ShedBoxAI vs Airbyte

Both ShedBoxAI and Airbyte are open-source data tools. Airbyte focuses on connectors; ShedBoxAI focuses on AI-powered processing.

Quick Comparison​

Key Differences​

Infrastructure Requirements​

Airbyte runs as Docker containers:

docker compose up -d

ShedBoxAI is a single Python package:

pip install shedboxai
shedboxai run config.yaml

Connector Philosophy​

Airbyte provides pre-built connectors with a standard protocol. ShedBoxAI uses flexible YAML configuration for any REST API:

data_sources:
  any_api:
    type: rest
    url: "https://api.example.com/data"
    headers:
      Authorization: "Bearer ${API_TOKEN}"
    response_path: "data"

AI-First Design​

ShedBoxAI was built for AI-powered data processing:

ai_interface:
  model:
    type: rest
    url: "https://api.openai.com/v1/chat/completions"
    method: POST
    headers:
      Authorization: "Bearer ${OPENAI_API_KEY}"
      Content-Type: "application/json"
    options:
      model: "gpt-4"

  prompts:
    classify:
      user_template: |
        Classify this record:
        {{ record | tojson }}

Airbyte focuses on moving data, not processing it.

When to Choose Airbyte​

• You need many pre-built connectors
• Data replication is your primary use case
• You have Docker infrastructure
• You don't need AI processing

When to Choose ShedBoxAI​

• You want simpler infrastructure
• You need AI-powered processing
• You prefer YAML over connector configuration
• You want a lighter-weight solution

Using Both Together​

They can complement each other:

• Airbyte for data replication to a warehouse
• ShedBoxAI for AI processing and custom workflows

Get Started​

• Quick Start Guide
• Data Sources Documentation
• View all comparisons