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Compare Kubernetes autoscaling options and see where Kedify fits

Kedify adds the enterprise layer to KEDA foundation:

Production‑ready HTTP/gRPC autoscaling
Predictive autoscaling
GPU/AI & LLM inference autoscaling
Vertical autoscaling
Multi-cluster dashboard, hardened KEDA builds (FIPS)

Built by the creators of

Who Already Uses The Technology

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.

Not sure what to choose?

Pick your workload and priority, and we’ll highlight the right
option in the table.

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Capability

What it scales

HTTP/gRPC/ WebSockets scaler

GPU/AI scalers

Vertical autoscaling

Predictive scaler

Multi-cluster autoscaling

Security & compliance

Multi-cluster dashboard

Scale-to-zero

Marketplace purchase

Support & SLA

Best for

Pods/workloads (event & HTTP) Unifies event metrics with request load—scale
consumers and APIs from the same brain.

Production‑ready (Envoy‑backed; scale‑to‑zero; fallback; header routing; maintenance/wait pages) Keeps p95 steady under bursts; handles cold
starts and long‑lived streams safely.

GPU‑driven autoscaling patterns; push‑based OTel metrics Scales on GPU‑aware signals and push telemetry—cuts
idle accelerator cost and scrape lag.

Pod Resource Profiles (boost at warm‑up; dial down after) Right‑sizes pods without restarts; spike for warm‑up, settle once stable.

Predict future load and proactively scale Uses advanced time series models trained on your
actual application data to predict future load patterns.

Scale and schedule across multiple Kubernetes clusters Centralized multi-cluster scaling and scheduling
for global applications and fleets.

Yes (one pane across EKS/GKE/AKS/ on-prem) Fleet‑wide view across clouds; compare
SLOs, spend, and signals in one UI.

Hardened KEDA builds; FIPS; SSO/SAML Hardened images and enterprise auth for audits and centralized access.

Yes (HTTP/events) Sleep idle APIs/workers; wake on demand with the first request or event.

AWS/GCP/Red Hat (counts toward commits) Procure via cloud marketplaces; attribute spend to existing commitments.

Enterprise support, SLAs Vendor support with SLAs and defined response targets.

Latency-sensitive APIs; GPU/AI; multi-cluster; regulated envs Latency‑critical APIs, accelerator‑heavy workloads, governed fleets.

Pods via events/external metrics Turns external/event signals into HPA
actions—great for queues and streams.

Community HTTP add‑on (beta; interceptor SPOF; no gRPC/WebSockets; more complex CRD) Adds HTTP scaling via an interceptor; extra ops
overhead; not for bidirectional streams.

Use community scalers; less turnkey for GPU Community GPU scalers exist; expect more wiring/tuning per vendor.

—

Community images Standard community images—you own hardening and access controls.

Yes (events) Event triggers can revive consumers;
resume time depends on your setup.

—

Community support Community channels only; response is best‑effort.

Teams starting with event scaling Event‑driven teams minimizing complexity for queues/streams.

Pods via CPU/memory Built‑in CPU/memory scaling; simple when signals are resource‑bound.

—

No (native)

—

Simple steady workloads Apps with steady, predictable load and no external triggers.

Tip for buyers:
Karpenter is node autoscaling and pairs with Kedify (pod/workload autoscaling). Use both for best effect.

Capability

What it scales

HTTP/gRPC/ WebSockets scaler

GPU/AI scalers

Vertical autoscaling

Predictive scaler

Multi-cluster autoscaling

Multi-cluster dashboard

Security & compliance

Scale-to-zero

Marketplace purchase

Support & SLA

Best for

Pods/workloads (event & HTTP)

Production‑ready (Envoy‑backed; scale‑to‑zero; fallback; header routing; maintenance/wait pages)

GPU‑driven autoscaling patterns; push‑based OTel metrics

Pod Resource Profiles (boost at warm‑up; dial down after)

Predict future load and proactively scale

Scale and schedule across multiple Kubernetes clusters

Yes (one pane across EKS/GKE/AKS/on-prem)

Hardened KEDA builds; FIPS; SSO/SAML

Yes (HTTP/events)

AWS/GCP/Red Hat (counts toward commits)

Enterprise support, SLAs

Latency-sensitive APIs; GPU/AI; multi-cluster; regulated envs

Pods via events/external metrics

Community HTTP add‑on (beta; interceptor SPOF; no gRPC/WebSockets; more complex CRD)

Use community scalers; less turnkey for GPU

—

Community images

Yes (events)

—

Community support

Teams starting with event scaling

Pods via CPU/memory

—

No (native)

—

Simple steady workloads

Tip for buyers:
Karpenter is node autoscaling and pairs with KEDA/Kedify(pod/workload autoscaling). Use both for best effect.

Where each option fits

Pick by workload and stage. Use Karpenter for nodes, KEDA or
Kedify for pods, and HPA for CPU and memory.

Kedify: Enterprise layer on KEDA for APIs and AI. Adds HTTP/gRPC/predictive autoscaling, GPU/AI/LLM scalers, in-place vertical resize, and a multi-cluster dashboard with FIPS.

KEDA (community): Extends HPA with external metrics for event-driven scaling. Great for queues and streams when you’re early.

HPA (Horizontal Pod Autoscaler): Simple CPU and memory scaling in Kubernetes. Not event-aware without KEDA.

Karpenter: Optimizes nodes for capacity and packing. Complements KEDA/Kedify, which scale pods, and helps control fleet cost.

Real-World Proof

"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.

Read the case study

"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.

Read the case study

“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.

Read the case study

“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.

Read the case study

With Kedify you get

HTTP/gRPC autoscaling

Production‑ready, Envoy‑
backed, header‑routing, maintenance/wait pages, fallback.

Learn More

Predictive autoscaling

Proactively scale Kubernetes clusters before demand spikes occur, helps with cold starts and overall stability.

Learn More

GPU & AI autoscaling

NVIDIA/AMD/Intel use cases; push‑based metrics.

Learn More

In‑place vertical autoscaling

Resize workloads based on a profile. Dial up for warm‑up; dial down once stable).

Learn More

Is Kedify Right for Your Use Case?

Whether you’re cutting GPU costs, preparing for your next big launch, or modernizing serverless
workloads, Kedify has you covered. Book a live demo or explore the docs to see Kedify in action.

Compare Kubernetes autoscaling options and see where Kedify fits

Who Already Uses The Technology

Not sure what to choose?

Capability

Capability

Where each option fits

Real-World Proof

With Kedify you get

HTTP/gRPC autoscaling

Predictive autoscaling

GPU & AI autoscaling

In‑place vertical autoscaling

Is Kedify Right for Your Use Case?

Frequently Asked Questions