Weave is a drag-and-drop machine learning deployment platform that makes it effortless for anyone from researchers to engineers to bring large-scale ML models into production without wrangling DevOps or infrastructure. Unlike platforms like AWS SageMaker or Hugging Face Deploy, which often require steep learning curves, complex configuration, or restrictive abstractions, Weave offers a radically simple, visual approach to deploying models. Just upload your model, drag it into place, and connect inputs and outputs. Weave handles the rest.
Weave introduces two powerful deployment modes: Threads and Fabric. Weave Threads is a serverless, lambda-like system designed for sparse, event-driven inference, perfect for low-frequency or bursty workloads where cost efficiency and scalability are key. Weave Fabric, on the other hand, is optimized for long-running, high-throughput applications where persistent memory and low latency are essential which is ideal for real-time APIs or conversational agents. Both modes are fully managed, auto-scaling, and support any ML framework, including custom models.
What sets Weave apart is its intuitive user experience, lightning-fast deployments, and flexibility. We’re model-agnostic, open by default, and designed to give developers complete control without requiring infrastructure expertise. Whether you're chaining models into complex pipelines, deploying your own LLM, or simply wrapping a trained model in an API, Weave makes it easy, scalable, and affordable. We’re weaving the future of ML deployment. Visually, and without compromise.
Built With
- amazon-web-services
- next.js
- tailwind
- typescript
Log in or sign up for Devpost to join the conversation.