> ## Documentation Index > Fetch the complete documentation index at: https://docs.thoras.ai/llms.txt > Use this file to discover all available pages before exploring further. # Frequently Asked Questions ## 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. ## 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. ## How long does Thoras take to install? Installation takes less than an hour and is done via a Helm chart. ## How are cost, savings, and waste calculated? Thoras monitors various signals across your cluster to calculate cost, savings, and waste over time: * **Controller spec and pod resource requests**: compute resources such as CPUs, memory, GPUs, etc. allocated to your workloads * **Pod usage and utilization**: what's actually being consumed versus what's requested * **Node-level metrics**: instance types, node counts, and their associated pricing All of this data is stored as time-series metrics within your cluster, so Thoras can track your spend, waste, and savings over time as you optimize with the platform. **Important:** All cost calculations and metric storage happen entirely within your cluster, no data leaves your environment. ## 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. ## 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.). ## 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. ## 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. ## 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. ## 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. ## 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 (adjusting replica count) and predictive vertical pod rightsizing (adjusting CPU and memory requests). * **Predictive horizontal pod autoscaling** is recommended for workloads with variable traffic patterns that need to scale the number of replicas to handle demand spikes. * **Predictive vertical pod rightsizing** is ideal for workloads with more predictable resource patterns that benefit from optimized CPU and memory requests to improve cluster utilization and reduce costs. ## Can I use both vertical and horizontal modes together? **Recommendation mode (both directions):** You can have both vertical and horizontal in `recommendation` mode simultaneously. Thoras will provide suggestions for both, but these suggestions are mutually exclusive. For example, if Thoras suggests 1Gi memory (vertical) and 3 pods (horizontal), the workload can rightsize by either applying the vertical suggestion or the horizontal suggestion, but not both. **Autonomous mode (one direction only):** Only one scaling direction can be in `autonomous` mode at a time. When enabling autonomous mode, you choose whether Thoras should automatically apply vertical or horizontal scaling. Contact Thoras' support if you wish to discuss which direction is the best option for your workloads. See [Understanding Vertical and Horizontal Scaling Modes](/guides/configuring-asts#understanding-vertical-and-horizontal-scaling-modes) for additional details. ## What types of metrics does Thoras support for vertical and horizontal scaling? Thoras supports CPU, memory, and any custom metrics relevant to your workload. ## Does Thoras support integration with existing autoscalers? Yes. Thoras works alongside HPA, KEDA, Cluster Autoscaler, and Karpenter without replacing or modifying any of them. See [How Thoras integrates with existing autoscalers](./integrations) for a detailed breakdown of each integration. ## 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.