One-command deployment
Deploy any Python-based model with demo deploy. We handle containerization, GPU provisioning, and routing automatically.
Platform
Infrastructure that thinks like you do
Deploy models with code, not config. Scale automatically. Monitor everything. Focus on building AI, not managing servers.
Core capabilities
Intelligent autoscaling
Scale from zero to thousands of replicas based on real-time demand. Sub-second cold starts mean no warm pools required.
Multi-framework support
Run PyTorch, TensorFlow, JAX, ONNX, or custom frameworks. We support any model that runs on CUDA.
Global edge network
Deploy to 12 regions worldwide. Automatic request routing to the nearest available GPU for minimal latency.
Real-time observability
Track inference latency, GPU utilization, error rates, and costs in real time. Integrated tracing and logs.
Version management
Deploy multiple model versions simultaneously. Canary deployments, A/B testing, and instant rollbacks built in.
GPU Fleet
Access the latest hardware without capital expense
We maintain a diverse GPU fleet so you can choose the right hardware for each workload. Switch between GPU types with a single config change.
- NVIDIA H100 – 80GB HBM3 for large language models
- NVIDIA A100 – 80GB for training and fine-tuning
- NVIDIA L40S – Cost-effective inference at scale
- NVIDIA A10G – Budget-friendly for smaller models
How it works
- 1
Write your inference code
Create a Python function that loads your model and handles predictions. Use any framework or library.
- 2
Configure GPU requirements
Specify GPU type, memory, and concurrency in a simple config file. We handle the rest.
- 3
Deploy to production
Run demo deploy to build, package, and launch your model on serverless infrastructure.
- 4
Scale automatically
Your endpoint scales based on traffic. Pay only for GPU time used during inference.
Monitoring & Control
Full visibility into your AI infrastructure
Built-in dashboards show real-time metrics across all your deployments. Set alerts, analyze trends, and optimize costs without external tooling.
“The observability stack alone would have taken us months to build. Having it built-in means we can actually see what's happening in production.”
Ready to deploy your first model?
Start with our quickstart guide and deploy a model in under 5 minutes.