Serverless inference endpoints.
Deploy AI models as auto-scaling API endpoints in seconds. GPU, CPU, and TPU inference with pay-per-prediction pricing. No infrastructure to manage.
Any framework
Models
< 50 ms
Latency
GPU / TPU
Hardware
0 to ∞
Scale
Inference infrastructure.
Deploy any model as an auto-scaling API endpoint with GPU support.
Any framework
Deploy PyTorch, TensorFlow, JAX, ONNX, and Triton models. Custom containers for any runtime.
GPU auto-scaling
Scale GPU inference from zero to hundreds of replicas. Automatic model loading and warm-up.
A/B testing
Split traffic between model versions. Shadow deployments for safe testing.
Model caching
Intelligent model caching across fleet. Sub-second cold starts for cached models.
Access control
API key and IAM-based access control. Rate limiting and request validation.
Batch inference
Process large datasets in batch mode. Automatic parallelization and result storage.
Getting started
Launch your first instance in three steps. CLI, console, or API — your choice.
ur ai models upload my-model \
--framework=pytorch \
--artifact=model.tar.gzAI at any scale.
LLMs, computer vision, and embeddings — serverless inference endpoints.
Suggested configuration
H100 · vLLM · Auto-scale
Estimate your costs
Create detailed configurations to see exactly how much your architecture will cost. Pay for what you use, down to the second.
Configuration 1
Inference Runtime
Usage Volume
Infrastructure
Options
Cost details
Auto-scaling endpoints. A/B testing. Model monitoring.
Works seamlessly with
Frequently asked questions
Deploy AI models in seconds.
Serverless inference with GPU auto-scaling. Pay per prediction.