Feature stores companies, products & suppliers
Serving and governing ML features for training and inference. Jobs: offline feature compute; online feature serving; point-in-time correctness.
What are Feature stores?
Serving and governing ML features for training and inference. Jobs: offline feature compute; online feature serving; point-in-time correctness.
What problems does it solve?
Training and serving use different feature code, so models silently skew.
Typical business use cases
- Offline feature compute
- Online feature serving
- Point-in-time correctness
Important capabilities
- Feature defs
- Serving API
- Lineage
What buyers should evaluate
- Point-in-time joins
- Access to features
- PII in features
Risks and governance considerations
Features that encode prohibited attributes.
Procurement checklist
- Feature catalogue
- Access
- Point-in-time tests
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
Amazon SageMaker Feature Store is a managed repository for machine learning features in Amazon SageMaker AI. Teams store, update, retrieve and share features for training and real-
By Canonical
Charmed Feast is Canonical's open-source, charm-based deployment of the Feast feature store, managed with Juju and designed to integrate with Charmed Kubeflow. It simplifies deploy
By Chalk
Chalk is the AI data platform that delivers real-time context and compute infrastructure for agents and ML models — deployed in your cloud, built for speed. Chalk Feature Store is
By Databricks
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data + AI Pl
By Google
Agent Platform Feature Store (formerly Vertex AI Feature Store) is Google Cloud's managed feature store for machine learning, part of Gemini Enterprise Agent Platform. It acts as a
By Hopsworks
Build, deploy, and scale production ML systems with Hopsworks. The Feature Store and MLOps platform for real-time AI, trusted by leading teams. Hopsworks Feature Store is Hopsworks
By JFrog
JFrog ML is JFrog's platform, from JFrog Ltd. Published at jfrog.com/jfrog-ml. Directory filing: Feature stores. Vendor legal name on this product record: JFrog Ltd. JFrog ML JFrog
By Microsoft
Managed feature store in Azure Machine Learning lets data teams develop features once and reuse them in production. Users supply a feature set specification and the service handles
By Redis
Developers love Redis. Unlock the full potential of the Redis database with Redis Enterprise and start building blazing fast apps. Redis Feature Form is Redis's platform, from Redi
By Snowflake
Snowflake enables organizations to collaborate, build AI-powered data apps, and unlock data insights—all within a secure and scalable AI Data Cloud. Snowflake Feature Store is Snow
By Tecton
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data + AI Pl
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Agent Platform Feature Store· Google
- Amazon SageMaker Feature Store· Amazon Web Services
- Azure Managed Feature Store· Microsoft
- Chalk Feature Store· Chalk
- Charmed Feast· Canonical
- Databricks Feature Engineering· Databricks
- Hopsworks Feature Store· Hopsworks
- JFrog ML· JFrog
- Redis Feature Form· Redis
- Snowflake Feature Store· Snowflake
- Tecton· Tecton
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Feature stores?
Serving and governing ML features for training and inference. Jobs: offline feature compute; online feature serving; point-in-time correctness.
What should not be listed as Feature stores?
Products whose buyer job is data prep tools, data modelling suites, or vector databases. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Feature stores supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What are Feature stores?
Serving and governing ML features for training and inference. Jobs: offline feature compute; online feature serving; point-in-time correctness.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.