Kubernetes autoscaling for cost, reliability, and scale

Problem:

CPU, memory, and GPU requests drift over time while warm replicas stay allocated long after demand changes.

Kedify solution:

Use Insights to review CPU and memory recommendations, FinOps views to quantify saved capacity, and vertical scaling with PRP/PRA when replica count is not the only lever.

What to review:

• Current vs. recommended CPU and memory requests
• Saved pod-hours, node-hours, CPU, memory, and GPU capacity
• Recommendation confidence and generated kubectl commands

Problem:

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

Kedify solution:

Scale from HTTP, OpenTelemetry, concurrency, token throughput, and custom GPU signals, then scale down or shrink pod resources when demand falls.

Example signals:

• Request rate and concurrency for APIs
• Token throughput and model-serving metrics
• PRP/PRA for warm-pod right-sizing when scale-to-zero is not appropriate

Problem:

Fragmented serverless and Kubernetes paths create cold-start trade-offs, weaker visibility, and duplicated operating models.

Kedify solution:

Bring serverless-style HTTP-triggered scaling into Kubernetes with scale-to-zero, waiting pages, unified telemetry, and stronger security controls.

Migration focus:

• Map function endpoints to Kubernetes Services or Ingress paths
• Use HTTP scaler request rate or concurrency as the first scaling signal
• Keep cold-start behavior visible with waiting pages and dashboard metrics

Problem:

Preview and developer environments often run all day even when nobody is using them.

Kedify solution:

HTTP scaler autowiring and waiting pages hold traffic during cold starts, then scale idle environments back down without custom routing logic.

What to configure:

• Scale-to-zero thresholds and cooldown windows
• Waiting or maintenance pages for first requests after idle periods
• Ingress, Gateway API, Istio, OpenShift Route, or Service traffic paths

Problem:

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

Kedify solution:

Use real-time HTTP scaling for live bursts and predictive scaling to pre-scale before recurring or scheduled demand arrives.

Signals to combine:

• Live request rate or concurrency from the HTTP scaler
• Historical traffic patterns for predictive scaling
• Timeouts and proxy tuning for peak-load behavior

Problem:

Edge and multi-region workloads need capacity close to users, and cluster incidents 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

Problem:

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

Kedify solution:

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

Job controls:

• Queue depth, lag, or backlog metrics for activation
• Max replica and concurrency limits for each job type
• Scale-down behavior after work drains

Problem:

Queues spike unpredictably while consumers sit idle for hours.

Kedify solution:

Scale on queue depth, lag, and external event sources across Kafka, RabbitMQ, Pulsar, Redis, SQS, and 70+ supported scalers.

Scaler inputs:

• Kafka lag, RabbitMQ queue depth, Redis length, SQS messages, or custom event metrics
• Activation thresholds for idle-to-active transitions
• Scaling Groups when several workers share one downstream limit

Problem:

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

Kedify solution:

HTTP scaler reacts to live traffic while waiting and maintenance pages protect UX during scale-from-zero. Predictive scaling anticipates repeatable demand.

Reliability guardrails:

• Request holding and timeout settings for scale-from-zero
• Predictive pre-scaling for known daily or seasonal peaks
• Proxy performance tuning for HTTP, gRPC, and long-lived traffic