Feast
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
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What is Feast?
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.
How Feast works
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.
Watch Feast in action
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What makes it worth shortlisting
The capabilities that matter most for teams evaluating Feast.
Training-serving consistency
Single feature definition generates identical features for training datasets and production serving, eliminating the skew between training and inference that degrades production model performance.
Feature registry
Searchable catalogue of all organisational feature definitions enabling cross-team discovery and reuse — reducing redundant feature computation across ML models with similar signals.
Online serving
Low-latency feature serving for production inference from online stores (Redis, DynamoDB) retrieving precomputed features in milliseconds for real-time ML predictions.
Best use cases
Who should use it
Pros
- Solves training-serving skew — the primary cause of ML model performance degradation in production
- Open source Apache 2.0 — free to use and self-host at any scale
- Feature registry enables cross-team feature discovery and reuse reducing redundant computation
- Point-in-time correct historical retrieval prevents data leakage in training datasets
Cons
- Self-hosting Feast requires significant infrastructure setup — Redis, storage backends, compute
- Smaller community than MLflow — fewer tutorials and integration examples
- Real-time feature computation requires Tecton enterprise or custom infrastructure beyond open source Feast
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Is it worth the price?
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
Tools like Feast
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.
Feast vs Hugging Face
A side-by-side look at the closest alternative in this category.
Feast
Feast (Tecton)
Hugging Face
Hugging Face
Open source and free. Tecton provides managed enterprise Feast from custom pricing. Cloud-provider managed versions available.
Free for most models and Spaces. Pro $9/month for private models, priority inference, and ZeroGPU. Enterprise Hub from $20/user/month for team collaboration and security.
Open source self-hosted keeps feature data on customer infrastructure. Tecton managed enterprise subject to Tecton's data handling. Enterprise agreements available.
Enterprise Hub includes SSO, access controls, audit logs, and private infrastructure. SOC 2 compliant.
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.
Public models and datasets are openly accessible. Enterprise Hub provides private model storage with data handling agreements. Review individual model licences before commercial deployment.
- Solves training-serving skew — the primary cause of ML model performance degradation in production
- Open source Apache 2.0 — free to use and self-host at any scale
- Feature registry enables cross-team feature discovery and reuse reducing redundant computation
- Point-in-time correct historical retrieval prevents data leakage in training datasets
- De facto standard hub for open source AI models with 500,000+ available
- Transformers library provides consistent API for diverse model families
- Spaces enable easy demo deployment without web infrastructure
- Free for public models and strong community support
- Self-hosting Feast requires significant infrastructure setup — Redis, storage backends, compute
- Smaller community than MLflow — fewer tutorials and integration examples
- Real-time feature computation requires Tecton enterprise or custom infrastructure beyond open source Feast
- Technical platform primarily for developers and researchers
- Inference API quality varies for very large or complex models
- Navigation complexity increases with scale of the platform
Technical & deployment info
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
Security & privacy
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.
What users are saying
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Common questions about Feast
Editorial Verdict
Should you use Feast?
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.