TRAIN & DEPLOYON CLOUD GPUSFROM LOCAL PYTHON.
No Docker. No Kubernetes.
No cloud setup. No YAML.
Bring any model from HuggingFace or your own weights. Fleet picks the cheapest GPU that fits and bills by the second.
The gap between a model on your laptop and a model in production is all ops. Fleet is that piece.
Every team rebuilds the same plumbing to get a model onto a GPU and behind an API. Fleet is the abstraction that makes all of it disappear.
- ✗Write a Dockerfile
- ✗Provision GPU instances
- ✗Configure Kubernetes
- ✗Wire up cloud IAM & VPCs
- ✗Install CUDA & drivers
- ✗Build a job scheduler
- ✗Stand up a serving layer
- ✗Babysit it all at 3am
Weights in, endpoint out. Fleet hides the GPUs, containers, and schedulers behind plain Python objects.
No Docker, no Kubernetes, no cloud console. Nothing to provision, patch, or page you at night.
Stay in your editor. The same script that runs on your laptop runs on a fleet of cloud GPUs.
Trained model to live, streaming endpoint in one call, and you only pay for the seconds you use.
Not just a stack of boring tables
Mission control for your models. Kick off training runs, spin up deployments, and test live inference, all from one neon cockpit, no terminal required.
Monitor the stuff that matters
Loss, learning rate, validation, streamed live across thousands of steps. Toggle a trendline to see through the noise and catch a run going sideways before it costs you GPU-hours.
Live in the terminal? There's a CLI. Building agents? The docs read themselves.
Dashboard, SDK, CLI, or autonomous agent: Fleet is the same platform from every angle, built to be driven by people and machines alike.
Every page is served as clean Markdown an LLM can ingest. No scraping, no HTML soup.
One-click "copy for LLM" on every doc, plus an MCP endpoint so coding agents can call Fleet directly.
The CLI, the SDK, and the docs all speak the same shapes. What a human reads is what an agent runs.
Pay for what you actually use.
No seats. No subscriptions. No idle servers draining your balance. Pick the tier your model needs and pay by the second it runs.
Inference runs on serverless GPU infrastructure. It scales up with demand and back down to zero when idle, so you only pay while requests are being served.