Choose the right horizontal control loop
Kedify uses Kubernetes HPA by default. KPA can replace the 1-to-N loop for selected ScaledObjects when a scalable or tenant-scoped control plane matters.
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The autoscaler for modern Kubernetes
Use one platform alongside HPA and Cluster Autoscaler to handle pod, HTTP, and event-driven scaling. Keep latency low, cut cost, and scale to zero when idle.
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Backed by the founder of GitLab, Kedify is the
autoscaling platform for modern infrastructure.
We help engineering and finance teams optimize costs, improve performance, and scale
effortlessly—across Kubernetes,
ML workloads, and event-driven systems.
In your demo, we’ll cover:
- How Kedify automates scaling for HTTP, gRPC,
queue, and inference workloads - A walkthrough of the Kedify platform and
observability dashboard - Your projected cost savings using our
ROI calculator - Live Q&A with our product or engineering leads
Search intent: Kubernetes autoscaler
A Kubernetes autoscaler is not one component. It is a stack of control loops.
When teams search for a Kubernetes autoscaler, they are often trying to map several layers at once: pod scaling with HPA or KPA, event-driven scaling with KEDA, node scaling with Cluster Autoscaler, and the operational glue needed to make those systems work well together. Kedify fits into that stack by improving workload-level autoscaling rather than replacing the rest of Kubernetes.
The practical decision is which autoscaler should react to which signal. Resource metrics, request rate, queue depth, Prometheus series, and forecasted demand should not all be forced through the same loop. Separating those paths is how teams reduce latency spikes, idle cost, and scaling noise.
Cover event-driven and Prometheus-based scaling
Queues, external systems, and application metrics often describe demand better than raw CPU. That is where KEDA-backed autoscaling becomes the practical next step.
Review Prometheus autoscalingHandle user-facing traffic and cold starts
For APIs and web workloads, request-aware autoscaling plus waiting and maintenance pages protect UX while still allowing aggressive idle cost reduction.
Read the scale-to-zero guideOperate across clusters and workload types
Platform teams rarely run one autoscaler for one service. Kedify helps standardize autoscaling policy across clusters, teams, and mixed workload classes.
Estimate the ROIWhere Kedify fits in the autoscaler stack
Think of Kedify as the layer that makes workload autoscaling more capable and more precise. HPA remains the default horizontal controller. KEDA extends Kubernetes toward external and event-driven signals. KPA can shard the 1 ↔ N loop and keep it tenant scoped, while Kedify adds request-aware paths, cold-start handling, and multi-cluster operations.
That model is especially useful if you already run a mix of synchronous APIs, background workers, and cost-sensitive workloads. It lets each workload follow the control loop it actually needs, instead of flattening everything into one generic autoscaler configuration.
Related reading
- KEDA vs HPA
Choose the right autoscaler based on signal source, workload type, and whether scale to zero matters.
- Prometheus and HPA do not line up cleanly
Why adapter-heavy Prometheus autoscaling often pushes teams toward KEDA-based workflows.
- Compare Kedify with other approaches
A practical comparison for teams deciding between DIY KEDA setups and a broader autoscaling platform.
- See the full autoscaling platform
How Kedify fits alongside HPA, Cluster Autoscaler, and specialized scaling loops in production.
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