Context Infrastructure Comparison: Fabra vs The World

TL;DR: Fabra is context infrastructure that owns the write path. We ingest, index, track freshness, and serve — enabling lineage, replay, and auditability that read-only frameworks cannot provide.

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

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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

# Feast: features.yaml + entity.yaml + registry.yaml + ...
# Fabra: Just Python
from datetime import timedelta

@feature(entity=User, refresh=timedelta(hours=1))
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.

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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.

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Fabra vs LangChain

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

• Abstractions for chains, agents, tools
• Integrations with 100+ services
• Read-only wrappers over external stores

Fabra is infrastructure, not a framework:

• We own the write path — ingest, index, track freshness
• Full lineage and replay for compliance
• Vector storage (pgvector) built-in
• Token budget management (@context(max_tokens=4000))

# 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 and orchestration. When to use Fabra: You need context infrastructure with lineage, replay, and compliance. (You can use both together — Fabra for storage/serving, LangChain for orchestration.)

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Fabra vs Pinecone

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

• SaaS only (no self-hosting)
• No lineage or replay — you can't audit what context was assembled
• No ML features integration
• No token budgeting

Fabra uses pgvector (runs in your existing Postgres):

• Self-hosted or managed Postgres
• We own the write path — full lineage and context replay
• Built-in token budgets and caching

When to use Pinecone: You only need vector search and prefer SaaS. When to use Fabra: You need context infrastructure with auditability, lineage, and ML features.

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Migration Guides

From Feast to Fabra

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

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

From LangChain to Fabra

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

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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