For the complete documentation index and AI-optimized content, see /llms.txt. All pages support markdown format via .md extension or Accept: text/markdown header.

Kedify Features

For the complete documentation index and AI-optimized content, see /llms.txt. All pages support markdown format via .md extension or Accept: text/markdown header.

Kedify features extend the upstream KEDA model with operational controls that are difficult to build and maintain in a large Kubernetes estate. These pages cover the horizontal control loop, tenancy, fleet operations, policy guardrails, vertical optimization, recommendations, and cost visibility.

What Kedify Features Add to KEDA

optimize cost and performance with in-place vertical scaling, not only replica changes
manage larger fleets with multi-cluster and multitenant control patterns
keep selected 1 ↔ N decisions inside a tenant-scoped KEDA/KPA control plane
review dashboard recommendations and estimated autoscaling savings
apply autoscaling guardrails consistently across teams, namespaces, and clusters
add workload lifecycle controls such as pausing, resuming, and resource-profile transitions

Feature Areas

Pod Resource Profile: reusable CPU and memory profiles for pods.
Pod Resource Autoscaler: automatic pod resource adjustments from demand.
Multi-Cluster Scaling: autoscaling patterns across clusters.
Multitenant KEDA: safer KEDA operations for shared environments.
Kedify Pod Autoscaler: sharded horizontal autoscaling that preserves the ScaledObject configuration model.
Insights: CPU and memory recommendations for workloads.
FinOps: estimated spend, peak capacity, and savings.
Scaling Policy: guardrails for consistent scaling behavior.
Scaling Groups: shared scaling policy for related workloads.
Scale Adapter: autoscale resources with an incomplete /scale subresource, such as Agones Fleets.
Resume Scaling Controller: pause and resume autoscaling configuration.

Recommended Starting Points

Compare Pod Resource Profiles and Pod Resource Autoscaler when horizontal scaling alone is not enough.
Review Multi-Cluster Scaling and Multitenant KEDA if you operate more than one team, namespace, or cluster.
Review Kedify Pod Autoscaler when the Kubernetes HPA controller is outside the isolation boundary you need.
Use Insights and FinOps when you want to connect utilization recommendations with estimated capacity and cost savings.
Use Scaling Policy and Scaling Groups if you need safer default behavior across multiple workloads.
Review Resume Scaling Controller when you need to pause autoscaling without removing the scaling configuration.