---
title: "Fabra vs Context Engineering Platforms: Choosing the Right Tool in 2025"
description: "Compare Fabra to Meridian, LangChain, Feast, Tecton, and other context engineering platforms. Understand when to use a feature store vs context infrastructure."
keywords: fabra vs meridian, context engineering, feature store comparison, llm context assembly, rag infrastructure, tecton alternative, feast alternative
date: 2025-12-09
---

# 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.

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](https://trymeridian.dev/) 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.

```python
# Fabra: Define features and context in Python
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="5m")
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](https://feast.dev/) 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

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

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

### Tecton

[Tecton](https://tecton.ai/) 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](https://langchain.com/) 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

```python
# 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())
```

```python
# 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](https://www.llamaindex.ai/) 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:**

```python
# 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](https://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:

1. **Unified:** Features + Context + Retrieval in one tool
2. **Pythonic:** Decorators, not YAML
3. **Self-Hosted:** Your data stays yours
4. **Local-First:** DuckDB for development, Postgres for production
5. **Production-Ready:** Freshness SLAs, context replay, 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

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

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

[Read the quickstart →](../quickstart.md) | [Compare to Feast →](feast-alternative.md)

---

## Sources

- [Meridian AI](https://trymeridian.dev/) - Context infrastructure for AI agents
- [Top 5 Feature Stores in 2025](https://www.gocodeo.com/post/top-5-feature-stores-in-2025-tecton-feast-and-beyond) - GoCodeo
- [Feature Store Comparison](https://www.featurestorecomparison.com/) - Independent comparison
- [Context Engineering for Agents](https://blog.langchain.com/context-engineering-for-agents/) - LangChain Blog
- [Context Engineering Guide](https://www.kubiya.ai/blog/context-engineering-ai-agents) - Kubiya
- [LlamaIndex Context Engineering](https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider) - LlamaIndex Blog
