AI Data Platform
Declarative Framework
Build powerful feature pipelines and manage infrastructure as code, all in Python
Define context as code in any notebook environment
Define features, embeddings, data sources, transformations, prompts, and tools using Python and SQL in any notebook environment. Enable collaboration between data scientists and engineers through a shared language and code repository, regardless of development platform.
Unify context creation across your AI stack
Access and manage data from disparate sources through a single interface. Streamline management of features, prompts, and embeddings from various data sources. Create, manage, and serve AI context from a centralized platform, improving consistency and reducing complexity.
Automate end-to-end pipeline management
Tecton’s declarative framework and CLI handle creation, provisioning, optimization, and execution of data pipelines based on standardized definitions. Deploy entire feature infrastructure with a single tecton apply command, reducing engineering overhead and optimizing pipelines for performance and efficiency.
Ensure reproducibility with built-in version control
Maintain full control over your AI assets with Tecton’s integrated version control capabilities. Track changes, manage versions of features and embeddings pipelines, and reproduce specific configurations using integrated version control. Tecton facilitates collaboration, debugging, and ensures consistency and reliability of AI applications over time.
Deploy updates to production instantly
Define features once for automatic updates across the AI stack. The production environment reflects the latest changes immediately, eliminating manual updates and reducing errors. Tecton minimizes downtime and accelerates deployment of new features and AI capabilities.
