Predictive Pod Rightsizing | Thoras AI

Stop Guessing Pod Sizes. Start Predicting.

Thoras uses machine learning to forecast what your pods actually need and adjusts CPU and memory before demand changes.

No restarts. No OOMs. No overprovisioning.

Thoras.ai Suggested Scaling

Results You Can Expect

30–70% reduction from CPU and memory rightsizing

Zero Restarts for right-sizing adjustments on supported workloads

Achieve ~85% Utilization while reducing node scheduling inefficiencies and memory pressure.

How It Works

Entirely Air-Gapped & Installs In 15 minutes

Data never leaves your cluster.

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Tailored to Your Needs

Thoras continuously analyzes CPU, memory, and request load across your services to understand when and how traffic actually spikes and ensure you have enough compute at the right time.

ML-driven Predictions

When demand is about to rise, we adjust resource requests before the spike, not after. When demand drops, we release the excess capacity you no longer need.

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Automatically Right-sized

No more manual sizing for max capacity. No threshold babysitting. Thoras keeps your pods fitted to actual demand autonomously, 24/7.

Why Predictive Right-Sizing Matters

Most platforms watch your pods, wait for waste, then tell you to fix it. That’s not optimization. That’s delayed notification.

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The VPA Problem

Kubernetes VPA restarts pods to apply new resource settings. For single-replica workloads or latency-sensitive services, that’s a non-starter. Not only that, but VPA doesn’t play well with HPA. You often have to choose one or the other.

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The Thoras Difference

Thoras predicts demand minutes or hours ahead of time (can be user-defined). Predictions coupled with real-time monitoring drive our autonomous resource scaling horizontally, vertically, or both while also leveraging in-place rightsizing to scale without the need for restarts.

Trusted by Engineers That Can’t Afford Mistakes

Ready to optimize your bursty workloads?

See how Thoras.ai can reduce costs while improving performance for your variable demand applications.