1
Before day 1
Kickoff & success criteria
Confirm the workload, SLOs, guardrails, access, and selected scaling path before the POC clock starts.
Thank you! We’ll be in touch to schedule a meeting.
If you prefer, you can select a meeting time below:
Kedify Autoscaling Proof of Concept
Validate Kedify on your GPU inference, HTTP/gRPC, or Kubernetes workloads in 14-30 days.
Prove faster, cheaper, more predictable scaling with installation, scaler setup, and results validation in your own environment.
Start your POC
What’s included in your POC
We prioritize your selected focus areas in the plan. If prerequisites are met, we validate GPU workloads, Kubernetes autoscaling, or HTTP/gRPC endpoints within the agreed scope.
Investment
$5K credited
Applied to the annual contract when you move forward.
Timeline
14-30 days
Kickoff before day 1, installation in days 1-2, scaler setup, and validation.
Scope
1 cluster, one scaling path
Selected scaler, telemetry, dashboard, and workload metrics needed to prove impact.
Outcome
Readout + rollout plan
Cost tracking, latency metrics, support, and recommended next steps.
How the 14-30 day POC runs
The kickoff happens before the POC starts. The first 1-2 days focus on installation, the next window sets up scalers, and the second half validates results.
Paid POC, credited on subscription
2
Days 1-2
Install & configure
Deploy Kedify, connect telemetry, verify dashboard access, and confirm the required workload signals are flowing.
3
Days 3-7 / 3-14
Scaler setup & baselines
Configure selected scalers, capture current cost and performance baselines, and tune the first policy set.
4
Second half
Validate results & roll forward
Compare cost, latency, utilization, and operational effort, then deliver the readout and rollout plan.
What You’ll Prove
The POC focuses on measurable autoscaling outcomes: cost, latency, utilization, and operational effort on a real workload.
GPU autoscaling
Lower idle GPU capacity
Validate GPU-aware, event-driven scaling for inference or fine-tuning while keeping P95 latency predictable.
Target: 30-40% lower GPU spend
Kubernetes autoscaling
Clusters that follow demand
Exercise predictive policies that scale Kubernetes clusters dynamically across cloud environments.
Evidence: cost and performance deltas
HTTP/gRPC endpoints
Traffic-aware application scaling
Scale services from request pressure and bursty traffic while preserving latency SLOs.
Evidence: latency and replica behavior
Built-in visibility
Operational proof, not a guess
Use Prometheus/OTel metrics, long-term storage, dashboards, and readouts to show the impact clearly.
Output: readout and rollout plan
Security, Procurement & Pricing
Who Benefits Day-to-Day
Ideal for teams running multi-cluster and GPU workloads who need predictable P95s and
lower spend. Typical team cloud spend is approximately $1M - $20M
annually.
Platform & DevOps teams
Ditch homegrown scripts and pager fatigue.
SREs
Fewer scaling incidents, clearer SLOs.
Developers
Preview environments on demand, zero wait time.
FinOps & finance
Saved pod-hours, node-hours, CPU, memory and GPU capacity turned into spend evidence.
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.
