---
title: "Fabra vs Feast: A Lightweight Python Feature Store Alternative 2025"
description: "Detailed comparison: Fabra vs Feast. Why you don't need Kubernetes for a feature store. Python decorators vs YAML config. RAG support comparison."
keywords: feast alternative, lightweight feature store, python feature store, fabra vs feast, open source feature store, ml feature store, rag feature store
---

# Fabra vs Feast: The Lightweight Feature Store Alternative

If you are looking for a **lightweight feature store** that runs on your laptop but scales to production, you have likely found Feast. And you likely found it complicated.

Fabra is the **developer-first alternative to Feast**. We provide the same core guarantees (Point-in-Time Correctness, Async I/O) without the infrastructure tax—plus built-in RAG/LLM support that Feast doesn't have.

## Feature Comparison

| Feature | Feast | Fabra |
| :--- | :--- | :--- |
| **Setup Time** | Days (Kubernetes, Docker) | Seconds (`pip install`) |
| **Configuration** | YAML Hell | Python Code (`@feature`) |
| **Infrastructure** | Spark / Flink / K8s | DuckDB (Local) / Postgres (Prod) |
| **Point-in-Time Joins** | ✅ Yes | ✅ Yes |
| **Async I/O** | ✅ Yes | ✅ Yes |
| **Hybrid Features** | ❌ No (Complex) | ✅ Yes (Python + SQL) |
| **RAG/LLM Support** | ❌ No | ✅ **Built-in Context Store** |
| **Vector Search** | ❌ No | ✅ **pgvector integration** |
| **Token Budgeting** | ❌ No | ✅ **@context decorator** |
| **Target User** | Platform Teams | ML & AI Engineers |

## Why Choose Fabra?

### 1. No Kubernetes Required

Feast assumes you have a platform team managing a Kubernetes cluster. Fabra assumes you are a developer who wants to ship code.

- **Feast:** Requires Docker, K8s, and complex registry syncing.
- **Fabra:** Runs on your laptop with DuckDB. Deploys to standard Postgres + Redis.

### 2. Python, Not YAML

Feast relies heavily on YAML for feature definitions. Fabra uses Python decorators.

**Feast:**

```yaml
# features.yaml
name: user_clicks
type: int64
...
```

**Fabra:**

```python
@feature(entity=User)
def click_count(user_id: str) -> int:
    return random.randint(0, 500)
```

### 3. Built-in RAG & LLM Support

Fabra includes a **Context Store** for LLM applications—something Feast doesn't offer at all.

```python
from fabra.retrieval import retriever
from fabra.context import context, ContextItem

@retriever(index="docs", top_k=5)
async def search_docs(query: str):
    pass  # Magic wiring to pgvector

@context(store, max_tokens=4000)
async def chat_context(user_id: str, query: str):
    docs = await search_docs(query)
    tier = await store.get_feature("user_tier", user_id)
    return [
        ContextItem(content=f"User tier: {tier}", priority=0),
        ContextItem(content=str(docs), priority=1),
    ]
```

### 4. One-Command Deployment

Deploy to any cloud with generated configs:

```bash
fabra deploy fly --name my-app
# Generates: Dockerfile, fly.toml, requirements.txt
```

Supported targets: Fly.io, Cloud Run, AWS ECS, Render, Railway.

### 5. Feature Parity on the Hard Problems

Fabra matches Feast on the critical "hard" problems of feature engineering:

- **Point-in-Time Correctness:** We use `ASOF JOIN` (DuckDB) and `LATERAL JOIN` (Postgres) to prevent data leakage, just like Feast.
- **Async I/O:** Our production serving path uses `asyncpg` and `redis-py` for high-throughput, non-blocking performance.

## When to Use Feast

Feast is a great tool for massive scale. Use Feast if:

- You have a dedicated platform team of 5+ engineers.
- You are already running Spark/Flink pipelines.
- You need to serve 100k+ QPS (though Fabra handles 10k+ easily).
- You don't need RAG/LLM capabilities.

## Migration from Feast

```bash
# 1. Install Fabra
pip install "fabra-ai[ui]"

# 2. Convert feature definitions
# YAML -> Python decorators

# 3. Serve
fabra serve features.py
```

## Conclusion

If you want "Google Scale" complexity, use Feast.
If you want **"Heroku for ML Features + RAG"**, use Fabra.

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