How does Kedify handle cold starts?

Waiting Pages and request holding during scale‑from‑zero; configurable timeouts.

Do you support multi‑cluster / multi‑region scaling?

Yes, scale Deployments and long‑running Jobs across a fleet using DistributedScaledObject / DistributedScaledJob (weighted placement + auto‑rebalance). Read docs .

Which gateways/ingresses are supported?

Gateway API, Istio, NGINX/Ingress, OpenShift Routes and also in-cluster traffic (Service to Service)

Do you support queue/event scalers?

Yes, there are 70+ built‑in scalers (Kafka, Redis, RabbitMQ, SQS, etc.).

What about GPU inference?

Combine GPU metrics, LLM related signals, and requests driven autoscaling.

Kedify helps teams reduce
costs, improve reliability, and
scale across clusters and regions
without the operational burden.

From AI inference to spiky seasonal traffic, see where autoscaling delivers
real business impact.

Explore Use Cases

Reduce AI Workload Costs & Complexity

Docs / Guides:

OTel Scaler Guide (LLM)

Problem:

LLM inference / AI pipelines are GPU-heavy, bursty, and expensive to keep warm.

Kedify solution:

GPU-aware autoscaling and OTel-based signals scale on real usage (RPS, concurrency, custom metrics), then scale down, including to zero when appropriate.

How it works (example signals):

HTTP/OTel for request rate, concurrency, or token throughput
PRP (vertical right-sizing) to shrink warm pods when idle (alternative to replica=0)

Migrate from AWS Lambda, Azure Functions, or Google Cloud Run

Docs / Guides:

Problem:

Fragmented serverless + K8s leads to complexity, limited visibility, and cold-start trade-offs.

Kedify solution:

Bring serverless-style, HTTP-triggered autoscaling to Kubernetes with scale-to-zero support, unified observability, and stronger security controls.

Scale-to-Zero Developer & Preview Environments

Docs / Guides:

Problem:

Preview and developer environments often run 24/7 and waste spend.

Kedify solution:

HTTP scaler plus autowiring and waiting pages hold traffic safely during cold starts and scale environments back down once idle.

Handle Spiky & Seasonal Traffic

Docs / Guides:

Problem:

Launches, flash sales, closes/rollovers, or viral spikes can cause over-provisioning or outages.

Kedify solution:

Real-time HTTP scaler with burst-friendly behavior, backed by production-grade Envoy-based proxying. Combined with the Predictive scaler to forecast and scale ahead of expected traffic spikes.

Multi-Cluster / Multi-Region Scaling

Docs / Guides:

Multi-Cluster Scaling docs

Problem:

Edge and multi-region workloads need capacity close to users, and outages should not require manual failover.

Kedify solution:

Scale Deployments and long-running Jobs across a fleet with weighted placement and automatic rebalancing when a cluster becomes unreachable.

How it works:

DistributedScaledObject for Deployments
DistributedScaledJob for long-running Jobs
Per-cluster weights and rebalancing policies

Dynamic Batch Processing

Docs / Guides:

Problem:

Nightly ETL, log analysis, or periodic model training do not need constant compute.

Kedify solution:

Use ScaledJobs on event queues like Kafka, SQS, and Redis to spin up capacity just in time and return to zero after work completes.

Optimize Event-Driven Architectures

Docs / Guides:

Problem:

Queues spike unpredictably while consumers sit idle for hours.

Kedify solution:

Scale on queue depth and lag across Kafka, RabbitMQ, Pulsar, Redis, SQS, and more, with 70+ supported scalers.

Prevent Latency & Service Delays

Docs / Guides:

Problem:

Mission-critical APIs must stay responsive under any load, and cold starts can hurt user experience.

Kedify solution:

HTTP scaler bursts on live traffic while Waiting/Maintenance Pages protect UX during scale-from-zero or maintenance. The Predictive scaler anticipates demand to minimise cold starts.