Amazon SageMaker Feature Store
Fully managed repository to create, store, share, and serve ML features
Featureform
Turn existing data pipelines into a collaborative virtual feature store
Featureform provides a centralized, immutable repository for defining, managing, and serving ML features, leveraging your current data stack while adding RBAC, audit logs, and vector-database support.
Overview
Overview
Featureform is a virtual feature store that sits atop your existing data stack. It lets data scientists define features, labels, and training sets in a logical, immutable form while the platform orchestrates the underlying compute and storage—whether Spark, Redis, or a vector database.
Collaboration & Governance
By centralizing definitions with metadata such as lineage, owner, and variant, teams can share and reuse features safely. Built-in role-based access control, audit logs, and dynamic serving rules help organizations meet compliance requirements without changing their workflow.
Flexible Deployment
Featureform can run locally on a single machine, in a Docker container, or at scale on Kubernetes, connecting to any supported provider. Native support for embeddings enables versioned vector stores for both training and inference, making it suitable for modern ML applications.
Highlights
Infrastructure-agnostic: works with existing data platforms
Immutable feature definitions with lineage and versioning
Built-in RBAC, audit logs, and dynamic serving rules
Native support for embeddings and vector databases
Pros
- Centralizes feature definitions for team collaboration
- Leverages existing compute and storage resources
- Ensures immutability and reproducibility of features
- Supports embeddings and vector stores out-of-the-box
Considerations
- Requires orchestration setup and configuration
- Learning curve for the transformation language
- Limited to supported providers/infrastructure
- May add latency due to external orchestration
Managed products teams compare with
When teams consider Featureform, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Individual data scientists needing reproducible feature pipelines
- ML teams that already have a data stack and want a feature store layer
- Enterprises requiring fine-grained access control and auditability
- Projects that use embeddings or vector similarity search
Not ideal when
- Organizations without any existing data infrastructure to integrate
- Teams seeking a fully managed, turnkey feature store service
- Use cases requiring ultra-low latency serving without orchestration overhead
- Projects with strict licensing constraints beyond MPL-2.0
How teams use it
Notebook-to-Production Feature Pipeline
Data scientists push transformations from Jupyter notebooks to a central repository, then Featureform orchestrates Spark jobs and serves the feature in Redis for online inference.
Enterprise Governance Enforcement
Featureform’s RBAC and audit logs automatically enforce GDPR-based serving rules, ensuring only authorized models access sensitive features.
Embedding Store for Recommendation System
Transformer-based embeddings are versioned and stored in a vector database, enabling consistent training and real-time similarity queries.
Cross-Team Feature Reuse
Multiple teams discover and reuse existing feature definitions, reducing duplicate work and improving model consistency.
Tech snapshot
Go39%
Jupyter Notebook37%
Python15%
JavaScript7%
Gherkin1%
C++1%
Frequently asked questions
What data infrastructure does Featureform support?
Featureform is infrastructure-agnostic and can orchestrate transformations on platforms such as Spark, Redis, and vector databases, connecting to your existing resources.
How does Featureform guarantee feature immutability?
All feature, label, and training set definitions are stored as immutable objects with versioning and lineage metadata, preventing accidental changes.
Can I run Featureform locally for development?
Yes, Featureform can be run on a single machine, in a Docker container, or via Minikube for local testing.
Is there a hosted SaaS version of Featureform?
Featureform is provided as open-source software; there is no separate SaaS offering mentioned in the documentation.
Under which license is Featureform released?
Featureform is released under the Mozilla Public License 2.0 (MPL-2.0).
Project at a glance
Stable- Stars
- 1,962
- Watchers
- 1,962
- Forks
- 103
LicenseMPL-2.0
Repo age5 years old
Last commit7 months ago
Primary languageGo
Last synced 4 hours ago