Unifying or Separating Endpoints in Generative AI Applications on AWS

When building generative AI applications on AWS, one critical decision is how to manage multiple components. For example, you might have a retrieval-augmented generation (RAG) pipeline for context and a fine-tuned model for specific tasks. Should these components share a single endpoint, or should you give each one its own? Both approaches have their pros and cons, and the right choice depends on your use case.

In this article, I’ll break down the unified endpoint vs. separated endpoint designs, so you can make an informed decision for your architecture.

The Unified Endpoint Approach

With a unified endpoint, you deploy a single API Gateway and route requests to the appropriate model based on paths, methods, or query parameters.

Here’s how it works:

• Use a single API Gateway, like https://api.example.com.

• Backend logic (usually a Lambda function) handles routing. For instance:

  • POST /rag routes traffic to the RAG pipeline.
  • POST /fine-tuned invokes the fine-tuned model.

Why Choose Unified?

Cost-Effective: Operating one gateway is cheaper than managing multiple.
Simplified Integration: Clients use one URL for all requests, reducing complexity.
Flexible: Adding new routes for additional models or services is straightforward.

Potential Drawbacks

Routing Overhead: You need backend logic to manage and direct requests.
Shared Bottlenecks: High traffic to one pipeline might impact the other unless autoscaling is configured carefully.
Unified endpoints are great for early-stage projects or MVPs where simplicity and cost savings matter most.

The Separated Endpoint Approach

In a separated design, each model gets its own API Gateway. For example:

• https://rag.example.com for the RAG pipeline.
• https://fine-tuned.example.com for the fine-tuned model.

Why Choose Separated?

Scalability: Each gateway can scale independently, ensuring reliable performance.
Reliability: Issues in one model don’t affect the other.
No Routing Logic: Each gateway directly connects to its respective model, simplifying backend code.

Trade-Offs

Higher Costs: Operating multiple gateways adds to your AWS bill.
More Complex Integration: Clients need to manage multiple URLs, which can complicate development.

Separated endpoints are ideal for production systems with high traffic or strict performance requirements.

Which Approach Is Right for You?

It depends on your application’s stage and requirements:

Use Unified Endpoints If:

• You’re in the early stages or building an MVP.
• Traffic for both models is predictable and not too high.
• Cost savings and simplicity are top priorities.

Use Separated Endpoints If:

• Your application handles high traffic or requires independent scaling.
• Reliability and modularity are critical.
• You’re running a production-grade system with strict SLAs.

A Hybrid Approach?

In many cases, starting with a unified endpoint and transitioning to separated endpoints as your app scales can be the best option. This approach lets you balance simplicity and cost in the beginning with scalability and performance later on.

Final Thoughts

Architecting generative AI applications on AWS involves trade-offs, and there’s no one-size-fits-all solution. Unified endpoints keep things simple and cost-effective for small or early-stage projects, while separated endpoints shine in production systems with demanding workloads.

If you’re just starting out, consider trying a unified endpoint and evolving your architecture as needed. AWS services like API Gateway and Lambda give you the flexibility to adapt and scale your design over time.

What’s your preference—unified or separated endpoints? Let’s discuss in the comments below!