What’s
Autoscaling?
“Plain English” vs. “Engineer‑Speak”
Plain English
Autoscaling is a smart light‑switch for your computers. When a crowd shows up, the lights click on so nobody is left in the dark. When the room empties out, they click off to save energy and money.
Engineer Speak
Event‑driven horizontal + vertical autoscaling for Kubernetes. Sub‑second burst capacity, GPU‑aware rightsizing, and up to 40% resource savings, built by the creators of KEDA.
Plain English
Why it matters: Traffic can spike or dip any time.
- Too many servers = wasted cash; too few = site crashes
- Autoscaling adds or removes
power automatically, so everything stays fast and the bill stays low
Engineer Speak
Why it matters: Keeps P95 latency at 99.99% during spikes.
- Eliminates manual HPA tuning and cuts idle nodes by 30-40%
- Streams OpenTelemetry metrics for real‑time scale decisions across clusters
Plain English
Autoscaling is a smart light‑switch for your computers. When a crowd shows up, the lights click on so nobody is left in the dark. When the room empties out, they click off to save energy and money.
Why it matters: Traffic can spike or dip any time.
- Too many servers = wasted cash; too few = site crashes
- Autoscaling adds or removes
power automatically, so everything stays fast and the bill stays low
Engineer Speak
Event‑driven horizontal + vertical autoscaling for Kubernetes. Sub‑second burst capacity, GPU‑aware rightsizing, and up to 40% resource savings, built by the creators of KEDA.
Why it matters: Keeps P95 latency at 99.99% during spikes.
- Eliminates manual HPA tuning and cuts idle nodes by 30-40%
- Streams OpenTelemetry metrics for real‑time scale decisions across clusters
Learn more in “Plain English”
The 30-second definition
Autoscaling:
Automatic right‑sizing of compute power to match real‑time demand
Horizontal scaling:
Adds or removes extra copies of an app (pods, services)
Vertical scaling:
Gives an app more or less horsepower (CPU, memory) without changing the copy count
Why you should care
Without
Autoscaling
30–40% of nodes idle
Cold‑starts & missed SLAs
Manual HPA tuning
With
Autoscaling
Up to 40% lower spend
99.99% latency compliance
Zero config drift
How Autoscaling works
Traffic, queue depth, GPU utilisation, or business events.
Rules or ML decide how many replicas or how much CPU/RAM you need.
Kubernetes (or another platform) spins pods up or down.
Keeps checking so you never over‑ or under‑shoot.
Types of Autoscaling:
Kubernetes Autoscalers
Horizontal Pod Autoscaler (HPA)
Scales:
Replicas
Best for:
Steady workloads
Watch-outs:
Reactive → delays
Vertical Pod Autoscaler (VPA)
Scales:
CPU / RAM limits
Best for:
ML & batch jobs
Watch-outs:
Restarts pods
Event‑Driven (KEDA)
Scales:
Any metric or event
Best for: Spiky queues, GPU
Watch-outs:
Needs metric adapter
Cluster Autoscaler
Scales:
Nodes
Best for:
Infra‑level savings
Watch-outs:
Slowest to react
Autoscaling Strategy
Autoscaling
Type
Resource-
based
Custom-
metrics-based
Event-driven
HTTP-based
Common Pitfalls
Cold‑starts vs. warm‑starts
latency spikes when new pods
spin up.
Over‑provisioning “just in case.”
burning money on idle nodes.
Slow, reactive resource scaling
CPU/memory metrics lag behind real demand, causing delayed scaling and misaligned capacity.
Thundering herds & API rate limits –
bursts of workers hammer
downstream services.
Observability blind spots
scaling in the dark without live metrics.
Autoscaling & Kubernetes:
Why It’s Harder Than It Looks
1.
Scrape intervals & delays
Prometheus, DataDog, HPA checks could take eternity at scale
2.
Multi‑cluster coordination
Clusters scale independently unless you unify metrics
3.
GPU & AI workloads
GPU nodes are expensive; scaling them wrong burns cash fast
4.
Security & compliance
Hardened images and FIPS matter in regulated environments
This is exactly why we built Kedify
Real-Time vs. Delayed
Autoscaling Dynamics
Traditional autoscalers rely on CPU and memory metrics,
introducing delays that cause over- or under-provisioning.
In
contrast, event- and HTTP-driven autoscaling responds
instantly to traffic changes, ensuring tighter alignment
between
demand and available pods.
Trusted by Teams Managing $1M–$5M+ in Cloud Spend
“We haven’t touched our scaling config in months—and our bills dropped.”
– Surag Mungekar, CISO, Rupert
What could you save?
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