Deploying to the Cloud: AWS, GCP, and Azure

Docker is great for "Standardization," but where does it actually run? In a production Graph RAG system, you rarely want to manage the hardware itself. You want to leverage Managed Cloud Services that handle the scaling, backups, and security of your graph.

In this lesson, we will explore the "Big Three" cloud strategies for Graph RAG. We will look at Amazon Neptune, Google Cloud Vertex AI Graph, and Azure Cosmos DB (Graph API). We will learn how to choose the right service based on your existing cloud footprint and how to connect your LangChain code to these high-availability clusters.

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1. Amazon Web Services (AWS): The Neptune Stack

If you are on AWS, Amazon Neptune is the primary choice.

• Protocol: Gremlin or OpenCypher.
• Managed RAG: Integration with Amazon Bedrock (for LLMs) and S3 (for ingestion).

The Workflow:

1. Documents arrive in S3.
2. AWS Lambda triggers an ingestion job into Neptune.
3. LangChain connects to the Neptune endpoint to retrieve context.

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2. Google Cloud Platform (GCP): The Vertex AI Graph

GCP has a deep integration between their graph capabilities and their AI suite (Vertex AI).

• Service: Spanner Graph (the newest high-scale option) or Managed Neo4j on GCP Marketplace.
• Advantage: Native integration with Gemini Multimodal models.

The Workflow: Use Spanner Graph for "Planet-Scale" Knowledge Graphs. It allows you to combine SQL and Graph queries in a single platform, perfect for when your RAG system needs both structured customer data and unstructured relationships.

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3. Microsoft Azure: Cosmos DB for Graph (Gremlin)

For the Microsoft ecosystem, Cosmos DB provides a Graph API specifically designed for highly distributed, multi-region applications.

• Protocol: Gremlin.
• Advantage: Integration with Azure OpenAI and Global Replication.

The Workflow: Azure is ideal if you are building an internal RAG bot for a large corporation that uses Active Directory for security.

graph TD
    subgraph "AWS"
    N[Neptune] --- B[Bedrock LLM]
    end
    
    subgraph "GCP"
    S[Spanner Graph] --- G[Gemini LLM]
    end
    
    subgraph "Azure"
    C[Cosmos DB] --- AO[Azure OpenAI]
    end
    
    style N fill:#FF9900,color:#000
    style S fill:#4285F4,color:#fff
    style C fill:#0089D6,color:#fff

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4. The "SaaS" Alternative: Neo4j Aura

If you don't want to be locked into a specific cloud provider's proprietary graph engine, you can use Neo4j Aura. This is a managed Neo4j instance that runs on any of the big three clouds but provides a consistent Cypher interface. This is the Most Popular Choice for developers who want to stay cloud-neutral.

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5. Summary and Exercises

Each cloud provider has a different "Flavor" of graph.

• AWS Neptune is mature and deeply integrated with Bedrock.
• GCP Spanner Graph is built for extreme, global scale.
• Azure Cosmos is best for global distribution and Microsoft directory integration.
• Neo4j Aura provides the most flexible, developer-friendly experience.

Exercises

1. Cloud Selection: Your company uses AWS for everything. You have 10 million documents. Should you use Neptune or manage your own Neo4j on EC2? Why?
2. Protocol Check: If you choose Amazon Neptune, do you use the Neo4jGraph class in LangChain or a different one? (Hint: Look up NeptuneGraph).
3. Visualization: Draw a map of your "Company Region" (e.g., US-East-1). Show where the Database lives and where the LLM (API) lives. Is there a "Cross-Cloud" latency risk?

In the next lesson, we will look at how to watch these clouds: Monitoring and Alerting for Graph DBs.