Feast (Tecton) / feast.dev
Open source feature store enabling ML teams to share, discover, and serve features consistently across training and production for reproducible and scalable ML model deployment.
Pricing
Free
Free plan
Yes
Category
Developer Tools
Platforms
3
Free plan
Yes
API access
Yes
Open source
Yes
Platforms
3
Feast (Feature Store) is an open source feature store that provides the infrastructure for computing, storing, and serving ML features consistently between training and production — solving the training-serving skew problem that causes production ML model performance to degrade from training performance.
The training-serving skew problem arises when features used in model training are computed differently (or at different times) than features served at inference — the most common and expensive cause of ML model performance degradation in production. Feast provides a single feature definition that generates consistent features for both training datasets and production serving.
Feature definitions are written as Python FeatureViews — defining the data source, transformation logic, and storage configuration once. Feast then handles generating offline training datasets from historical data and serving real-time features at low latency for production inference.
The feature registry provides a searchable catalogue of all defined features in the organisation — enabling teams to discover and reuse features built by other teams rather than recomputing the same signals. Feature reuse reduces redundant computation and ensures consistency across models using similar signals.
Tecton, the company that originated Feast's development and the managed enterprise feature store provider, provides Feast-compatible enterprise infrastructure with real-time feature computation, governance, and monitoring beyond the open source version's capabilities.
Feast runs as ml platform software built around data and code workflows. Users typically start with a prompt, upload, or connected data source, and the underlying model handles the heavy lifting before returning a result you can refine or export. It's available on python, java, and cli, with API access for teams that want to embed it into their own products.
The capabilities that matter most for teams evaluating Feast.
Single feature definition generates identical features for training datasets and production serving, eliminating the skew between training and inference that degrades production model performance.
Searchable catalogue of all organisational feature definitions enabling cross-team discovery and reuse — reducing redundant feature computation across ML models with similar signals.
Low-latency feature serving for production inference from online stores (Redis, DynamoDB) retrieving precomputed features in milliseconds for real-time ML predictions.
Open source and free. Tecton provides managed enterprise Feast from custom pricing. Cloud-provider managed versions available.
Model
Open Source
Starting price
Free
Free trial
No
Tecton provides managed enterprise Feast. Vertex AI Feature Store provides managed feature store on GCP. SageMaker Feature Store provides managed feature store on AWS. Hopsworks provides an alternative open source feature store.
A side-by-side look at the closest alternative in this category.
Key facts about model providers, platforms, and team support.
Model Provider
Feast, Tecton
Platforms
Python, Java, CLI
Deployment
Open Source, SaaS, Self-hosted
Integrations
Redis, DynamoDB, BigQuery, Snowflake, Spark, Flink, Kubernetes, API
Team Collaboration
No
Launch Year
2022
Compliance signals and data-handling notes as reported by the vendor.
Open source self-hosted keeps feature data on customer infrastructure. Tecton managed enterprise subject to Tecton's data handling. Enterprise agreements available.
Feature data stored in customer-controlled backends (Redis, DynamoDB, BigQuery). Feast does not access underlying data — customers control storage and access. Tecton managed has separate data handling.
Editorial Verdict
Feast is the standard open source feature store for ML engineering teams who want to eliminate training-serving skew and enable feature reuse, with Tecton providing the managed enterprise version.
Last verified July 24, 2026.
Open source and free. Tecton provides managed enterprise Feast from custom pricing. Cloud-provider managed versions available.
Open source self-hosted is free. ClearML Cloud managed from $13/month. Enterprise custom.
Open source self-hosted keeps feature data on customer infrastructure. Tecton managed enterprise subject to Tecton's data handling. Enterprise agreements available.
Open source self-hosted keeps all data on customer infrastructure. ClearML Cloud subject to ClearML's data handling policy. Enterprise includes data handling agreements.
Feature data stored in customer-controlled backends (Redis, DynamoDB, BigQuery). Feast does not access underlying data — customers control storage and access. Tecton managed has separate data handling.
Self-hosted deployment keeps all ML experiment data on customer infrastructure. ClearML Cloud transmits experiment data to ClearML's servers — review privacy policy.
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