Scale Before the Spike.
Meet Thoras, your infrastructure Al toolkit. Thoras leverages bleeding-edge Al to predict resource needs, prevent downtime, and optimize Kubernetes workloads — saving you money and keeping your systems running smoothly.
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Your Infrastructure, Predicted.
Thoras analyzes historical patterns to predict workload demand, so you can scale before traffic spikes, not after.
Scaling shouldn’t feel like gambling — modern autoscalers react too late, forcing you to overprovision compute or risk downtime. Thoras forecasts workload demand with precision using bleeding-edge ML modeling, so your infrastructure is always one step ahead.
Integrations
By integrating directly into your Kubernetes environment and observability stack, Thoras analyzes historical metrics to forecast demand and proactively adjust resources before spikes or failures bring everything crashing down.
Chaos Happens. Thoras is Ready.
From viral moments to global events, Thoras ensures your infrastructure scales before the surge — because 'winging it' isn’t a cloud strategy.
The Best Scaling Is the One You Don’t Notice.
Install in Minutes, Start Predicting Instantly
Deploy Thoras seamlessly into your Kubernetes cluster with a single Helm chart install —no external APls, no complex setup. Once installed, Thoras immediately starts analyzing real-time traffic patterns and historical workload data.
Connect Metrics, Define Scaling Targets
Thoras integrates with your existing observability stack to ingest key workload metrics like CPU, memory, and request rates. Define service targets that align with your SLIs and SLOs to ensure predictive scaling meets your reliability goals.
Thoras AI Scaling Takes Over
Thoras' adaptive Al models continuously predict traffic demand, detecting trends before they happen. It autonomously adjusts Kubernetes scaling policies — optimizing replica counts, provisioning exactly what's needed, and eliminating excess compute before it burns your budget.
Your Infrastructure, Always Optimized
Your environment now scales before the spike, not after. With higher utilization and fewer wasted resources, Thoras ensures you run lean while maintaining peak reliability. Your infrastructure is no longer reactive — it's intelligently self-optimizing.
If Kubernetes Is the Brawn, Thoras Is the Brain.
Thoras predicts demand before it happens, so you always have the right resources at the right time. Eliminate overprovisioning, slash cloud waste, and ensure reliability without manual tuning.
Al That Predicts, Not Reacts
Traditional autoscalers only respond after traffic spikes, forcing teams to waste compute just to stay safe. Thoras forecasts demand in advance, letting you scale before the spike — not after.
"Thoras also helps enterprises discover optimization opportunities within reliability to help save on cloud costs."
Observability Without the Bloat
Your monitoring stack is filled with redundant metrics, logs, and traces that drive up storage costs without improving visibility. Thoras identifies and removes low-value telemetry, keeping only what's truly useful.
“By leveraging AI, companies can streamline their data operations while increasing speed and accuracy in decision-making.”
Run Kubernetes Without Overhead
Instead of reacting to real-time spikes, Thoras predicts resource needs in advance. It guides Kubernetes to allocate just the right amount of infrastructure-reducing waste, minimizing costs, and improving reliability.
“We’re excited to support Nilo, Jen, and the Thoras team. As thesis-driven investors, we’ve been seeking the next generation of software that tackles major SRE and DevOps challenges. Thoras is achieving impressive results in a short amount of time and addressing critical cloud costs and uptime issues faced by companies today.”
Runs Inside Your Cluster, Not Ours
Thoras integrates natively with Kubernetes, fitting effortlessly into your workflows. Installation is straightforward, and the system is designed to support the shared goals of reliability and efficiency across your team.
“We want customers to have the best of both worlds. AI/ML allows us to reduce noisy metrics and under utilized compute—without sacrificing performance.”
