Lepton AI
Build and deploy high-performance AI applications at scale with zero infrastructure management.

The industry-standard containerization platform for building, sharing, and running distributed AI and web applications.
Docker remains the cornerstone of the modern software development lifecycle in 2026, evolving from a simple container engine to a comprehensive AI-ready development ecosystem. Its technical architecture utilizes OS-level virtualization to deliver software in packages called containers, which are isolated from one another and bundle their own software, libraries, and configuration files. In the 2026 market, Docker has pivotally integrated 'Docker Scout' for real-time software supply chain security and the 'GenAI Stack' to facilitate the local orchestration of Large Language Models (LLMs) and Vector Databases. The platform operates on a client-server architecture, where the Docker Daemon (dockerd) manages objects like images, containers, networks, and volumes. With the rise of multi-architecture silicon (ARM64/x86), Docker’s BuildKit has become essential for high-performance, cross-platform image builds. Docker’s positioning is no longer just about isolation; it is about providing a standardized 'Developer Inner Loop' that ensures parity between a local MacBook, a GitHub Action runner, and a production Kubernetes cluster.
A software supply chain security tool that analyzes image layers against known CVE databases.
Build and deploy high-performance AI applications at scale with zero infrastructure management.
The fastest polyglot Git hooks manager for high-performance engineering teams.
The version-controlled prompt registry for professional LLM orchestration.
Template-free Kubernetes configuration management for declarative application customization.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A high-performance build engine featuring concurrent solver, cache imports/exports, and secret mounts.
A tool for defining and running multi-container applications using YAML files.
Native support for building images for different CPU architectures (x86, ARM, RISC-V) using QEMU emulation.
Allows developers to share reproducible development states including code, dependencies, and settings.
Ability to freeze a running container and restore it later from that exact state.
Pre-configured containers optimized for GPU passthrough (NVIDIA/AMD) with local LLM runtimes.
A monolithic application is too slow to scale and difficult to update.
Registry Updated:2/7/2026
New hires spend 3 days setting up local databases and languages.
Python dependency hell when trying multiple LLM libraries (PyTorch vs TensorFlow).