Enterprise Kubernetes autoscaling for APIs, queues, and AI workloads, with right-sizing and spend reporting built in.
Kedify provides HTTP, event-driven, GPU, predictive, vertical, and multi-cluster autoscaling with enterprise security and support.
Cut cloud costs by 30–40%. Reclaim engineering time.
From the core maintainers of
Teams choose Kedify when autoscaling needs to reduce spend, remove tuning work, and stand up to enterprise review.
Cut costs fast Most teams reduce spend by 30–40% in their first few months
Get value immediately Install in minutes, integrate quickly, optimize instantly
Scale proactively Predict future demand and respond before spikes hit
Run with enterprise confidence SOC2 Type II certified and FIPS-compliant options
Buy flexibly Deploy through AWS, GCP, and Red Hat Marketplaces
Get expert support Backed by the core KEDA maintainers when autoscaling matters
Kedify helps teams move faster, scale smarter, and stay in control.
Customer proof
“We haven’t touched our scaling config in months, and our bills dropped.”
— Surag Mungekar, CISO, Rupert
Customer 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 Ops Leader, Tao Testing
Customer proof
“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
Customer proof
"Our custom feature request was implemented extremely quickly and made a real difference."
— Yi Hong, Member of Technical Staff, Amigo
Customer proof
“Our traffic spikes are unpredictable by nature. Kedify gave us the confidence to scale to zero without gambling on user experience.”
— Zackky Muhammad, Lead DevOps Engineer, Kitabisa
Kedify closes the loop between workload demand, resource recommendations, autoscaling action, and verified cost impact.
Inputs
Collect demand, pod pressure, scaling history, and capacity across clusters.
Telemetry intake
Kedify combines workload demand, pod pressure, scaling history, and fleet capacity so decisions are based on live behavior, not CPU alone.
Insights
Right-size CPU and memory requests with confidence before scaling acts.
Insights
Insights surfaces CPU and memory recommendations with confidence, explanations, and safe commands before resource changes are applied.
Actions
Autoscale APIs, queues, jobs, GPU inference, predictions, and pod resources.
Autoscaling action
Kedify acts on HTTP traffic, queue pressure, predictive demand, GPU workloads, and vertical pod resources from the same control loop.
Fleet
Coordinate policies across clusters with guardrails, weights, and failover.
Fleet control
Multi-cluster scaling uses advanced patterns to place work across member clusters with weights and failover.
FinOps
Tie saved pod-hours, node-hours, CPU, memory, and GPU to FinOps impact.
FinOps
FinOps estimates saved pod-hours, node-hours, CPU, memory, and GPU capacity against recent peaks so savings are visible and explainable.
"We haven't touched our scaling config in months, and our bills dropped."
– Surag Mungekar, CISO, Rupert
Powered by precision autoscaling, built-in observability, and robust financial reporting.
30-40%
reduction in compute cost
5-10 hrs/week
saved on infrastructure tuning
Fewer SLA hits
from faster scale-ups
1-2 quarters
typical payback window
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.
Use existing procurement paths, cloud commitments, and vendor approvals.
Purchase Kedify via AWS, GCP, or Red Hat Marketplaces to count towards cloud spend, bypass vendor delays, and accelerate buying.
Kedify handles uneven Kubernetes demand across request-driven APIs, queue workers, GPU inference, and multi-cluster platforms.
Autoscale HTTP/gRPC services, background jobs, and shared platform workloads without manual replica tuning.
Scale workers from queue depth, event rate, and backlog so capacity appears when work arrives and scales down when it clears.
Right-size and autoscale inference workloads from demand and GPU signals while protecting latency targets.
Turn scaling behavior into spend reporting, resource recommendations, and capacity guardrails across clusters.
"We justified Kedify through our AWS commit. No red tape, no hold-up."
— Brian Lee,
COO / CFO, NinjaCat
Kedify
Smarter autoscaling, no overprovisioning
DIY
Manual tuning, idle compute waste
Kedify
Automation, UI
DIY
YAML + toil
Kedify
SOC2, FIPS-compliant, SLA-backed
DIY
No security guarantees
Kedify
ROI-driven platform, finance visibility
DIY
Infrastructure as sunk cost
Book a free ROI consult or try a Proof of Concept in your environment.
