Pull a model, run a forecast. No cloud API, no setup.
curl -fsSL https://www.routeframe.com/install.sh | bash
curl -fsSL https://www.routeframe.com/install.sh | bash
routeframe pull toto
routeframe run toto --input "45,48,52,49,55" --horizon 8
Routeframe puts foundation model forecasting directly in your workflow - reduce infrastructure costs with proactive scaling, catch anomalies before they become incidents, predict demand before it spikes, and embed forecasting into any service through a local API.
Predict CPU, memory, and traffic weeks ahead. Provision capacity proactively and cut cloud costs by eliminating emergency over-provisioning. Teach the model about deploy days with --exogenous is_deploy to account for known spikes.
Routeframe forecasts what your metrics should look like. When reality diverges from the prediction, that's your signal. Pipe live metrics through routeframe monitor and catch anomalies in real time before they become incidents.
Forecast revenue, orders, API traffic, or inventory from a CSV with one command. Fine-tune on your own data to capture your business's seasonal patterns. Flag known events like holidays or launches with --exogenous is_holiday.
Run routeframe serve and call POST /api/forecast from Go, Java, Python, or any language. 4ms latency. The model stays warm between requests. No ML dependencies in your service, no round-trip to a cloud API.
Cloud ML APIs charge per prediction, add latency, and require sending your data off-network. Routeframe is a CLI that runs on your hardware - and because it exposes a local REST API, it can be used as a tool by any AI agent of your choice to fine-tune models like Toto on your data and reason over your time series forecasts.
Your metrics never leave your network. No API keys, no data processing agreements, no compliance reviews.
Run a million forecasts a day. It's your CPU. Cloud APIs charge $0.01-0.10 per call -- that adds up.
Local GPU inference is 100x faster than a round-trip to a cloud endpoint. Fast enough for real-time dashboards.
Air-gapped environments, edge deployments, planes. The model runs with no internet connection.
| Before | With Routeframe | |
|---|---|---|
| Get a forecast | Ask the data team, wait 2 weeks | Run one command, get it now |
| Add to your service | Deploy a Python ML service | POST to localhost:11435 |
| Train on your data | Set up PyTorch, write training loop | routeframe finetune --data csv |
| Handle known events | Manual adjustments, tribal knowledge | --exogenous holidays,deploys |
| Dependencies | Python, PyTorch, CUDA, 4+ GB | 15 MB binary, nothing else |
Foundation model trained on 2 trillion time series data points from real-world infrastructure, business, and IoT metrics. Handles seasonality, trends, and sudden shifts out of the box. Fine-tune it on your data to learn your specific patterns.
routeframe pull toto