A platform team standardizing admission control across dozens of clusters keeps running into the same fork in the road: OPA Gatekeeper's Rego gives them one policy language they could theoretically reuse across Kubernetes, their API gateway, and their Terraform pipeline - but nobody on the team already knows Rego, and onboarding time keeps slipping. Kyverno would get policies shipped in YAML this week, but ties the team to a Kubernetes-only tool with no path to unifying policy anywhere else in the stack.
That's the real decision underneath OPA Gatekeeper vs Kyverno in 2026: both are mature, CNCF, Apache-2.0 admission controllers doing the same core job - validating and mutating resources at the API server - so the choice comes down to language, mutation maturity, and whether policy reuse beyond the cluster actually matters to your organization.
Both engines intercept the same Kubernetes admission-control flow. What differs is the policy language and the CRD model built around it.
OPA Gatekeeper: Built on Open Policy Agent, a general-
purpose policy engine. Policies written in
Rego via ConstraintTemplates and Constraints
CRDs. CNCF graduated project. Same Rego
policies can run outside Kubernetes too.
Kyverno: Purpose-built for Kubernetes specifically.
Policies are plain YAML Kubernetes resources
(ClusterPolicy) - no new language. CNCF
Incubating project. Mutation and generation
are first-class, GA capabilities.
Gatekeeper's bet is that a single, general-purpose policy language paying an upfront learning cost is worth it for organizations that want the same rules enforced across many different systems, not just Kubernetes. Kyverno's bet is that most platform teams' actual problem is Kubernetes admission control specifically, and forcing a new language onto that problem when plain YAML can express it just as well only slows adoption down.
Gatekeeper requires learning Rego, OPA's declarative policy language, expressed through two separate CRDs - ConstraintTemplates define the policy logic, Constraints instantiate it against specific resources. That two-CRD separation is powerful for reuse but adds a layer of indirection beginners have to internalize before writing their first real policy.
Kyverno policies are themselves Kubernetes resources written in familiar YAML, behaving like any other manifest a Kubernetes-native team already works with daily. For platform teams without existing Rego expertise, this is consistently the deciding factor - not because Rego is objectively bad, but because the onboarding cost of a new language is real and immediate, while cross-platform reuse is a benefit that only materializes if the organization actually builds policy for non-Kubernetes systems later.
apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
name: add-team-label
spec:
rules:
- name: inject-team-label
match:
resources:
kinds: ["Pod"]
mutate:
patchStrategicMerge:
metadata:
labels:
team: "+(request.object.metadata.labels.team || 'unassigned')"
Both engines technically support mutation in 2026, but the maturity gap is real. Kyverno's mutation is generally available, lives inside the same ClusterPolicy resource used for validation, and is used in nearly every production Kyverno deployment - the standard use case being auto-injecting team and owner labels onto every pod for cost attribution, even when a developer forgot to set them manually.
Gatekeeper's mutation requires separate Assign and AssignMetadata CRDs, has technically existed since Gatekeeper 3.10, but remains rarely used in production compared to Kyverno's integrated approach - the extra CRD indirection that makes Rego reusable elsewhere is the same indirection that makes ad-hoc mutation feel like more setup than it's worth for most teams.
On a Kubernetes 1.34 test cluster, both engines add latency to every CREATE and UPDATE call to the API server, but the difference is small at typical scale and dominated by network round-trip rather than policy-evaluation cost - both scale to 100+ policies before that latency becomes noticeable on developer-facing kubectl operations.
Controller memory footprint differs more meaningfully: Gatekeeper sits at roughly 270MB total across its controller and audit components, while Kyverno runs closer to 600MB total across its four controllers. On a small cluster, Kyverno's footprint feels comparatively heavy; on a typical production cluster, the difference becomes noise against everything else running.
Kyverno's PolicyReport CRDs are the standard the Kubernetes Policy Working Group adopted, and tooling like the Policy Reporter UI consumes them directly for dashboard visibility. Gatekeeper has its own status mechanism that isn't PolicyReport-compatible - the Gatekeeper team is working toward that compatibility, but it isn't GA as of 2026, which means Gatekeeper users lose out on tooling built around the community-standard reporting format.
Operationally, Kyverno's single installation and single CRD model - policies are valid Kubernetes resources visible directly through kubectl - fails in well-documented, predictable ways. Gatekeeper's split between ConstraintTemplates, Constraints, and a separately-profiled audit subsystem requires more thoughtful operational handling, and the controller can reach states needing manual intervention to recover. Neither is operationally bad, but Kyverno requires less specialized expertise to keep healthy day to day.
Language and Maturity:
| Factor |
OPA Gatekeeper |
Kyverno |
| Policy language |
Rego (ConstraintTemplates + Constraints) |
Plain YAML (ClusterPolicy) |
| CNCF maturity |
Graduated |
Incubating |
| Mutation support |
Via separate CRDs, rarely used in production |
GA, same resource as validation, widely used |
Operations and Fit:
| Factor |
Controller footprint |
Best fit |
| OPA Gatekeeper |
~270MB total |
Cross-platform policy reuse (APIs, Terraform, K8s) |
| Kyverno |
~600MB total (4 controllers) |
Kubernetes-only, fast onboarding, mutation-heavy |
Choose Kyverno if your team is Kubernetes-native, wants policies written as plain YAML with no new language to learn, and needs mutation or resource-generation capabilities as first-class features rather than an afterthought - it's the easier choice to keep healthy operationally for organizations standardizing on a single tool for Kubernetes admission policy specifically.
Choose OPA Gatekeeper if you need maximum policy expressiveness, already have Rego skills on the team, or want to reuse the same policy language across Kubernetes and non-Kubernetes systems like APIs or Terraform - that cross-platform reuse is Gatekeeper's clearest durable advantage, but only pays off if your organization genuinely plans to enforce policy beyond the cluster.
For Indian enterprises building out a platform engineering function from scratch, Kyverno's shorter onboarding curve and PolicyReport-compatible tooling ecosystem generally make it the lower-friction default for a first Kubernetes policy engine, with Gatekeeper worth revisiting specifically if a broader Rego-based governance strategy emerges later - the two aren't mutually exclusive to evaluate in sequence.
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