Keep KEDA, add the missing production paths
Kedify keeps KEDA at the center of the stack and adds the pieces teams usually need next: request-driven scaling, better cold-start handling, and tighter operator controls.
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Go beyond KEDA with Kedify
Keep your KEDA setup and add HTTP/gRPC autoscaling, predictive scaling, and scale-to-zero workflows without rebuilding your platform.
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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
KEDA in production
Use KEDA for event-driven scaling, then close the gaps around it.
KEDA is the right starting point for event-driven Kubernetes autoscaling, but it is rarely the whole production story. Teams usually still need faster request-driven loops, better scale-to-zero handling, and a clear answer for when HPA is enough versus when KEDA should take over.
This page is meant for that next layer. If you already run KEDA and want better coverage for HTTP workloads, predictive demand, or cross-cluster governance, Kedify extends the setup instead of asking you to replace it.
Autoscale from HTTP, gRPC, and real user traffic
Move beyond queue depth and scrape-based loops when workloads need fast request-driven scaling, graceful cold-start behavior, and scale to zero for APIs and frontends.
Explore HTTP autoscalingScale before predictable peaks arrive
Add predictive and proactive control loops for workloads that follow recurring traffic patterns and cannot wait for CPU-based signals to catch up.
Read about predictive scalingCoordinate scale-to-zero and multi-cluster operations
Use one autoscaling surface for cost control, cross-cluster rollouts, and KEDA-based workloads that need more than a single-cluster control plane.
Compare Kedify with other optionsWhat to validate before you expand a KEDA deployment
Start with intent, not tooling. If your workloads are driven by queues, streams, external APIs, or cloud services, KEDA is already a strong fit. The next validation step is whether you also need request-driven autoscaling, pre-warming for recurring peaks, or better fallback behavior when workloads are allowed to scale from zero.
For most platform teams, the practical question is not KEDA or Kedify. It is how to keep KEDA as the event-driven core while adding faster control loops and more operational guardrails where standard Kubernetes autoscaling falls short.
Related reading
- KEDA vs HPA
A direct comparison of event-driven KEDA workflows against standard Kubernetes HPA loops.
- HTTP autoscaling for real traffic
Why HTTP and gRPC workloads need request-aware scaling instead of relying on lagging resource metrics alone.
- Predictive autoscaling
How to scale ahead of repeatable traffic spikes instead of reacting after SLOs are already at risk.
- Prometheus autoscaling with KEDA
What breaks when teams bolt Prometheus onto HPA directly, and where KEDA simplifies the path.
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Estimate your potential savings in seconds with the Kedify ROI Calculator.
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