Featureform

Featureform makes it easier for developers to deliver the right data, at the right time, for the next generation of intelligent systems. Our open-source products, Featureform and EnrichMCP, give teams the tools to build and serve structured data for machine learning and unlock that same data for AI agents through a semantic layer.

Agent Infrastructure

Visit Website

Founded

2019

Location

San Francisco, CA

Employees

Funding

$10M+

Featureform (now part of Redis): Virtual Feature Store for ML & AI Agents

Featureform is an open-source, “virtual” feature store that sits on top of your existing data stack. Teams define features once in Python and reuse them across training and serving—without moving data. In 2025, Featureform was acquired by Redis to accelerate low‑latency, reliable delivery of structured data into AI agents and LLM applications. See coverage from [GlobeNewswire](https://www.globenewswire.com/news-release/2025/10/09/3164211/0/en/Redis-Acquires-Featureform-to-Help-Developers-Deliver-Real-time-Structured-Data-into-AI-Agents.html), [Techstrong](https://techstrong.ai/features/redis-acquires-featureform-to-strengthen-ai-data-infrastructure/), and [DevOpsDigest](https://www.devopsdigest.com/redis-acquires-featureform).

Key Highlights

**Category:** Open‑source, virtual feature store for ML and LLM/AI agent apps

**Core idea:** Define features once; reuse across training and inference with consistent semantics

**Strengths:** Point‑in‑time correctness, unified batch + streaming, low‑latency online serving

**Fit:** Works over your warehouses, lakes, and online stores (Iceberg‑friendly; online via Redis)

**Status:** Acquired by Redis (2025); part of Redis’ AI data and context engine strategy

**HQ:** San Francisco, CA (per [LinkedIn](https://www.linkedin.com/company/featureform-ml))

**Open source:** Available on [GitHub](https://github.com/featureform/featureform) with docs at [docs.featureform.com](https://docs.featureform.com) and site at [featureform.com](https://featureform.com)

What Featureform Does

Featureform provides a virtualized feature registry and orchestration layer that:

Keeps feature definitions, lineage, and governance centralized while data stays in your infra

Ensures point‑in‑time correctness from training sets to online serving

Bridges batch and streaming with a single definition and unified pipelines

Serves features at low latency for production models and AI agents

If you’re shipping LLM or ML systems and struggling to deliver correct, consistent features into production, Featureform targets this “last‑mile” feature layer—now aligned with Redis for fast online delivery.

Core Capabilities

**Define once, reuse everywhere:** Python‑based, versioned feature definitions reused across training and inference.

**Point‑in‑time correctness:** Avoids data leakage with time‑aware joins and reproducible training sets.

**Unified batch + streaming:** One logical feature spans Spark/Flink/Kafka pipelines for both offline and real‑time use.

**Online serving:** Low‑latency retrieval via online stores, commonly with [Redis](https://redis.io/) for production inference.

**Virtual architecture:** Orchestrates storage across your warehouses, lakes, and online stores; reduces vendor lock‑in.

**Lineage & governance:** Track versions, ownership, and usage for compliance and team‑wide reuse.

Who It’s For

Data engineering and MLOps teams standardizing features across the model lifecycle

ML engineers building real‑time personalization, fraud detection, ranking, and recommendations

LLM platform teams feeding structured, reliable context into agents and RAG systems

Orgs that want to keep their own infra (warehouse, lake, online store) while adding governance and reuse

Common Use Cases

Central feature registry with versioning, lineage, and discoverability across teams

Point‑in‑time correct training datasets and consistent online feature serving

Real‑time features for personalization, fraud detection, and ranking

Structured context delivery into AI agents and RAG pipelines

Unifying batch and streaming pipelines under one feature definition

Architecture & Integrations

Community talks and examples show patterns with:

Data processing & orchestration: Spark, Airflow, Flink

Warehouses & data lakes: Snowflake, Databricks, BigQuery, Iceberg tables

Online serving: Redis for low‑latency features

Streaming & messaging: Kafka

Helpful pointers:

Featureform site and docs: [featureform.com](https://featureform.com), [docs.featureform.com](https://docs.featureform.com)

Ecosystem references: [featurestore.org](https://www.featurestore.org/)

ODSC session (Featureform + Redis + Databricks + SageMaker): [Build and deploy with a feature store](https://odsc.com/speakers/feature-stores-in-practice-build-and-deploy-a-model-with-featureform-redis-databricks-and-sagemaker/)

Open Source & Trials

Open‑source repo: [featureform/featureform](https://github.com/featureform/featureform)

Licensing and activity: Check the repo for license, stars, contributors, and latest commits

Packaging: Post‑acquisition enterprise offerings are under Redis’ AI data portfolio; contact sales via [Featureform](https://featureform.com) or [Redis](https://redis.io/) for enterprise trials

Market Context & User Sentiment

Pros (community feedback):

Open‑source and approachable for smaller teams: [Reddit r/MachineLearning launch](https://www.reddit.com/r/MachineLearning/comments/v7pxj0/p_featureform_opensource_virtual_feature_store/)

Clear explanations of feature store architectures: [Reddit r/dataengineering discussion](https://www.reddit.com/r/dataengineering/comments/v3agqk/feature_stores_explained_the_three_common/)

Viable free option to try for feature store needs: [Reddit r/mlops thread](https://www.reddit.com/r/mlops/comments/1lanevv/need_open_source_feature_store_fully_free/)

Cons (community feedback):

Deployment can benefit from Kubernetes familiarity: [Reddit r/mlops discussion](https://www.reddit.com/r/mlops/comments/17xn3rw/feature_stores_tecton_is_no_longer_supporting/)

Early‑stage ecosystem vs. incumbents; limited third‑party reviews (no confirmed G2/Capterra listings)

Company & Status

Company: Featureform

Category: Open‑source virtual feature store for ML and AI/LLM apps

Status: Acquired by Redis in 2025 (see [LinkedIn](https://www.linkedin.com/company/featureform-ml) and press below)

Business model: Open source with enterprise features; now part of Redis’ AI data offering

Noted tech fit: Iceberg‑native positioning noted in acquisition posts; strong fit with Redis online serving

References & News

LinkedIn company profile: [Featureform on LinkedIn](https://www.linkedin.com/company/featureform-ml)

Acquisition coverage:

[GlobeNewswire](https://www.globenewswire.com/news-release/2025/10/09/3164211/0/en/Redis-Acquires-Featureform-to-Help-Developers-Deliver-Real-time-Structured-Data-into-AI-Agents.html)

[Techstrong](https://techstrong.ai/features/redis-acquires-featureform-to-strengthen-ai-data-infrastructure/)

[DevOpsDigest](https://www.devopsdigest.com/redis-acquires-featureform)

Counsel announcements: [Fenwick](https://www.fenwick.com/insights/experience/fenwick-represents-redis-in-acquisition-of-featureform), [Pillsbury](https://www.pillsburylaw.com/en/news-and-insights/pillsbury-advises-featureform-acquisition-redis.html)

Looking for specifics? See the [docs](https://docs.featureform.com) for integration matrices, configuration examples, and deployment guides.

Related Companies

Arcade

Baseten

Inference is everything. Baseten is an AI infrastructure platform giving you the tooling, expertise, and hardware needed to bring great AI products to market - fast. Our proprietary Inference Stack utilizes the cutting-edge of performance research combined with highly performant and reliable infrastructure to give you out-of-the-box global availability with 99.99% of uptime.

Cast AI

Increase your profit margin without additional work. CAST AI cuts your cloud bill in half, automates DevOps tasks, and prevents downtime in one Autonomous Kubernetes platform.

Ciroos

Ciroos (pronounced "Sai-rose") offers an AI SRE teammate that empowers site reliability engineers (SREs), DevOps and operations teams to be superheroes. Built from the ground up with the power of multi-agentic AI, Ciroos enables operations teams to reduce toil, investigate incidents, explain anomalies, and drive autonomous operations, across complex multi-domain environments, all while leaving humans in control. Reach out to us at www.ciroos.ai to learn more about what an AI SRE Teammate can do for you.

Context.ai

Context is the first AI Office Suite that automates your workflow by creating documents, presentations, spreadsheets, and more using your data, tools, and style.

Databricks Mosaic AI

Databricks is the Data and AI company. More than 15,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow. --- Databricks applicants Please apply through our official Careers page at databricks.com/company/careers. All official communication from Databricks will come from email addresses ending with @databricks.com or @goodtime.io (our meeting tool).