Feast is a free, open source feature store for ML and RAG. See how it works, its architecture, pricing, pros and cons, and top alternatives like Tecton.
Feast is an open source feature store, a piece of data infrastructure that lets machine learning teams define, compute, store, and serve the input variables ('features') that models rely on, using one consistent definition for both training and live inference. It was created in 2018 by Gojek and Google Cloud and is now governed as an independent project under the LF AI and Data Foundation, part of the Linux Foundation.
The project pairs an offline store, typically a data warehouse such as BigQuery, Snowflake, or Redshift, used for historical feature extraction during model training, with an online store, typically a low-latency key-value database such as Redis or DynamoDB, used to serve the freshest feature values to production models in milliseconds. A central registry tracks feature definitions as versioned metadata, and a Python SDK plus optional REST feature server let engineering teams read, write, and discover features programmatically.
Feast's core technical differentiator is point-in-time correct joins, which automatically align historical feature values to the exact timestamp of each training example, preventing models from accidentally training on future data that would not have been available at prediction time.
Beyond structured, tabular features, Feast has expanded to support vector embeddings and similarity search through integrations with stores like Qdrant, Milvus, and Faiss, making it usable for retrieval-augmented generation (RAG) and other large language model applications, alongside data quality monitoring and observability tooling for inspecting what has actually been served in production.
Feast itself is completely free. It is released under the Apache License 2.0, meaning the source code can be downloaded, self-hosted, modified, and redistributed without any license fee, subscription, or usage cap imposed by the project.
There is no official managed Feast-as-a-service offering, so adopting teams pay only for the underlying cloud infrastructure they choose to run it on, such as a data warehouse, a Redis or DynamoDB cluster, and compute for materialization jobs, plus the engineering time to operate that infrastructure. Organizations wanting a managed alternative typically look to commercial vendors like Tecton rather than to Feast directly.
Yes. Feast is fully open source under the Apache License 2.0, so there is no license fee. Costs come only from the infrastructure (databases, warehouses, compute) a team chooses to run it on.
Feast was created in 2018 as a joint project between Gojek and Google Cloud, and was publicly announced as open source in January 2019. It moved under the governance of the Linux Foundation for AI (now LF AI and Data Foundation) in 2020.
A feature store centralizes the computation and storage of machine learning input variables so they can be reused consistently across model training and live production inference, avoiding duplicated pipelines and mismatches between training and serving data.
Yes, Feast supports materializing features from streaming pipelines into its online store for low-latency serving, in addition to scheduled batch materialization from the offline store.
Yes. Newer versions of Feast support storing and retrieving vector embeddings through integrations with stores like Qdrant, Milvus, and Faiss, enabling retrieval-augmented generation and other large language model workloads alongside traditional ML features.
The most commonly compared alternatives are Tecton (a managed commercial feature platform), Hopsworks (self-hosted or managed, popular in regulated industries), AWS SageMaker Feature Store, and Databricks Feature Store, each trading off managed convenience against Feast's open source flexibility.