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Where does Thoras store my data?

We have no access to your data. Because Thoras is self-hosted, all of your data stays with you and never leaves your environment. All data and metrics remain local to your cluster and are never transmitted beyond your environment’s perimeter.

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What type of data does Thoras use?

The Thoras platform uses historical numeric Kubernetes metrics to train its models and generate predictions. It collects container performance metrics and custom metrics via the Kubernetes API.

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How long does Thoras take to install?

Installation takes less than an hour and is done via a Helm chart.

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How does Thoras reside in a customer environment?

The platform is lightweight and runs on a per-cluster basis to manage target workloads using an AIScaleTarget custom resource. The AIScaleTarget is configured with just a few lines of YAML.

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Do I have to pick a model to get accurate forecasts?

No—Thoras handles all model training and tuning to ensure you receive the most accurate forecasts. It leverages a variety of model architectures, parameters, and features, and continuously scores itself to identify the optimal model for your workload patterns. Users can customize inputs such as prediction frequency and operating mode (e.g., cost-saving, balanced, etc.).

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Where do my forecasting models live?

As with all your data, all models reside within your cluster. Training, tuning, and prediction happen entirely within your environment. Models never leave your infrastructure.

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How often do forecasts get made?

You’re in control. We generally recommend a forecast cadence of 5 to 30 minutes. More variable workloads often benefit from a faster cadence.

Thoras automatically manages model training and fine-tuning as needed. It continuously monitors forecast quality and updates models whenever improvements are possible, so no user intervention is required.

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What if my forecast is wrong?

Thoras will NEVER leave your services under-provisioned. While even the most accurate forecasting models can’t anticipate events like an early marketing email or a DDoS attack, Thoras is built to handle these situations reliably. If your service suddenly experiences unexpected demand, Thoras overrides the forecast and scales in real time to meet current needs.

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How much data does Thoras need to produce accurate forecasts?

Most services will receive highly accurate forecasts within the first 48 hours.

In rare cases, more time may be needed. For example, if your service has a unique usage pattern that only occurs on Saturdays, Thoras would need to observe at least one Saturday to ‘warm up’ and learn that specific trend.

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What types of predictive scaling does Thoras provide? For what use cases is each type of scaling method recommended?

Thoras provides both predictive horizontal pod scaling and predictive pod right-sizing. Currently, only one type is supported per workload. Multi-dimensional scaling is planned for a future release.

Predictive horizontal pod autoscaling is recommended for workloads with spiky usage that require rapid scaling. Predictive pod right-sizing is ideal for workloads that prioritize efficient cluster bin packing and cost optimization.

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What types of metrics does Thoras support for vertical and horizontal scaling?

Thoras supports CPU, memory, and any custom metrics relevant to your workload.

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Does Thoras support integration with existing autoscalers?

Thoras integrates seamlessly with Cluster Autoscaler, Karpenter, HPA, and Keda.

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Does Thoras support integration with CI/CD?

Yes! Thoras is compatible with virtually all CI/CD tools and workflows, including ArgoCD and Blue/Green deployments. We can also add support for any unique or unsupported workflows upon request.