Redis Feature Form

A feature store made to get ML features into prod—fast.

Machine learning thrives on real-time data, but creating real-time pipelines can seem impossible. Not with Feature Form. Simply define your features as code, and Feature Form will turn them into prod-grade data pipelines for model training and inference.

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How it works
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Key features
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Serve real-time features to models in production

Operationalize real-time data for machine learning

Feature Form orchestrates between your offline data systems and real-time serving. It turns your feature definitions into computation across Snowflake, Databricks, and more, and keeps your Redis online store up to date for low-latency feature retrieval.

Real-time feature pipelines made easy
Works on your existing data stack
Define features once and use them for training and inference

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Faster feature iteration with reusable pipelines

Built for enterprise AI teams

Keep teams from reinventing features, understand which models are using which features, and learn how features are evolving over time.

Workspace-scoped auth and permissions
Isolated data providers and configs
Independent observability per workspace

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Get finely-tuned feature control

Plan and validate changes before they hit production with full visibility into how updates affect downstream pipelines and models.

Plan changes with ff apply --plan
Run impact analyses across pipelines
Split materializations into discrete jobs
Trace execution with OpenTelemetry

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Keep data secure at any scale

Maintain full access control, auditability, and stay compliant without compromising speed. Security is part of the system.

Workspace-scoped RBAC
API key pairs and model roles
Audit logs for every change
Secret providers, mTLS, encrypted transport

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<1msmillisecond per read

38%decrease in latency

It’s not just about putting in a food order. It’s about providing an optimal experience for our customers. It’s really, really fast. Plus it’s a lot cheaper.

Daniel GalinkinHead of ML Engineering

We also saw a 38% decrease in Redis latencies, helping to improve the runtime performance of serving models.

Arbaz KhanStaff Software Engineer

Redis Feature Form

Workspaces & multi-tenancy

Run multiple ML teams on one platform with workspace isolation, scoped access, and independent environments.

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Atomic DAGs & version control

Plan, apply, and roll back changes at the graph level so updates are consistent, traceable, and never partially applied.Scale with Apache Iceberg for indexing, time travel, and high-performance access to large datasets.

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One platform, full feature lifecycles

Define features once, orchestrate pipelines across your data stack, and serve in real time—without stitching together scripts and systems.

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Learn about tracking

Ensure models see the same feature definitions in training and production, eliminating drift and preventing silent failures

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Redis as an online store

Open architecture, no lock-in

Integrate with Snowflake, Databricks, Spark, Postgres, and more while Redis handles real-time serving.Use Feast as your offline feature store and Redis for real-time online serving.

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