leptonai

Developers write application code using Lepton's Python-native abstractionsdefining compute requirements, scaling behavior, and API surfaces declarativelyand the platform handles cluster management, auto-scaling, model caching, and API gateway infrastructure without requiring teams to become Kubernetes experts.

The platform supports GPU-accelerated workloads and is optimized for the specific patterns of AI serving: batching inference requests to maximize GPU utilization, streaming outputs for LLM generation use cases, handling cold start latency for models that need to be loaded from storage, and supporting multi-model serving architectures where several models collaborate on a single request.

Lepton AI integrates with Hugging Face for direct model deployment, making it straightforward to take any model from the Hub and serve it behind a production-ready API in minutes.

Startups building AI-native products, ML teams at enterprises looking to move beyond ad-hoc GPU serving setups, and developers building AI applications who prefer managed infrastructure use Lepton AI to focus on model selection and application logic rather than serving infrastructure.

The platform's pricing model around actual compute usage rather than reserved instances is particularly suited to workloads with variable demanda common pattern for AI features that are used heavily during business hours and minimally overnight.