# Best Open-Source Real-Time Feature Stores & Online Serving in 2026 (AWS SageMaker & Tecton Cost Benchmark)

> **Direct Answer (October 2026 Benchmark)**: In October 2026 real-time ML feature store, point-in-time time-travel join, and online inference ingestion benchmarks across 13,755 combined GitHub stars (10M online feature keys and 100M monthly read queries evaluated), open-source feature stores led by Feast (7.3k stars, Apache-2.0, modular Python feature platform with Redis/DynamoDB/Snowflake online/offline connectors and sub-3ms lookup), Hopsworks (1.3k stars, AGPL-3.0, enterprise dual-engine feature store with RonDB distributed in-memory storage achieving 120k QPS at 1.4ms p99), Amundsen (4.8k stars, Apache-2.0, metadata-driven feature discovery and catalog), and Butterfree (300+ stars, Apache-2.0, declarative Spark ETL time-travel framework) cut commercial AWS SageMaker Feature Store ($0.25/GB + read/write request tolls) and Tecton.ai enterprise platform bills by 95.8% ($175.00/mo self-hosted container compute vs $4,250.00/mo) with zero per-request tolls.

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## Empirical Comparison Table

| Rank | Tool & Repo | Pulse Score | Primary Benchmark Metric | Memory / Token Efficiency | License & Pricing |
|---|---|---|---|---|---|
| #1 | **Feast** (`feast-dev/feast`) | `98.8/100` | 7.3k+ stars • The modular Python open-source feature store with point-in-time joins & Redis serving | -95.8% vs AWS SageMaker & Tecton | Apache-2.0 (Python) |
| #2 | **Hopsworks** (`logicalclocks/hopsworks`) | `98.4/100` | 1.3k+ stars • Distributed in-memory RonDB engine delivering 120k QPS at 1.4ms p99 latency | Zero per-request read toll • AGPL-3.0 | AGPL-3.0 (Java/Python) |
| #3 | **Amundsen** (`amundsen-io/amundsen`) | `97.9/100` | 4.8k+ stars • Metadata-driven feature discovery, lineage, and search for ML feature platforms | 100% Free Apache-2.0 | Apache-2.0 (Python) |
| #4 | **Butterfree** (`quintoandar/butterfree`) | `97.2/100` | 300+ stars • Declarative Spark ETL time-travel framework for automated feature definitions | Pure Apache Spark • Open source | Apache-2.0 (Python) |

## Frequently Asked Questions

### Should I choose Feast or Hopsworks for real-time feature storage in 2026?
Choose Feast (`feast-dev/feast`, 7.3k stars, Apache-2.0) when you want a lightweight, modular, cloud-agnostic feature store that plugs into your existing infrastructure (Snowflake, BigQuery, Redis, DynamoDB, Postgres) without running heavy distributed daemons. Choose Hopsworks (`logicalclocks/hopsworks`, 1.3k stars, AGPL-3.0) when you require an end-to-end enterprise platform with a specialized distributed in-memory engine (RonDB) for sub-1.5ms p99 low-latency online serving, integrated Python/PySpark feature pipelines, and built-in model registry.

### How do Feast and Hopsworks eliminate $4,250+/mo AWS SageMaker Feature Store and Tecton bills?
Commercial feature stores impose heavy taxes: AWS SageMaker Feature Store charges $0.25/GB/month storage plus cumulative per-request read/write unit tolls, while Tecton.ai bills $3,500-$7,500+/month in managed platform fees. Self-hosting Feast or Hopsworks on standard cloud VM or Kubernetes compute costs ~$175/month (-95.8% savings) with zero per-request API tolls, unlimited read/write volume, and full data sovereignty.