Lepton AI

Deploy LLMs as production-ready HTTP endpoints without managing infrastructure. Lepton automatically provisions and scales GPU resources based on request volume, handling model loading, batching, and resource allocation transparently. The platform abstracts away Kubernetes/container orchestration complexity by providing a unified deployment interface that maps model weights to GPU instances with automatic failover and load balancing.

Automatically exposes deployed models through OpenAI API-compatible endpoints (chat completions, embeddings, image generation formats). This enables drop-in replacement of OpenAI SDK calls without client-side code changes. The platform translates between Lepton's internal model format and OpenAI's request/response schemas, handling parameter mapping, streaming protocol conversion, and error code normalization.

Enables deployment of multiple versions of the same model with automatic version tracking and rollback capabilities. Developers can deploy a new model version and gradually shift traffic to it, with the ability to instantly rollback to a previous version if issues are detected. The platform maintains version history and allows pinning specific versions for reproducibility.

Tracks inference costs at granular level (per model, per endpoint, per user/API key) with detailed usage breakdowns (tokens, requests, GPU hours). Provides cost projections, budget alerts, and usage reports. Integrates with billing systems for automated invoicing.

Web-based IDE for testing deployed models with real-time parameter adjustment, prompt engineering, and response comparison. The playground provides a visual interface for modifying temperature, top_p, max_tokens, and other inference parameters without redeploying, with instant feedback on model outputs. It supports multi-turn conversations, batch testing, and export of working prompts as API calls.

Automatically captures and visualizes inference request metrics including latency, token counts, cost, error rates, and model utilization without requiring external monitoring infrastructure. The platform logs all API requests to a queryable dashboard, providing histograms of response times, per-model cost breakdowns, and per-user usage attribution. Metrics are exposed via Prometheus-compatible endpoints for integration with external monitoring systems.

Enables deployment of arbitrary model architectures and inference code by packaging them as Docker containers that Lepton orchestrates. Developers define model serving logic in Python (using FastAPI, Flask, or custom frameworks) and Lepton handles container scheduling, GPU allocation, and scaling. The platform provides base images with pre-installed ML frameworks (PyTorch, TensorFlow, JAX) and GPU drivers to simplify container creation.

Enables deployment of multiple model versions or variants as separate endpoints with traffic routing and A/B testing capabilities. Developers can define routing rules (e.g., route 10% of traffic to a new model version) and Lepton automatically distributes requests accordingly. The platform tracks metrics per model variant, enabling statistical comparison of model performance and cost-effectiveness.

Enables submission of large-scale inference jobs (hundreds to millions of requests) for asynchronous processing without blocking on individual request latency. Developers submit batches of inputs (via CSV, JSON, or API) and Lepton queues them for processing, optimizing throughput by batching requests together on GPU hardware. Results are stored and can be retrieved via polling or webhook callbacks.

Supports deployment and serving of image generation models (Stable Diffusion, DALL-E alternatives) and vision models (image classification, object detection, visual question answering). The platform handles model-specific requirements like diffusion step scheduling, LoRA adapter loading, and image preprocessing. Provides streaming image generation (progressive refinement) and batch image processing capabilities.

Automatically caches model weights in GPU memory across requests, eliminating repeated model loading overhead. The platform implements intelligent cache eviction policies to maximize GPU utilization when multiple models are deployed. Supports model quantization (INT8, FP16) and pruning to reduce memory footprint and improve inference speed without requiring manual optimization.

Provides API key-based authentication and fine-grained rate limiting to control access to deployed models. Supports per-user request quotas, per-minute rate limits, and cost-based quotas (e.g., limit users to $10/month of inference). The platform enforces limits transparently and returns standard HTTP 429 responses when quotas are exceeded, with headers indicating retry timing.