Embeddings Generation

Generate embeddings at lightning speed, leveraging best-in-class models.

Define embeddings with a single line of code Create embeddings from unstructured data using a single command. Define and automate embedding generation, focusing on AI application development rather than GPU management and pipeline complexity.

Generate embeddings from all data sources Supercharge your AI applications with Tecton’s high-performance Embeddings Engine, capable of processing up to 100k embeddings per second. From structured databases and data warehouses to data lakes and streaming platforms, Tecton transforms all your available data into rich, multidimensional representations.

Leverage best-in-class embedding models Use best-in-class open-source embeddings models effortlessly. Experiment with cutting-edge models that ship out of the box with Tecton with a single line of code change. Alternatively, bring your custom PyTorch models to Tecton or request Tecton to leverage API-based model providers (such as AWS Bedrock or OpenAI). Leverage the most recent advancements in the field, accelerating your development cycle while staying at the forefront of AI capabilities.

Save money with an efficient embeddings engine Maximize GPU utilization through distributed inference, dynamic token batching, and automated token budget selection. Process larger-than-memory datasets efficiently, achieving up to 100k embeddings per second on standard cloud GPU instances. Scale AI applications effectively while optimizing performance.

Store and manage embeddings in a dedicated vector database Tecton integrates with your chosen vector database, providing efficient storage and retrieval of embeddings.

Features

• Declarative Embeddings Pipelines: Define embeddings from unstructured data with minimal code using Tecton’s batch feature views.
• Optimized Inference Engine: Dynamic batching, GPU-aware scheduling, and model-specific tuning for high-throughput generation.
• Seamless Data Integration: Connect to Snowflake, S3, BigQuery, and more for automated data ingestion and processing.
• Flexible Serving Architecture: Pre-generate or serve embeddings in real-time, with caching and autoscaling for performance.
• Model Agnostic & Extensible: Use built-in models like Snowflake Arctic or bring your own PyTorch models and APIs.