notes or a development roadmap. It should be
+possible to collect this information before implementation begins, in order to
+avoid requiring implementors to split their attention between writing release
+notes and implementing the feature itself. KEP editors and SIG Docs
+should help to ensure that the tone and content of the `Summary` section is
+useful for a wide audience.
+
+A good summary is probably at least a paragraph in length.
+
+Both in this section and below, follow the guidelines of the [documentation
+style guide]. In particular, wrap lines to a reasonable length, to make it
+easier for reviewers to cite specific portions, and to minimize diff churn on
+updates.
+
+[documentation style guide]: https://github.com/kubernetes/community/blob/master/contributors/guide/style-guide.md
+-->
+
+## Motivation
+
+<!--
+This section is for explicitly listing the motivation, goals, and non-goals of
+this KEP.  Describe why the change is important and the benefits to users. The
+motivation section can optionally provide links to [experience reports] to
+demonstrate the interest in a KEP within the wider Kubernetes community.
+
+[experience reports]: https://github.com/golang/go/wiki/ExperienceReports
+-->
+
+With the addition of scaling based on object and external metrics it became possible to automatically adjust the number of running replicas based on an application
+provided metric. A typical use-case case for this is scaling the number of queue consumers based on the length of the consumed queue.
+
+In cases of a frequently idle queue or a less latency-sensitive workload, there is no need to keep one replica running at all times.
+Instead, you can dynamically scale to zero replicas, especially for workloads with high resource demands, such as those requiring GPUs.
+This approach not only reduces costs but also has significant energy-saving potential, particularly as GPU workloads become more prevalent.
+When replicas are scaled to zero, the HPA must also be capable of scaling back up as soon as messages become available.
+
+### Goals
+
+<!--
+List the specific goals of the KEP. What is it trying to achieve? How will we
+know that this has succeeded?
+-->
+
+- Provide scaling to zero replicas for object and external metrics
+- Provide scaling from zero replicas for object and external metrics
+
+### Non-Goals
+
+<!--
+What is out of scope for this KEP? Listing non-goals helps to focus discussion
+and make progress.
+-->
+
+- Provide scaling to/from zero replicas for resource metrics
+- Provide request buffering at the Kubernetes Service level
+
+## Proposal
+
+<!--
+This is where we get down to the specifics of what the proposal actually is.
+This should have enough detail that reviewers can understand exactly what
+you're proposing, but should not include things like API designs or
+implementation. What is the desired outcome and how do we measure success?.
+The "Design Details" section below is for the real
+nitty-gritty.
+-->
+
+Allow the HPA to scale from and to zero using `minReplicas: 0` and a HPA status condition.
+
+### User Stories (Optional)
+
+<!--
+Detail the things that people will be able to do if this KEP is implemented.
+Include as much detail as possible so that people can understand the "how" of
+the system. The goal here is to make this feel real for users without getting
+bogged down.
+-->
+
+#### Story 1: Scale a heavy queue consumer on-demand
+
+As the operator of a video processing pipeline, I would like to reduce costs. While video processing is CPU intensive, it is not a latency sensitive workload. Therefore I want
+my video processing workers to only be created if there is actually a video to be processed and terminated afterwards.
+
+### Notes/Constraints/Caveats (Optional)
+
+<!--
+What are the caveats to the proposal?
+What are some important details that didn't come across above?
+Go in to as much detail as necessary here.
+This might be a good place to talk about core concepts and how they relate.
+-->
+
+Currently disabling HPA is possible by manually setting the scaled resource to `replicas: 0`. This works as the HPA itself could never reach this state itself.
+As `replicas: 0` is now a possible state when using `minReplicas: 0` it can no longer be used to differentiate between manually disabled or automatically scaled to zero.
+
+Additionally the `replicas: 0` state is problematic as updating a HPA object `minReplicas` from `0` to `1` has different behavior. If `replicas` was `0` during the update, HPA
+will be disabled for the resource, if it was `> 0`, HPA will continue with the new `minReplicas` value.
+
+To resolve these issues the KEP is introducing an explicit `ScaledToZero` condition inside the `HorizontalPodAutoscalerStatus`. When `ScaledToZero=True` was recorded the HPA will scale
+up a workload from `0 ~> 1` and remove the condition `ScaledToZero=True`. If the condition is not found, the HPA maintains the current behavior of performing no change.
+
+When the HPA scales a workload from `1 ~> 0`, it records the `ScaledToZero=True` condition inside the status.
+
+### Risks and Mitigations
+
+<!--
+What are the risks of this proposal, and how do we mitigate? Think broadly.
+For example, consider both security and how this will impact the larger
+Kubernetes ecosystem.
+
+How will security be reviewed, and by whom?
+
+How will UX be reviewed, and by whom?
+
+Consider including folks who also work outside the SIG or subproject.
+-->
+
+As `ScaledToZero` is no explicit property, applying a new Deployment with `replicas: 0` and HPA `minReplicas: 0` can be confusing as the Deployment will never scale.
+
+This needs should be documented and is detectable by looking at the existing `ScalingActive` condition.
+
+In the future pausing the HPA can become an explicit feature and the implicit pausing via `replicas: 0` can be deprecate to remove this confusing.
+
+## Design Details
+
+<!--
+This section should contain enough information that the specifics of your
+change are understandable. This may include API specs (though not always
+required) or even code snippets. If there's any ambiguity about HOW your
+proposal will be implemented, this is the place to discuss them.
+-->
+
+Add `ScaledToZero` as HPA `HorizontalPodAutoscalerConditionType`
+
+```golang
+const (
+ // ScaledToZero indicates that the HPA controller scaled the workload to zero.
+ ScaledToZero HorizontalPodAutoscalerConditionType = "ScaledToZero"
+)
+```
+
+### Test Plan
+
+<!--
+**Note:** *Not required until targeted at a release.*
+
+Consider the following in developing a test plan for this enhancement:
+- Will there be e2e and integration tests, in addition to unit tests?
+- How will it be tested in isolation vs with other components?
+
+No need to outline all of the test cases, just the general strategy. Anything
+that would count as tricky in the implementation, and anything particularly
+challenging to test, should be called out.
+
+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
+-->
+
+[x] 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.
+
+Most logic related to this KEP is contained in the HPA controller so the testing of
+the various `minReplicas`, `replicas` and `ScaledToZero` should be achievable with unit tests.
+
+Additionally integration tests should be added for enable scale to zero by, setting
+ `ScaledToZero: true`, setting `minReplicas: 1` and waiting for `replicas` to become `0` and another test for increasing `minReplicas: 1` and observing that `replicas` became `1` again and confirming that `ScaledToZero: true` has been removed.
+
+##### 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.
+-->
+
+- `/pkg/controller/podautoscaler`: `2025-02-06` - `96.4`
+
+##### 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
+-->
+
+- N/A in this case.
+
+##### 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.
+-->
+
+E2E tests will be added under <https://github.com/kubernetes/kubernetes/tree/master/test/e2e/autoscaling> to test scale down to `0` and scale up
+with this feature enabled and scale down `1` without this feature.
+
+- <test>: <link to test coverage>
+
+### 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
+
+- Implement the `ScaleToZero` condition recording
+- Ensure that all `minReplicas` state transitions from `0` to `1` are working as expected
+- E2E tests are passing without flakiness
+
+#### Beta
+
+- Allowing time for feedback
+
+#### GA
+
+- Allowing time for feedback
+
+### Upgrade / Downgrade Strategy
+
+<!--
+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?
+-->
+
+As this KEP changes the allowed values for `minReplicas`, special care is required for the downgrade case to not prevent any kind of updates for HPA objects using `minReplicas: 0`. The alpha code already accepts `minReplicas: 0` with the flag enabled or disabled since Kubernetes version 1.16 downgrades to any version >= 1.16 aren't an issue.
+
+Before downgrading all HPAs need to be set to `minReplicas: 1` to avoid any deployments being stuck at `replicas: 1`.
+
+### 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
+  in the kubelet? How does an n-2 kubelet 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?
+
+<!--
+Pick one of these and delete the rest.
+
+Documentation is available on [feature gate lifecycle] and expectations, as
+well as the [existing list] of feature gates.
+
+[feature gate lifecycle]: https://git.k8s.io/community/contributors/devel/sig-architecture/feature-gates.md
+[existing list]: https://kubernetes.io/docs/reference/command-line-tools-reference/feature-gates/
+-->
+
+- [x] Feature gate (also fill in values in `kep.yaml`)
+  - Feature gate name: `HPAScaleToZero`
+  - Components depending on the feature gate: `kube-apiserver`
+- [x] Other
+  - Describe the mechanism:
+
+    When HPAScaleToZero feature gate is enabled HPA supports scaling to zero pods based on object or external metrics. HPA remains active as long as at least one metric value available.
+
+  - Will enabling / disabling the feature require downtime of the control
+    plane?
+
+    No
+
+  - Will enabling / disabling the feature require downtime or reprovisioning
+    of a node?
+
+    No
+
+###### Does enabling the feature change any default behavior?
+
+<!--
+Any change of default behavior may be surprising to users or break existing
+automations, so be extremely careful here.
+-->
+
+  HPA creation/update with `minReplicas: 0` is no longer rejected.
+
+###### Can the feature be disabled once it has been enabled (i.e. can we roll back the enablement)?
+
+<!--
+Describe the consequences on existing workloads (e.g., if this is a runtime
+feature, can it break the existing applications?).
+
+Feature gates are typically disabled by setting the flag to `false` and
+restarting the component. No other changes should be necessary to disable the
+feature.
+
+NOTE: Also set `disable-supported` to `true` or `false` in `kep.yaml`.
+-->
+
+Yes. To downgrade the cluster to version that does not support scale-to-zero feature or to disable to feature gate:
+
+1. Make sure there are no hpa objects with minReplicas=0 and maxReplicas=0. Here is a oneliner to update it to 1:
+
+    `$ kubectl get hpa --all-namespaces  --no-headers=true | awk  '{if($6==0) printf "kubectl patch hpa/%s --namespace=%s -p \"{\\\"spec\\\":{\\\"minReplicas\\\":1,\\\"maxReplicas\\\":1}}\"\n", $2, $1 }' | sh`
+2. Disable `HPAScaleToZero` feature gate
+3. In case step 1. has been omitted, workloads might be stuck with `replicas: 0` and need to be manually scaled up to `replicas: 1` to re-enable autoscaling.
+
+###### What happens if we reenable the feature if it was previously rolled back?
+
+Nothing, the feature can be re-enabled without problems and workload with `replicas: 0` targeted by a HPA will be scaled again.
+
+###### Are there any tests for feature enablement/disablement?
+
+<!--
+The e2e framework does not currently support enabling or disabling feature
+gates. However, unit tests in each component dealing with managing data, created
+with and without the feature, are necessary. At the very least, think about
+conversion tests if API types are being modified.
+-->
+
+There currently unit tests for the alpha cases and tests planned to be added for the new functionality.
+
+### Rollout, Upgrade and Rollback Planning
+
+<!--
+This section must be completed when targeting beta to a release.
+-->
+
+As this is a new field every usage is opt-in. In case the kubernetes version is downgraded, currently scaled to 0 workloads might need to be manually scaled to 1 as the controller would treat them as
+paused otherwise.
+
+If a rollback is planned, the following steps should be performed before downgrading the kubernetes version:
+
+1. Make sure there are no hpa objects with minReplicas=0 and maxReplicas=0. Here is a oneliner to update it to 1:
+
+    `$ kubectl get hpa --all-namespaces  --no-headers=true | awk  '{if($6==0) printf "kubectl patch hpa/%s --namespace=%s -p \"{\\\"spec\\\":{\\\"minReplicas\\\":1,\\\"maxReplicas\\\":1}}\"\n", $2, $1 }' | sh`
+2. Disable `HPAScaleToZero` feature gate
+3. Downgrade the Kubernetes version
+
+###### 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.
+-->
+
+There are no expected side-effects when the rollout fails as the new `ScaleToZero` condition should only be enabled once the version upgraded completed.
+
+If the `kube-apiserver` has been upgraded before the `kube-controller-manager`, an HPA object has been updated to `minReplicas: 0` and the workload is already scaled down to 0 replicas, you must manually scale the workload to at least one replica.
+
+You can detect this situation in one of two ways:
+
+- Manually, by checking the HPA status and verifying that all entries show ScalingActive set to true and do not mention ScalingDisabled, or
+
+- Automatically, by using the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics)
+ to ensure the `ScalingActive` condition is `true.`
+
+If an rollback is attempted, all HPAs should be updated to `minReplicas: 1` as otherwise HPA for deployments with zero replicas will be disabled until
+replicas have been raised explicitly to at least `1`.
+
+###### 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?
+-->
+
+If workloads an unexpected number of HPA entities contain a the status `ScalingActive` `false` and mention `ScalingDisable` the feature isn't working as desired and all HPA objects should be updated to > 0 again and their managed workloads should be scaled to at least 1.
+
+This condition can also be detected using the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics), but reason should be manually confirmed for flagged HPA objects.
+
+###### 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.
+-->
+
+No yet as no implementation based on the new condition is available.
+
+###### 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.
+-->
+
+No.
+
+### 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.
+-->
+
+The new status will be visible inside the `kube_horizontalpodautoscaler_status_condition` metric provided by [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) as
+and the `minReplicas: 0` setting reflected in `kube_horizontalpodautoscaler_spec_min_replicas`.
+
+###### 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.
+-->
+
+When this feature is enabled for a workload scaled based on an object or external metric, the workload should be scaled to 0 replicas when the metric is 0.
+
+###### 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.
+-->
+
+No changes to the autoscaling SLOs.
+
+###### 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.
+-->
+
+No changes to the autoscaling SLIs.
+
+###### 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.).
+-->
+
+No, in regards to this KEP.
+
+### 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:
+-->
+
+The addition has the same dependencies as the current autoscaling controller.
+
+### 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.)
+-->
+
+No, the amount of autoscaling related API calls will remain unchanged. No other components are affected.
+
+###### 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)
+-->
+
+No, this only modifies the existing API types.
+
+###### Will enabling / using this feature result in any new calls to the cloud provider?
+
+<!--
+Describe them, providing:
+  - Which API(s):
+  - Estimated increase:
+-->
+
+No, the amount of autoscaling related cloud provider calls will remain unchanged. No other components are affected.
+
+###### 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)
+-->
+
+Yes, one additional boolean field inside the `spec` of every `HorizontalPodAutoscaler` resource.
+
+###### 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
+-->
+
+No, the are no visible latency changes expected for existing autoscaling operations.
+
+###### 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
+-->
+
+No, the are no visible changes expected for existing autoscaling operations.
+
+###### 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?
+-->
+No.
+
+### 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?
+
+Autoscaling will not occur, this is the same as the current behaviour.
+
+###### 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.
+-->
+
+- Failed to fetch the relevant object or external metrics.
+  - Detection: `ScalingActive: false` condition with `FailedGetExternalMetric` or `FailedGetObjectMetric` reason.
+  - Mitigations: manually scale the resource.
+  - Diagnostics: Related errors should be printed as the messages of `ScalingActive: false`.
+  - Testing: <https://github.com/kubernetes/kubernetes/blob/0e3818e02760afa8ed0bea74c6973f605ca4683c/pkg/controller/podautoscaler/replica_calculator_test.go#L451>
+
+###### What steps should be taken if SLOs are not being met to determine the problem?
+
+Check `metric_computation_duration_seconds` to see which metric encountered the latency issue.
+If the latency problem is caused by metrics used for scaling to zero, you can remove those metrics again from your HPA(s).
+
+## 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
+-->
+
+- (2019/02/25) Original design doc: <https://github.com/kubernetes/kubernetes/issues/69687#issuecomment-467082733>
+- (2019/07/16) Alpha implementation (<https://github.com/kubernetes/kubernetes/pull/74526>) merged for Kubernetes 1.16
+
+## 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.
+-->
+
+Third-party solutions like [KEDA][] already support scaling to zero for various resource (e.g. [RabbitMQ Queues](https://keda.sh/docs/2.16/scalers/rabbitmq-queue/)). However, these solutions often introduce additional paradigms and complexity. Since Horizontal Pod Autoscaling is already a core feature of Kubernetes and supports scaling to one, adding native support for scaling to zero would be a valuable and low-complexity enhancement.
+
+[KEDA]: https://keda.sh/
+
+## 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/2021-scale-from-zero/kep.yaml b/keps/sig-autoscaling/2021-scale-from-zero/kep.yaml
new file mode 100644
index 00000000000..394666e9747
--- /dev/null
+++ b/keps/sig-autoscaling/2021-scale-from-zero/kep.yaml
@@ -0,0 +1,43 @@
+title: HPA supports scaling to/from zero pods for object/external metrics
+kep-number: 2021
+authors:
+  - "@johanneswuerbach"
+owning-sig: sig-autoscaling
+participating-sigs:
+status: implementable
+creation-date: "2020-09-26"
+reviewers:
+  - "@gjtempleton"
+  - "@adrianmoisey"
+approvers:
+  - "@gjtempleton"
+
+see-also:
+replaces:
+
+# 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: "v1.35"
+
+# The milestone at which this feature was, or is targeted to be, at each stage.
+milestone:
+  alpha: "v1.16"
+  beta: "x.y"
+  stable: "x.y"
+
+# 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: HPAScaleToZero
+    components:
+      - kube-apiserver
+disable-supported: true
+
+# The following PRR answers are required at beta release
+metrics:
+  - kube_horizontalpodautoscaler_spec_min_replicas
+  - kube_horizontalpodautoscaler_status_condition