---
title: "Fabra vs Feast vs Tecton vs LangChain | Feature Store & RAG Comparison 2025"
description: "Compare Fabra with Feast, Tecton, LangChain, and Pinecone. Find the best feature store and RAG infrastructure for your team. Updated for 2025."
keywords: fabra vs feast, tecton alternative, feature store comparison, langchain alternative, rag infrastructure, mlops tools, vector database comparison, context store, llm infrastructure
---

# Feature Store & RAG Comparison: Fabra vs The World

> **TL;DR:** Fabra is the only tool that unifies **Feature Store** (for ML) and **Context Store** (for LLMs) in one system. No Kubernetes required.

## Quick Comparison Table

### Feature Stores

| Feature | **Fabra** | **Feast** | **Tecton** |
| :--- | :--- | :--- | :--- |
| **Best For** | Startups & Scale-ups (Series A-C) | Enterprises with Platform Teams | Large Enterprises with Budget |
| **Open Source** | ✅ Yes (Apache 2.0) | ✅ Yes | ❌ No (Proprietary) |
| **Infrastructure** | **Lightweight** (Postgres + Redis) | **Heavy** (Kubernetes + Spark) | **Managed** (SaaS) |
| **Configuration** | Python Decorators (`@feature`) | YAML Files | Python SDK |
| **Point-in-Time Joins** | ✅ ASOF/LATERAL JOIN | ✅ Yes | ✅ Yes |
| **Processing** | DuckDB (Local) / Postgres (Prod) | Spark / Flink | Spark / Rift |
| **RAG/LLM Support** | ✅ Built-in Context Store | ❌ No | ❌ No |
| **Setup Time** | 30 seconds | Days | Hours |
| **Cost** | Free (OSS) | Free (OSS) | $$$$$ |

### RAG & LLM Infrastructure

| Feature | **Fabra** | **LangChain** | **Pinecone + Custom** |
| :--- | :--- | :--- | :--- |
| **Type** | Unified Infrastructure | Framework/Library | Vector DB + Glue Code |
| **Vector Search** | ✅ Built-in (pgvector) | ❌ Requires integration | ✅ Core feature |
| **Token Budgeting** | ✅ `@context(max_tokens=4000)` | ❌ Manual | ❌ Manual |
| **ML Features** | ✅ Full Feature Store | ❌ No | ❌ No |
| **Caching** | ✅ Redis (built-in) | ❌ Manual setup | ❌ Manual setup |
| **Self-Hosted** | ✅ Yes | ✅ Yes | ⚠️ Pinecone is SaaS |
| **Learning Curve** | Low (Python decorators) | High (many abstractions) | Medium |

---

## Detailed Breakdowns

### Fabra vs Feast

**Feast** is the gold standard for open-source feature stores, designed for "big tech" scale. It assumes you have:

- A dedicated platform team (5+ engineers)
- Kubernetes cluster running
- Spark/Flink pipelines

**Fabra** is designed for the "99%":

- Runs on your laptop with DuckDB
- Deploys to standard Postgres + Redis
- Python decorators instead of YAML hell

```python
# Feast: features.yaml + entity.yaml + registry.yaml + ...
# Fabra: Just Python
@feature(entity=User, refresh="hourly")
def click_count(user_id: str) -> int:
    return db.query("SELECT COUNT(*) FROM clicks WHERE user_id = ?", user_id)
```

**When to use Feast:** You have 100k+ QPS and a platform team.
**When to use Fabra:** You want to ship features this week, not this quarter.

---

### Fabra vs Tecton

**Tecton** is an enterprise SaaS product from the creators of Uber's Michelangelo. It's powerful but:

- Closed source
- Expensive ($50k+ / year)
- Vendor lock-in

**Fabra** provides 80% of the value for 0% of the cost:

- Same core guarantees (PIT correctness, async I/O)
- Open source (Apache 2.0)
- Deploy anywhere (Fly.io, Railway, AWS, GCP)

**When to use Tecton:** You're a Fortune 500 with dedicated ML budget.
**When to use Fabra:** You want enterprise features without enterprise pricing.

---

### Fabra vs LangChain

**LangChain** is a framework for building LLM applications. It provides:

- Abstractions for chains, agents, tools
- Integrations with 100+ services
- Steep learning curve

**Fabra** is infrastructure, not a framework:

- Vector storage (pgvector) built-in
- Token budget management (`@context(max_tokens=4000)`)
- Unified features + context in one system

```python
# LangChain: Multiple imports, chain setup, retriever config...
# Fabra: Python decorators
@retriever(index="docs", top_k=5)
async def search_docs(query: str):
    pass  # Magic wiring to pgvector

@context(store, max_tokens=4000)
async def build_prompt(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),
    ]
```

**When to use LangChain:** You need complex agent workflows.
**When to use Fabra:** You need reliable RAG infrastructure.

---

### Fabra vs Pinecone

**Pinecone** is a managed vector database. It's great for vector search, but:

- SaaS only (no self-hosting)
- No ML features integration
- No token budgeting
- Requires custom glue code for RAG

**Fabra** uses pgvector (runs in your existing Postgres):

- Self-hosted or managed Postgres
- Unified with Feature Store
- Built-in token budgets and caching

**When to use Pinecone:** You only need vector search and prefer SaaS.
**When to use Fabra:** You want vector search + ML features + token budgets in one system.

---

## Migration Guides

### From Feast to Fabra

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

# 2. Convert YAML to Python decorators
# 3. Run: fabra serve features.py
```

### From LangChain to Fabra

```python
# Replace LangChain retrievers with @retriever
# Replace custom context logic with @context
# Keep your LLM calls (OpenAI, Anthropic, etc.)
```

---

## Conclusion

| If you need... | Use... |
| :--- | :--- |
| Google-scale complexity | Feast |
| Enterprise SaaS with budget | Tecton |
| Complex agent workflows | LangChain |
| Vector-only SaaS | Pinecone |
| **Unified ML + RAG infrastructure** | **Fabra** |

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