# Thoras Documentation

## Docs

- [Compliance & Security](https://docs.thoras.ai/faq/compliance.md): Learn about Thoras security certifications and data protection practices
- [Integrations](https://docs.thoras.ai/faq/integrations.md): How Thoras works alongside KEDA, Cluster Autoscaler, and Karpenter
- [Frequently Asked Questions](https://docs.thoras.ai/faq/introduction.md)
- [Quickstart](https://docs.thoras.ai/getting-started/quickstart.md): Deploy Thoras and see Kubernetes resource recommendations in minutes!
- [What is Thoras?](https://docs.thoras.ai/getting-started/what-is-thoras.md)
- [ArgoCD](https://docs.thoras.ai/guides/argo-cd.md): ArgoCD is a declarative, GitOps continuous delivery tool for Kubernetes applications. This section provides considerations for using ArgoCD to manage Thoras-targeted workloads.
- [Configuring AIScaleTargets](https://docs.thoras.ai/guides/configuring-asts.md)
- [Cost Estimates](https://docs.thoras.ai/guides/cost-estimates.md): Understand how Thoras calculates potential savings and waste across your Kubernetes cluster based on workload resource usage and node pricing data.
- [Enabling Autonomous Mode](https://docs.thoras.ai/guides/enabling-autonomous-mode.md): Enable predictive scaling with confidence
- [Choosing a Forecast Optimization Strategy](https://docs.thoras.ai/guides/forecast-optimization-strategies.md)
- [Predictive Horizontal Pod Autoscaling](https://docs.thoras.ai/guides/hpa.md)
- [Interpreting Graphs](https://docs.thoras.ai/guides/interpreting-graphs.md)
- [JVM Workload Scaling](https://docs.thoras.ai/guides/jvm-scaling.md): How Thoras optimizes Java-based applications
- [Metrics](https://docs.thoras.ai/guides/metrics.md): Thoras exposes native Prometheus metrics that offer real-time visibility into the actions and health of the Thoras system. This empowers your teams with deeper insights, transparency, and observability.
- [Pausing Autonomous Scaling](https://docs.thoras.ai/guides/pause-scaling.md): Temporarily halt all autonomous scaling actions cluster-wide
- [Predictive Vertical Pod Rightsizing](https://docs.thoras.ai/guides/vertical-pod-rightsizing.md): Predictive Vertical Pod Rightsizing is the process of preemptively optimizing the CPU and memory resource requests and limits for Kubernetes pods to ensure they have just enough resources to run efficiently without over or under provisioning. Unlike horizontal scaling, which adjusts the number of po…
- [Advanced Setup](https://docs.thoras.ai/installation/advanced-setup.md)
- [System Requirements](https://docs.thoras.ai/installation/system-requirements.md)
- [AIScaleTarget](https://docs.thoras.ai/reference/ast-definition.md)
- [ClusterAIScaleTemplate](https://docs.thoras.ai/reference/cluster-aiscale-template.md): ClusterAIScaleTemplate is a cluster-scoped custom resource that automatically creates and manages AIScaleTargets for matching workloads across multiple namespaces. This feature eliminates the need to manually create individual AIScaleTargets for each workload, enabling fleet-wide scaling policies.
- [Forecasting](https://docs.thoras.ai/reference/forecasting.md)