Traditional cloud providers add layers of virtualization debt. Vectis executes directly in Linux eBPF hooks and hardware-isolated Firecracker micro-VMs for raw bare-metal performance.
Zero-copy packet routing inside the Linux kernel. Eliminates Envoy sidecar proxies, dropping service-to-service roundtrip latency to sub-0.8ms.
On-demand NVIDIA H100 & A100 SXM5 GPUs with 900 GB/s NVLink. Warm up models like LLaMA-3 and Mistral in under 850ms.
Hardware-isolated Firecracker micro-VMs booting in 14.2ms. Pack 4,000 tenants per physical host with zero memory bleed.
Inspect how a request travels from Anycast edge ingress to serverless GPU inference in under 3.5 milliseconds.
32 US & Global PoPs route client to closest fiber node.
Kernel inspection, TLS 1.3 termination, and zero-trust ACL.
Isolated container runtime executes application code.
Sub-millisecond direct GPU memory DMA tensor inference.
Integrate Vectis into your existing build pipeline with two lines of code.
import vectis
# Initialize Vectis Global Mesh Client (US Core)
client = vectis.Client(api_key='vec_live_us_east_8821')
# Provision Serverless H100 GPU Cluster with 14ms Cold Start
cluster = client.gpu.allocate(
accelerator='nvidia-h100-sxm5',
region='us-east-1',
min_replicas=0,
max_replicas=64,
nvlink=True
)
# Stream Distributed Tensor Inference
response = cluster.infer(
model='meta-llama/Meta-Llama-3-70B-Instruct',
prompt='Analyze real-time order book telemetry',
max_tokens=512
)
print(response.output)Official clients maintained with zero external dependencies and native async support.
pip install vectis-cloud
npm i @vectis/sdk
go get vectis.dev/sdk
cargo add vectis