Fabra vs Context Engineering Platforms: Choosing the Right Tool in 2025

The AI infrastructure landscape has exploded. Feature stores, context platforms, RAG frameworks—every week brings a new tool claiming to solve your ML/AI data problems.

The key question to ask: Does this tool own the write path, or just query external stores?

This distinction matters because you can't audit what you don't control. When regulators ask "what did your AI know when it decided?", read-only frameworks have no answer.

This post cuts through the noise. We'll compare Fabra to the major players and help you choose the right tool for your use case.

The Landscape in 2025

The market has split into distinct categories:

Category	Tools	Focus
Feature Stores	Feast, Tecton, Hopsworks	Structured ML features
Context Infrastructure	Meridian AI, Contextual AI	Agent memory, conflict resolution
RAG Frameworks	LangChain, LlamaIndex	Document retrieval pipelines
Unified Stores	Fabra	Features + Context + Retrieval

Fabra vs Meridian AI

Meridian AI is context infrastructure for AI agents. It solves a specific problem: when agents encounter conflicting information across multiple sources, which is correct?

What Meridian Does

Causal Provenance: Tracks the "why" behind every data change
Conflict Resolution: Determines which conflicting document is accurate
Entity Resolution: Matches entities across different systems
90+ Integrations: Connects to Salesforce, Slack, Google Drive, etc.

What Fabra Does Differently

Fabra is a unified feature and context store for ML engineers building AI applications.

# Fabra: Define features and context in Python
from datetime import timedelta
from fabra.core import FeatureStore, entity, feature
from fabra.context import context, ContextItem
from fabra.retrieval import retriever

store = FeatureStore()

@entity(store)
class User:
    user_id: str

@feature(entity=User, refresh=timedelta(minutes=5))
def transaction_count_1h(user_id: str) -> int:
    return query_db(user_id)  # Your logic

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

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

When to Choose Each

Use Case	Meridian	Fabra
Agents accessing enterprise SaaS tools	✅	❌
Resolving conflicts across 50+ data sources	✅	❌
Compliance audit trails for enterprise agents	✅	✅
Building ML features + RAG in Python	❌	✅
Point-in-time correct training data	❌	✅
Token budget management	❌	✅
Self-hosted, open-source	❌	✅

Choose Meridian if: You're building enterprise agents that need to reconcile conflicting data from Salesforce, Jira, Slack, etc.

Choose Fabra if: You're building ML features and RAG pipelines in Python, want to self-host, or need point-in-time training data.

Fabra vs Feature Stores (Feast, Tecton, Hopsworks)

Feast

Feast is the open-source standard for ML feature stores. It's battle-tested at scale.

Feast Strengths:

Pluggable architecture (Redis, BigQuery, Snowflake)
Kubernetes-native
Strong community and ecosystem

Feast Limitations:

YAML-heavy configuration
Requires infrastructure (Spark, K8s)
No native RAG/LLM support

# Feast: YAML configuration
feature_views:
  - name: user_features
    entities: [user_id]
    ttl: 3600s
    features:
      - name: transaction_count
        dtype: INT64

# Fabra: Python decorators
@feature(entity=User, refresh=timedelta(hours=1))
def transaction_count(user_id: str) -> int:
    return query_db(user_id)

Tecton

Tecton is the managed feature platform built by Uber's Michelangelo team. It's the enterprise gold standard.

Tecton Strengths:

<10ms online latency
Native streaming support
Automatic lineage tracking
Enterprise SLAs

Tecton Limitations:

Managed SaaS only (no self-hosting)
Premium pricing
No native RAG support

When to Choose Each

Factor	Feast	Tecton	Fabra
Cost	Free (infra costs)	$$$$	Free
Setup Time	Days/weeks	Hours	Minutes
Infrastructure	Kubernetes + Spark	Managed	DuckDB (local) or Postgres
RAG/LLM Support	None	None	Built-in
Scale	100k+ QPS	100k+ QPS	10k+ QPS
Self-Hosted	✅	❌	✅

Choose Feast if: You have platform engineers and existing Spark/K8s infrastructure.

Choose Tecton if: You need enterprise SLAs, streaming features, and have budget.

Choose Fabra if: You want to ship this week, need RAG support, or don't have a platform team.

Fabra vs RAG Frameworks (LangChain, LlamaIndex)

LangChain

LangChain is the dominant framework for building LLM applications. It provides abstractions for chains, agents, and retrieval.

LangChain Strengths:

Huge ecosystem
Every LLM provider supported
Great for prototyping

LangChain Limitations:

Framework, not infrastructure
You still need a vector DB
No native ML feature support
Token budgeting is DIY

# LangChain: Framework approach
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA

vectorstore = Pinecone.from_documents(docs, embeddings)
qa_chain = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever())

# Fabra: Infrastructure approach
@context(store, max_tokens=4000)
async def build_prompt(user_id: str, query: str):
    return [
        ContextItem(content=await search_docs(query), priority=0),
        ContextItem(content=await get_user_context(user_id), priority=1),
    ]

LlamaIndex

LlamaIndex specializes in data indexing and retrieval for LLMs.

LlamaIndex Strengths:

Best-in-class data connectors
Advanced retrieval strategies
Memory components

LlamaIndex Limitations:

Framework-level, not infrastructure
No ML feature store
Requires separate storage

Key Difference: Framework vs Infrastructure

LangChain and LlamaIndex are frameworks—they tell you how to build.

Fabra is infrastructure—it stores and serves your features and context.

You can use both together:

# Use LangChain for your agent logic
# Use Fabra for feature storage and context assembly
from langchain.agents import AgentExecutor
from fabra.context import context

@context(store, max_tokens=4000)
async def build_agent_context(user_id: str, query: str):
    # Fabra handles storage, budgets, and serving
    return assembled_context

# Pass to your LangChain agent
agent.run(context=await build_agent_context(user_id, query))

Fabra vs Context Engineering Platforms

Contextual AI

Contextual AI provides infrastructure for RAG and agentic workflows with sophisticated retrieval.

Google ADK

Google's Agent Development Kit treats context as a first-class system with explicit transformations and scope isolation.

How Fabra Compares

Feature	Contextual AI	Google ADK	Fabra
Focus	Enterprise RAG	Multi-agent	Unified ML + RAG
Deployment	Managed	Framework	Self-hosted
ML Features	❌	❌	✅
Open Source	❌	Partial	✅

The Fabra Difference

Fabra occupies a unique position: context infrastructure that owns the write path.

Write Path Owner: We ingest, index, track freshness, and serve — not just query
Unified: Features + Context + Retrieval in one tool
Pythonic: Decorators, not YAML
Self-Hosted: Your data stays yours
Compliance-Ready: Full lineage, context replay, and audit trails

What Fabra Is Best At

Startups building AI features: Ship in days, not quarters
ML engineers who need RAG: Don't want to manage LangChain + Pinecone + Feast
Teams needing compliance: Full lineage and context replay
Python-first teams: Decorators > YAML

What Fabra Is Not For

100k+ QPS workloads: Use Tecton or Feast with Spark
Enterprise SaaS integrations: Use Meridian
No-code RAG builders: Use LangChain templates

Decision Framework

Do you need to reconcile conflicting data from 50+ SaaS tools?
  └─ Yes → Meridian
  └─ No ↓

Do you have 5+ platform engineers and Spark infrastructure?
  └─ Yes → Feast or Tecton
  └─ No ↓

Do you need 100k+ QPS with streaming features?
  └─ Yes → Tecton
  └─ No ↓

Do you want ML features + RAG in one Pythonic tool?
  └─ Yes → Fabra
  └─ No → LangChain + Pinecone + your favorite feature store

Try Fabra

pip install "fabra-ai[ui]"
fabra serve examples/basic_features.py

No Docker. No Kubernetes. No YAML. Just Python.

Read the quickstart → | Compare to Feast →

Sources

Meridian AI - Context infrastructure for AI agents
Top 5 Feature Stores in 2025 - GoCodeo
Feature Store Comparison - Independent comparison
Context Engineering for Agents - LangChain Blog
Context Engineering Guide - Kubiya
LlamaIndex Context Engineering - LlamaIndex Blog