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.
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.
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.).
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.
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.
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 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.
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.