Vector turns your trained model into a monitored, autoscaling endpoint. No YAML archaeology required.
One pipeline that treats deployment as a solved problem, so your team can stay in the model loop.
Point at a directory. Vector detects the framework, pins dependencies, and builds a reproducible image.
Scale to zero when idle, burst to fleet under load. You pay for compute, not for promises.
Token-level latency traces, drift alerts, and a log stream that reads like a flight recorder.
Canary a new checkpoint to 5% of traffic. One command back if the numbers disagree.
VPC peering, at-rest encryption, and no training on your traffic. Ever.
PyTorch, JAX, ONNX, GGUF. If it runs, Vector serves it.
The dashboard is a live instrument panel: traffic, latency percentiles, GPU saturation and cost — all on one hairline grid.
Explore the dashboard