Effortless Scale for Machine Learning Workloads
Shuttl gives you elastic GPU and CPU scaling out of the box—no infrastructure to manage, no wasted spend.
Elastic by Design
Traditional infrastructure forces you to pick between two bad options: over-provisioning and wasting money, or under-provisioning and hitting performance bottlenecks. Shuttl was built to eliminate that tradeoff.
Whether you're scaling model training, serving real-time inference, or just running experiments—Shuttl automatically adjusts compute resources based on actual demand.
Auto-Scaling GPUs & CPUs
Shuttl scales your compute up when it’s needed and down to zero when it’s not—automatically.
Zero Idle Waste
You only pay for what you use. Idle workloads are shut down without manual intervention.
Built for Spiky Workloads
Whether you’re batch training, real-time inferencing, or running experiments—Shuttl adapts in real-time.
Pricing That Scales With You
Shuttl pricing is based on actual resource usage—CPU, GPU, and runtime. If nothing’s running, your bill is zero.
Built for Builders, Backed by Serious Infrastructure
Shuttl gives ML developers the tools they need to ship fast—without dragging infrastructure along for the ride. Just connect your GitHub repo and push your code. We handle the rest: building, deploying, scheduling, autoscaling, and exposing clean APIs.
Behind the scenes, we run on a hardened Kubernetes control plane with smart autoscaling, secure isolation, and support for any workload that fits in a container.
Zero-DevOps Deployments
No YAML, no CLI, no CI/CD pipeline setup. Shuttl builds and deploys your code on every push—instantly.
Python-First, Container-Ready
Native support for Python ML frameworks. Need more flexibility? Bring your own Docker container.
Batch Jobs, Inference, or Event-Driven
Shuttl supports your full ML lifecycle—from one-off training jobs to always-on inference APIs.
Secure by Default
Every workload runs in its own container, inside a name-spaced VPC. Want even more isolation? We support dedicated clusters.
Smart Scheduling
Shuttl prioritizes workloads based on GPU/CPU needs, ensuring your jobs start fast and scale smoothly.
Flexible Ingress
Send data via bulk uploads or streaming queues. Supports both sync and async processing, encrypted at rest and in transit.
🚀 Ready to Scale Smarter?
Start deploying ML workloads without the infrastructure drag. No over-provisioning. No idle costs. No DevOps required.