Kedify adds the enterprise layer to the KEDA foundation:
Built by the creators of
KEDA powers autoscaling for companies you know including Microsoft, FedEx, Grab,
Qonto, Alibaba Cloud, Red Hat and many more. Kedify gives these capabilities turnkey
to enterprises that don’t want to build and maintain it themselves.
Autoscaling comparison
Compare the capabilities that matter once autoscaling moves from a single workload to a production platform: signals, cost control, fleet governance, and enterprise readiness.
Scaling signals
Compare the metrics and traffic signals each option can use to make pod scaling decisions.
Cost and optimization
See which options help teams right-size workloads, prove savings, and reduce idle capacity.
Fleet and governance
Understand what changes when autoscaling becomes a shared platform capability.
Enterprise readiness
Compare the operational, procurement, security, and support layers around production autoscaling.
| Capability Scaling signals |  Enterprise KEDA plus right-sizing, FinOps, fleet controls, security, and support. | Open-source event-driven autoscaling. Your team owns production operations. |  Kubernetes-native replica scaling for CPU and memory driven workloads. |
|---|---|---|---|
| Resource-based replica scaling Horizontal scaling from CPU and memory metrics. | Included Runs on the Kubernetes HPA path and adds higher-level controls around it. | Included KEDA supports CPU and memory scaling through Kubernetes HPA behavior. | Native Built into Kubernetes for CPU and memory based pod scaling. |
| Event and external metric scaling Scale from queues, streams, databases, cloud services, and custom metrics. | Supported Supported KEDA operations with enterprise dashboard, lifecycle, and support layers. | Core strength Open-source scaler ecosystem for event and external metric driven scaling. | Not native Needs an external metrics adapter such as KEDA. |
| HTTP, gRPC, and WebSockets Scale request-driven APIs and services from live traffic, including scale to zero. | Production-ready Envoy-backed HTTP scaler with autowiring, fallback, headers, waiting pages, and maintenance pages. | Community add-on HTTP add-on path with more operational ownership and narrower production coverage. | Not covered CPU may lag request pressure and does not provide request routing or scale to zero. |
| OpenTelemetry and custom metrics Use push-based application, Dapr, GPU, and model-serving signals without a Prometheus-only path. | Built in OTLP ingestion, PromQL-like queries, direct push, collector sidecars, and service discovery patterns. | DIY path Possible through community scalers and metric adapters, with more integration work. | Adapter required Needs custom or external metrics plumbing. |
| GPU and AI inference signals Scale accelerator-heavy workloads from GPU and inference-specific metrics. | Turnkey patterns OTel and GPU-aware scaling patterns for model serving and accelerator utilization. | Possible Community scaler combinations can work, but teams own the telemetry and tuning. | Not covered CPU and memory are poor proxies for GPU queue and utilization pressure. |
Horizontal scaling from CPU and memory metrics.
Runs on the Kubernetes HPA path and adds higher-level controls around it.
KEDA supports CPU and memory scaling through Kubernetes HPA behavior.
Built into Kubernetes for CPU and memory based pod scaling.
Scale from queues, streams, databases, cloud services, and custom metrics.
Supported KEDA operations with enterprise dashboard, lifecycle, and support layers.
Open-source scaler ecosystem for event and external metric driven scaling.
Needs an external metrics adapter such as KEDA.
Scale request-driven APIs and services from live traffic, including scale to zero.
Envoy-backed HTTP scaler with autowiring, fallback, headers, waiting pages, and maintenance pages.
HTTP add-on path with more operational ownership and narrower production coverage.
CPU may lag request pressure and does not provide request routing or scale to zero.
Use push-based application, Dapr, GPU, and model-serving signals without a Prometheus-only path.
OTLP ingestion, PromQL-like queries, direct push, collector sidecars, and service discovery patterns.
Possible through community scalers and metric adapters, with more integration work.
Needs custom or external metrics plumbing.
Scale accelerator-heavy workloads from GPU and inference-specific metrics.
OTel and GPU-aware scaling patterns for model serving and accelerator utilization.
Community scaler combinations can work, but teams own the telemetry and tuning.
CPU and memory are poor proxies for GPU queue and utilization pressure.
| Capability Cost and optimization | Enterprise KEDA plus right-sizing, FinOps, fleet controls, security, and support. | Open-source event-driven autoscaling. Your team owns production operations. | Kubernetes-native replica scaling for CPU and memory driven workloads. |
|---|---|---|---|
| Insights and right-sizing Review CPU and memory recommendations with confidence, explanations, and apply commands. | Included Namespace-scoped monitoring, recommendations, confidence, explanations, and dashboard or kubectl apply paths. | Not covered KEDA scales replicas. It does not produce resource recommendations. | Not covered HPA reacts to utilization but does not recommend requests or limits. |
| FinOps savings reporting Estimate current spend, recent peak, saved capacity, and savings by pods, nodes, CPU, memory, and GPU. | Included Organization and cluster views for estimated spend, saved compute-hours, peak drivers, and pricing coverage. | Not covered Autoscaling events are available, but cost evidence is left to external tooling. | Not covered No autoscaling savings or capacity impact reporting. |
| Predictive autoscaling Pre-scale before recurring demand, launches, daily peaks, or seasonal traffic arrives. | Included MetricPredictor models from KEDA, OTLP, CSV, or combined metric sources. | DIY Requires external forecasting logic and metric integration. | Reactive only Scales after measured resource pressure appears. |
| Vertical scaling with PRP and PRA Resize pod CPU and memory in place when replica count is not the only lever. | Included Pod Resource Profiles for lifecycle transitions and Pod Resource Autoscaler for utilization bands. | Not covered KEDA changes replica counts, not pod resource shape. | Not covered HPA is horizontal only. |
| Scale to zero or shrink idle pods Reduce idle API and worker cost while keeping controlled wake-up behavior. | HTTP and events Scale HTTP services to zero or shrink the last replica with Pod Resource Profiles. | Events Event triggers can scale consumers down and back up. | No native zero HPA does not natively scale a workload to zero replicas. |
Review CPU and memory recommendations with confidence, explanations, and apply commands.
Namespace-scoped monitoring, recommendations, confidence, explanations, and dashboard or kubectl apply paths.
KEDA scales replicas. It does not produce resource recommendations.
HPA reacts to utilization but does not recommend requests or limits.
Estimate current spend, recent peak, saved capacity, and savings by pods, nodes, CPU, memory, and GPU.
Organization and cluster views for estimated spend, saved compute-hours, peak drivers, and pricing coverage.
Autoscaling events are available, but cost evidence is left to external tooling.
No autoscaling savings or capacity impact reporting.
Pre-scale before recurring demand, launches, daily peaks, or seasonal traffic arrives.
MetricPredictor models from KEDA, OTLP, CSV, or combined metric sources.
Requires external forecasting logic and metric integration.
Scales after measured resource pressure appears.
Resize pod CPU and memory in place when replica count is not the only lever.
Pod Resource Profiles for lifecycle transitions and Pod Resource Autoscaler for utilization bands.
KEDA changes replica counts, not pod resource shape.
HPA is horizontal only.
Reduce idle API and worker cost while keeping controlled wake-up behavior.
Scale HTTP services to zero or shrink the last replica with Pod Resource Profiles.
Event triggers can scale consumers down and back up.
HPA does not natively scale a workload to zero replicas.
| Capability Fleet and governance | Enterprise KEDA plus right-sizing, FinOps, fleet controls, security, and support. | Open-source event-driven autoscaling. Your team owns production operations. | Kubernetes-native replica scaling for CPU and memory driven workloads. |
|---|---|---|---|
| Multi-cluster scaling Scale deployments and jobs across member clusters with weights, scheduling, and failover behavior. | Included DistributedScaledObject and DistributedScaledJob place replicas or jobs across registered member clusters. | Single cluster KEDA scales resources in the cluster where it runs. | Single cluster Works inside one Kubernetes cluster. |
| Multitenant KEDA Run isolated KEDA operators per tenant, namespace, or business unit with shared metric routing. | Included Tenant-scoped operators, shared metrics adapter, mTLS isolation, and automatic routing. | Not covered Upstream KEDA does not provide tenant-scoped operators or tenant-aware metric routing. | Not covered No tenant-aware external metric routing. |
| Scaling Groups Set shared max capacity across related ScaledObjects to protect constrained backends. | Included Label-selected groups cap aggregate scaling while preserving workload minimums. | Not covered Each ScaledObject scales independently. | Not covered No shared capacity model across workloads. |
| Scaling Policy Apply scheduled adjustments for polling interval, min/max replicas, idle replicas, and pause behavior. | Included Cron-based policy controller for ScaledObjects and ScaledJobs. | Manual Teams typically edit each ScaledObject or ScaledJob directly. | Manual Requires external automation or direct HPA changes. |
| Resume Scaling Controller Automatically resume paused autoscaling after a duration or at a specific time. | Included Adds pause reason and automatic resume workflows for maintenance windows. | Manual KEDA supports pause annotations, but resuming is operator owned. | Manual Pause and resume behavior needs external process. |
Scale deployments and jobs across member clusters with weights, scheduling, and failover behavior.
DistributedScaledObject and DistributedScaledJob place replicas or jobs across registered member clusters.
KEDA scales resources in the cluster where it runs.
Works inside one Kubernetes cluster.
Run isolated KEDA operators per tenant, namespace, or business unit with shared metric routing.
Tenant-scoped operators, shared metrics adapter, mTLS isolation, and automatic routing.
Upstream KEDA does not provide tenant-scoped operators or tenant-aware metric routing.
No tenant-aware external metric routing.
Set shared max capacity across related ScaledObjects to protect constrained backends.
Label-selected groups cap aggregate scaling while preserving workload minimums.
Each ScaledObject scales independently.
No shared capacity model across workloads.
Apply scheduled adjustments for polling interval, min/max replicas, idle replicas, and pause behavior.
Cron-based policy controller for ScaledObjects and ScaledJobs.
Teams typically edit each ScaledObject or ScaledJob directly.
Requires external automation or direct HPA changes.
Automatically resume paused autoscaling after a duration or at a specific time.
Adds pause reason and automatic resume workflows for maintenance windows.
KEDA supports pause annotations, but resuming is operator owned.
Pause and resume behavior needs external process.
| Capability Enterprise readiness | Enterprise KEDA plus right-sizing, FinOps, fleet controls, security, and support. | Open-source event-driven autoscaling. Your team owns production operations. | Kubernetes-native replica scaling for CPU and memory driven workloads. |
|---|---|---|---|
| Dashboard and guided operations Give platform, SRE, and FinOps teams one place to review scaling, recommendations, and savings. | Included Dashboard for clusters, workloads, recommendations, FinOps views, and configuration workflows. | CLI and YAML Community project primarily operated through Kubernetes resources and your own dashboards. | CLI and YAML Native Kubernetes objects and metrics, no business dashboard. |
| Secure builds, SOC 2, and FIPS Use hardened autoscaling components in regulated or security-sensitive environments. | Included SOC 2 Type II, hardened image variants, FIPS evidence, signed artifacts, and enterprise controls. | Community images You own hardening, attestations, compliance evidence, and patch governance. | Cluster-owned Covered by your Kubernetes distribution and platform security program. |
| Marketplace procurement Buy through cloud marketplaces and align autoscaling spend with committed cloud budgets. | AWS, GCP, Red Hat Procure through marketplaces and count spend toward eligible cloud commitments. | Open source No commercial procurement path or enterprise subscription by itself. | Platform included Included with Kubernetes; no separate buying path. |
| Enterprise support and SLA Get accountable help for production autoscaling incidents, migrations, and tuning. | Included Support from engineers building and operating Kedify and KEDA-based autoscaling platforms. | Community Community support and your internal Kubernetes expertise. | Vendor dependent Support depends on your Kubernetes platform provider. |
Give platform, SRE, and FinOps teams one place to review scaling, recommendations, and savings.
Dashboard for clusters, workloads, recommendations, FinOps views, and configuration workflows.
Community project primarily operated through Kubernetes resources and your own dashboards.
Native Kubernetes objects and metrics, no business dashboard.
Use hardened autoscaling components in regulated or security-sensitive environments.
SOC 2 Type II, hardened image variants, FIPS evidence, signed artifacts, and enterprise controls.
You own hardening, attestations, compliance evidence, and patch governance.
Covered by your Kubernetes distribution and platform security program.
Buy through cloud marketplaces and align autoscaling spend with committed cloud budgets.
Procure through marketplaces and count spend toward eligible cloud commitments.
No commercial procurement path or enterprise subscription by itself.
Included with Kubernetes; no separate buying path.
Get accountable help for production autoscaling incidents, migrations, and tuning.
Support from engineers building and operating Kedify and KEDA-based autoscaling platforms.
Community support and your internal Kubernetes expertise.
Support depends on your Kubernetes platform provider.
Tip for buyers: Karpenter handles nodes. HPA, KEDA, and Kedify handle pods and workloads. Most mature platforms use both layers together.
Where each option fits
HPA, KEDA, Kedify, and Karpenter are not interchangeable. Mature platforms usually combine node autoscaling with workload autoscaling, then add governance and cost proof when the estate grows.
Enterprise autoscaling platform
Use Kedify when autoscaling has become a platform capability, not a single YAML object.
Use when
Best for teams that want KEDA power without building the enterprise layer themselves.
Open-source event-driven scaling
Use KEDA when you need event and external metrics for pods and your team is ready to operate it directly.
Use when
You outgrow KEDA alone when autoscaling needs governance, cost evidence, or enterprise support.
Kubernetes-native replica scaling
Use HPA when CPU or memory are reliable enough signals and the workload is operationally simple.
Use when
You outgrow HPA when CPU reacts too late or the signal lives outside Kubernetes resource metrics.
Node autoscaling companion
Use Karpenter with HPA, KEDA, or Kedify. It optimizes node capacity while workload autoscalers manage pods.
Use when
Karpenter does not replace workload autoscaling. Pair it with Kedify for the strongest pod-plus-node cost loop.
Customer outcomes
The same autoscaling, right-sizing, and cost visibility capabilities are already reducing operational work and infrastructure waste in production Kubernetes estates.
200x
traffic burst handled
“Before Kedify, scaling up was a constant challenge. Now, our platform adapts instantly to our users’ needs, and we’ve freed up our team to focus on new features rather than managing resource spikes.”
- Rafael Tovar, Cloud Operations Leader, Tao Testing
With Kedify, Tao Testing handled a 200× traffic burst with zero downtime and ~40% lower spend.
150-200
preview environments
“With Kedify, our developers get the best of both worlds, cost-efficient scaling like Google Cloud Run, but fully integrated within our Kubernetes-based platform.”
- Jakub Sacha, SRE, Trivago
Trivago migrated 150–200 preview environments from Cloud Run to Kubernetes while keeping scale to zero efficiency.
With Kedify you get
Kedify keeps KEDA at the center of event-driven scaling, then adds the product, governance, and business evidence platform teams need in production.
Right-sizing Insights
Review CPU and memory recommendations with confidence, explanations, and a dashboard or kubectl apply path.
FinOps
Track estimated spend, recent peaks, saved capacity, and savings by pods, nodes, CPU, memory, and GPU.
Request scaling
Autoscale HTTP, gRPC, and WebSocket services with scale to zero, autowiring, fallback, and waiting pages.
AI and custom signals
Scale from OTLP push, collector sidecars, custom app metrics, Dapr, GPU, and model-serving pressure.
Forecasting
Train predictive models from KEDA, OTLP, CSV, or combined metric sources so replicas are ready earlier.
Vertical scaling
Use Pod Resource Profiles and Pod Resource Autoscaler to change pod CPU and memory in place.
Fleet scaling
Distribute deployments and jobs across member clusters with weights, scheduling, and failover behavior.
Tenant and policy controls
Add multitenant KEDA, Scaling Groups, Scaling Policy, and automatic resume workflows around KEDA resources.
Enterprise readiness
Use a dashboard, hardened images, FIPS evidence, SOC 2 controls, marketplace procurement, and enterprise support.
