AI-Powered Cost Forecasting

Finally see what Karpenter is actually doing

Karpenter doesn't truly optimize without efficient pod scaling. Thoras makes those decisions visible so you understand why nodes are spinning up, what’s driving cost, and where you’re leaving money on the table.

Thoras.ai Sugested Scaling
Min
1
Max
20
Current
15
Rec
4
Thoras.ai Sugested Scaling
11 pods
Thoras Forecaster predicts lower than averadge utilization over the next 15 minutes
HOW IT WORKS

Entirely Air-Gapped & Installs In 15 minutes

Data never leaves your cluster.

Full visibility into node decisions

Thoras surfaces every Karpenter provisioning event in context. See which pods triggered node creation, which instance types were selected, and whether those decisions aligned with your cost and performance goals.

Understand the “why” behind every node

Kaprenter’s logs are dense. Thoras translates provisioning decisions into clear explanations: why this instance type, why this availability zone, and what would have happened with different configurations.

Optimize Karpenter, don’t replace it

Thoras doesn’t compete with Karpenter—it makes Karpenter smarter. By predicting pod demand before it triggers provisioning, Thoras helps Karpenter make better decisions with more lead time.

Why it matters

The Karpenter visibility problem

Karpenter is fast. It provisions nodes in seconds based on pending pods. But that speed comes with a tradeoff: it’s a black box.

What you don’t see

Why Karpenter chose a c5.xlarge instead of an m5.large. Whether that spot instance was the cheapest option. Why nodes keep spinning up and down during your nightly batch jobhs.

What Thoras provides

Opportunities to consolidate workloads and complete visibility into Karpenter’s decision-making, plus predictive insights that help you configure Karpenter for optimal cost and performance without trial and error.

Trusted by Engineers That Can’t Afford Mistakes

Stop reacting. Start predicting.

See how Thoras can eliminate waste, prevent incidents, and give your team back the hours they spend manually tuning thresholds.