Fabra vs Feast: Context Infrastructure That Owns the Write Path

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 context infrastructure that owns the write path. We ingest, index, track freshness, and serve — not just query. This gives you the same core guarantees (Point-in-Time Correctness, Async I/O) without the infrastructure tax—plus lineage, replay, and auditability that Feast cannot provide.

The key difference: When regulators ask "what did your AI know when it decided?", Feast has no answer. Fabra does.

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
Lineage & Replay	❌ No	✅ Full audit trail
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:

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

Fabra:

@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.

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:

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

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

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

# 3. Serve
fabra serve features.py

FAQ

Q: Why is Feast so complicated to set up? A: Feast was designed for "big tech" scale — it assumes Kubernetes, Spark/Flink pipelines, and a dedicated platform team. For most companies (Series A-C, <500 engineers), this is overkill.

Q: Can I run a feature store without Kubernetes? A: Yes. Fabra uses DuckDB for local development and standard Postgres + Redis for production. No K8s required. Deploy to Fly.io, Railway, or any container platform.

Q: How do I migrate from Feast to Fabra? A: Convert your YAML feature definitions to Python @feature decorators. Same data access patterns, but simpler. Run pip install "fabra-ai[ui]" and fabra serve features.py.

Q: Does Fabra have the same guarantees as Feast? A: Yes for the hard problems: Point-in-Time Correctness (ASOF/LATERAL joins), Async I/O (asyncpg + redis-py). Plus we add lineage, context replay, and RAG support that Feast doesn't have.

Q: What if I need RAG/LLM features alongside ML features? A: Fabra's Context Store handles this natively with @retriever and @context decorators. Feast has no RAG support — you'd need to add LangChain, Pinecone, and custom glue code.

Q: Is Fabra production-ready? A: Yes. We use asyncpg connection pooling, Redis caching with circuit breakers, and Prometheus metrics. Same reliability patterns as Feast, without the infrastructure complexity.

Conclusion

If you want "Google Scale" complexity, use Feast. If you want context infrastructure that owns the write path—with lineage, replay, and auditability built-in—use Fabra.

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Also Building LLM/RAG Features?

Fabra includes a Context Store for LLM applications — vector search, token budgets, and full audit trails. Same infrastructure, same deployment.

Context Store → | RAG Audit Trail → | Compliance Guide →

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