Dynamically adjust compute resources in real-time based on traffic, load, and custom metrics. Scale up to handle spikes, scale down to cut costs, and never worry about downtime again.
A closed-loop system that continuously monitors, evaluates, and adjusts your infrastructure without human intervention.
Collect real-time data from CPU, RAM, network I/O, request latency, and custom business metrics via our distributed agents.
AI engine analyzes traffic patterns, historical data, and defined thresholds to predict resource needs.
Instantly provision or deprovision instances across regions while maintaining service mesh connectivity.
Automatically scale down during low-traffic periods, enforce budget guardrails, and generate cost-efficiency reports.
Advanced scaling strategies tailored for microservices, serverless, and traditional architectures.
Define complex scaling rules using CPU, memory, queue depth, custom app metrics, or HTTP request rates.
ML-driven forecasts analyze historical patterns and scheduled events to provision resources before traffic spikes.
Set hard limits on max instances, budget caps, and cool-down periods to prevent runaway scaling costs.
Support for HPA (instance count), VPA (CPU/RAM allocation), and mixed scaling strategies per workload.
Coordinate scaling across global data centers to maintain low latency and handle regional outages seamlessly.
Define scaling policies in YAML/JSON or via Terraform providers. Version control, CI/CD ready.
Declarative scaling rules that integrate directly with your CI/CD pipeline. No vendor lock-in, fully compatible with Kubernetes HPA and custom CloudNexus agents.
From flash sales to ML inference bursts, auto-scaling adapts to your unique traffic patterns.
Instantly scale checkout services during Black Friday or limited drops. Scale down within minutes when traffic normalizes.
Handle unpredictable GPU workload bursts. Scale inference endpoints based on queue depth and latency thresholds.
Automatically provision compute for heavy data migration jobs when new enterprise customers sign up.
Dynamically allocate edge compute nodes based on regional traffic surges and cache miss rates.
CloudNexus scales up in under 2 seconds for standard workloads. With predictive scaling enabled, instances are pre-warmed before traffic arrives, effectively eliminating cold-start latency for stateless services.
Yes. You can configure absolute maximum replicas, daily/weekly budget caps, and enforce conservative scale-down policies. Our cost guardrails will pause scaling and alert your team before limits are breached.
Absolutely. We support native Kubernetes HPA/VPA integration, custom CloudNexus agents for VM fleets, and serverless functions. You can mix strategies across the same workload.
You only pay for the actual compute time used by provisioned instances. There are no extra fees for the auto-scaling engine itself. Billing is usage-based with per-second granularity.
Yes. Ingest custom metrics via Prometheus, OpenTelemetry, or our HTTP endpoint. You can scale based on database connection pools, message queue depth, or any business KPI.
Deploy your auto-scaling policy in under 5 minutes. Get $200 in free credits to test with production-like traffic.