Using Serverless to Rapidly Build GenAI Solutions in AWS: A Powerful 1-2 Punch

At Allata, we believe that serverless compute, particularly on AWS, is a key enabler for rapidly building GenAI applications. We’re seeing numerous use cases across various industries where companies are successfully combining the speed of serverless compute with the power of GenAI to quickly build smarter applications and features, all with a strong focus on innovation.

Let's take a closer look at how these technologies can be leveraged together effectively...

The Typical Approach to Building Serverless GenAI Solutions

Most generative AI serverless solutions follow a foundational approach:

1. Define architecture and orchestration with serverless compute services
2. Configure foundation models to access data sources
3. Complete workflow actions with API calls
4. Accelerate application hosting and security with serverless compute

Why Serverless? (and what it is)

For those reading this who are less technical, serverless computing lets you build and run applications without worrying about managing servers. It automatically handles the setup, scaling, and maintenance, and you only pay per request, making it a cost-efficient way to focus solely on your app’s functionality. Some of the popular serverless services that are leveraged by teams include ECS, Step Functions, Lambda, Fargate, and EventBridge (to name a few).

Serverless compute abstracts away many of the interdependencies and cost concerns that development teams have faced in the past. Now, it’s cost-effective and quick for teams to spin up GenAI solutions (we’re talking ridiculously cheap, since it’s pay-per-request).

The obvious use cases we’re seeing gain traction with customers are retrieval-augmented generation (RAG), document summarization, intelligent document processing, and content generation, but there are so many more.

Validating AI Use Cases

There a number of excellent frameworks that your team can use if you aren't sure where to start when validating AI use cases. A few of my favorites are:

1. Using [Input Data] + perform [Functionality] to solve for [Need]
2. The "7 factor framework" that maps use cases against considerations like Team, Feasibility, Timeline, ROI, and Risks.
3. The "2 by 2 framework" which maps value against feasibility.

Let's Talk!

If you’re interested in chatting about leveraging AI more, specifically in AWS Cloud, I’d be happy to do a deeper dive into some of the use cases we’re seeing, both horizontally across industries and specific to industry verticals.