Build Generative AI Applications on AWS- A Comprehensive Guide for 2025

Learn about- Build Generative AI Applications on AWS.

Today, GenAI is transforming businesses by offering unlimited opportunities to automate and innovate workflows. 97% of C-suite leaders see GenAI as a transformative factor to revolutionize business processes with advanced analytics capabilities. 

However, GenAI comes with integration and implementation challenges as it requires complex and resource-intensive infrastructure. To start with your GenAI applications, AWS is the right partner that provides businesses with the right tools and resources to scale at the right time, without impacting performance.

For example, Accenture use GenAI across workflows and experienced 30% more productivity in software development. They are planning to engage more employees with GenAI expertise. 

If you want to try your hands with GenAI, then you will find this blog interesting, as it covers-

• Key steps to develop GenAI apps using AWS
• Different AWS services and tools for the simplified development process
• Use cases and expert guidance

GenAI Landscape in 2024 and Beyond

GenAI has been the talk of the trend. Almost every enterprise is experimenting it across their workflows for the following reasons.

From conversational chatbots to automated security, workflows, customer support, and marketing, GenAI has made its impact. With the introduction of more advanced LLM models, GenAI capabilities are expanding to deliver the best possible user experience across industries.

Businesses thus seek advanced GenAI solutions that not only improve user experience but also are cost-effective and reliable. After GPT-4 versions, GPT-5 is to be launched soon with better analytics, performance, and efficiency capabilities. 

Reasons to stand out and deliver advanced experience are the core reasons of the growing GenAI market, expected to reach US$356.10bn by 2030. The results are more promising when we combine GenAI with cloud services offered by AWS.

The Role of Cloud Services in Developing GenAI Applications

Developing GenAI apps requires models that are trained on large data sets to provide accurate and faster results. Generative AI models, like GPT-3 or BERT, need huge amounts of data to learn and make better predictions. For example:

• BERT was trained using 3.3 billion pieces of text data (called tokens) and has 340 million settings (called parameters) that help it understand language.
• GPT-3 is much bigger, with 175 billion parameters, and was trained on 500 billion words taken from the internet (a dataset called Common Crawl).

Training such data requires resources, computational power, strong processors, GPUs, and other tools. This is where the role of cloud computing comes in. It has the capability to provide businesses with that much computational power, resources on demand, and storage for large data sets that are available at one click.

However, businesses find it difficult to manage all resources and costs. Thus, Artificial Intelligence Platforms as a Service (AIPaaS) come in. These are the platforms that help businesses build, train, and launch AI applications by combining AI tools with cloud services, making the process simpler and more cost-effective.

Among various cloud providers like AWS, Google, and Microsoft, AWS has more credibility, resources, and tools just for streamlining your AI experiments.

How? Let’s understand.

Why is AWS the best choice for GenAI Application Development?

AWS is the pool of resources, tools, and integrations that can simplify your GenAI app development journey. From inspecting images to creating fake data, making animations, or generating pictures and videos, AWS made it possible.

Here are some reasons to choose AWS experts for your next GenAI project.

• Easy to build applications- AWS offers pre-built customizable models. You can also customize them based on your data while ensuring high-end security.
• AWS offers a powerful infrastructure designed for machine learning, so your AI works fast.
• They also have special tools (like Graviton) that give you the best performance at a good price.
• AWS has built-in security tools to protect your data, like managing who has access and encrypting data to keep it safe.
• AWS tools like SageMaker Notebook, you can try out different ideas for your AI app without spending much time setting things up.
• It’s easy to connect your AI app with other AWS services like databases and analytics tools to get even more value.
• AWS focuses on implementing AI responsibly and ethically, which you don’t have to manage.

All you need is to get yourself an AWS expert agency that can pull off your GenAI app like a pro. Connect with AWS experts in India for cost-effective results. 

Prerequisites to Build Generative AI Applications on AWS

Before you start, let’s take a look at the key elements of your GenAI application. 

• Foundation model interface – A tool that lets you connect to AI models through an API (a way for different software to talk to each other).
• Front-end web/mobile application – The part of the app that users interact with, either on websites or mobile phones.
• Data processing labeling – Preparing data by marking or tagging it so that AI models can learn from it.
• Model training – The process of teaching AI models by using labeled data, so they can learn patterns and get better at their tasks.
• High-quality monitoring and security tools – Tools to watch over models, spot any problems, and keep data and systems safe.
• Vector database – A special type of storage that saves information (like text or images) in a way that AI models can understand and use.
• Machine learning platform – A system that provides everything needed to develop, test, and run AI models.
• Machine learning network storage – A way to store data that can be quickly accessed and used for machine learning tasks.
• AI model training resource – The powerful computers (like GPUs) used to help train AI models faster and more efficiently.
• Text-embeddings for vector representation – A method of turning words or text into numbers (vectors) so AI models can process and understand them better.

Step-by-Step Guide to Build Generative AI Applications on AWS

Once you have everything handy, let’s start developing.

Step 1- Choose the right approach to Build Generative AI Applications on AWS

There are two approaches to start with. Either you can create an AI model from scratch or you can fine-tune the existing AI model based on your data. How AWS helps in choosing the right approach.

• AWS offers Amazon Sagemaker (managed service) that helps businesses deploy the Gen AI models it offers tools to build, train, and deploy ML models, along with the capability to start from scratch.
• If you want to go with using the existing FM, then AWS offers Amazon Bedrock to fine-tune your existing AI models without the annotation of BigData. 

After choosing the suitable approach, you need data to train the algorithms. 

Step 2- Preparing Data

This step involves collecting, cleansing, and analyzing data followed by processing it to train the algorithms. Here are the steps to prepare the data for Gen AI app development on AWS.

• Data collection- gather the relevant data that will help you get the desired results. You can use tools like Amazon Macie to classify, label, and secure the training data.
• Data processing- it involves cleaning and formatting data to prepare it for modeling. This step reduces the chances of noise/errors, handling missing values, and other data-related issues. You can use Amazon EMR with Apache Spark and Apache Hadoop to process larger amounts of data instantly. 
• Data analysis- Look at how the data is spread out, find any unusual or incorrect data points, check if some data is more common than others, and figure out what changes are needed to clean the data. You can make this process easier by using Amazon SageMaker Data Wrangler to set up an automated workflow for analyzing the data.
• Feature engineering- Create new pieces of data or change the existing ones to make the data easier for the model to understand and learn from. You can use Amazon SageMaker Data Wrangler to make this process easier. It has over 300 tools that help you clean up, adjust, and combine data without needing to write any code.

Step 3- Select AWS Tools and Services

AWS offers many amazing Gen AI tools and services that streamline the Gen AI app development process. 

Amazon Bedrock

A fully managed service that provides powerful foundational AI models for tasks like language understanding and text-to-image. You can customize and fine-tune these models using custom APIs. some FM available on Amazon Bedrock are- Amazon Titan, Jurassic, Claude, Command, Llama2, and Stable Diffusion. 

AWS Inferential and AWS Trainium (custom machine learning accelerators)

A machine learning tool designed for better performance and cost-efficient ML workloads. AWS Inferentia is used for inference while ensuring high performance and cost-efficient results. You can use it to deploy models in production. 

AWS Trainium is built for training ML models efficiently. It improves model training time and reduces training costs. You can use it for-

• Training Large Language Models (LLMs)
• Image and Video Processing
• Natural Language Processing (NLP)
• Recommendation Systems
• Generative AI (Text-to-Image, Image-to-Image)
• Healthcare and Medical AI Models
• Autonomous Vehicles
• AI for Robotics
• Financial Services and Fraud Detection
• Gaming AI (NPC Behavior & Procedural Content Generation)

Amazon Code Whisper

An AI coding assistant developed by Amazon. It is designed to provide real-time, contextually relevant code suggestions to developers directly within their integrated development environment (IDE). from improving software development time to security scans, it has improved developers’ experience to code faster and better. 

Being integrated with AWS, it offers code suggestions for AWS application programming interfaces (APIs), making it even more valuable for developers on AWS projects. Trained on billions of lines of open-source and Amazon-exclusive code, it provides accurate and helpful code suggestions.

Amazon Sagemaker

It is a platform that gives full control over AI model training and deployment. It includes SageMaker JumpStart, which helps users find pre-built content and models to start building their own machine-learning apps.

Step 4- Train or Fine-tune Your AI Model

The next step is to either train your model from scratch or adjust an existing one. In both cases, having high-quality data is very important, so make sure you gather and analyze your data carefully. If you want to fine-tune the existing model, here are three approaches.

• Instruction-based Fine-Tuning- This involves training the model to do specific tasks using labeled data. It’s helpful for startups with limited data who want to create a custom AI model for their needs.
• Domain Adaptation- Here, you train the model using a large dataset specific to your industry or field. It’s useful for businesses with specialized data, like healthcare startups, who want the model to work with their unique information.
• Retrieval-Augmented Generation (RAG)- This method improves the model by adding an information retrieval system. It helps the model find and use relevant data (from a large collection) based on what the user is asking, making it more accurate and context-aware.

Step 5- Building your Gen AI Application

To deploy your trained AI model into a complete application using AWS, here are the steps.

• First, store your model and necessary files in an S3 bucket for easy access and version control. Then, create a Lambda function that will download the model from S3, process inputs, run the AI model, and return the results. 
• Set the function’s memory, time limits, and performance settings for optimal use. 
• Use API Gateway to expose the Lambda function as an API, allowing users to interact with the model securely. 
• For the user interface (UI), you can host a static web app on S3 or a dynamic one on services like EC2 or Elastic Beanstalk. 
• Lastly, if you’re using containers, you can deploy the app with ECS or AWS Fargate, and for GraphQL APIs, use AWS AppSync for efficient data management.

Step 6- Test Your Gen AI Application

Before launching your application on AWS for production, it’s important to thoroughly test it. This includes 

• Checking that all parts of the app work correctly through different tests like unit tests, integration tests, and end-to-end tests. 
• Test how the app performs under stress, heavy loads, and when it needs to scale. For generative AI applications, it’s essential to analyze data biases, ensure fairness, and assess the impact of the model’s outcomes to make sure the AI is ethical. 
• Perform security checks like vulnerability scans and penetration tests to protect data. 

Once testing is done, you can deploy the application on AWS using tools like infrastructure as code, automated deployment, A/B testing, and canary deployment. For example, you can use AWS Neuron to deploy models on Inferentia accelerators, which work well with machine learning frameworks like PyTorch and TensorFlow. 

Finally, set up auto-scaling and fault-tolerant systems to ensure your app remains reliable and scalable when it’s live.

When to Choose AWS For Gen AI Development?

The question is- Does AWS is the right choice for every Gen AI app development?

Well, yes, you can use AWS services to develop any type of Gen AI application. 

• AI chatbots (AI customer support)
• Build a real-time data analytics tool
• Employee assistant
• Automate code generation
• Customized reporting  

Real-world Use Cases- AWS Generative AI in Action

Here are some brands that have used AWS to develop Gen AI solutions. 

1. Booking.com Builds Generative AI for Customer Recommendations

• Goal: create an AI system to recommend travel options to customers.
• Solution: They used Amazon SageMaker to build, train, and deploy their AI models. For scaling, they also used Amazon Bedrock, which helps them fine-tune AI models to give personalized travel recommendations.
• Benefit: With Amazon Bedrock, they can use the right language models and keep their data secure. This helps them provide better, more personalized travel suggestions to customers.

2. Nearmap Uses Amazon SageMaker for Faster AI Model Training

• Goal: Track environmental and structural changes using AI, but their model training was slow.
• Solution: They switched to Amazon SageMaker, which helped speed up training by using cloud-based services instead of syncing data locally.
• Benefit: With SageMaker, Nearmap improved the speed of their AI model training, allowing them to deliver results faster, and used Amazon S3 for storing large datasets.

3. Instabase Builds an AI Hub for Document Processing

• Goal: Help businesses process documents using AI without needing to build their own models.
• Solution: They built their AI Hub platform on AWS, using services like Amazon EC2 and Amazon EKS to run large AI models that can digitize and understand documents.
• Benefit: This setup saved costs, improved performance, and allowed Instabase to get SOC 2 compliance quickly. They also reduced compute costs by 30% and launched their platform in just 3 months.

4. Jumio Uses Amazon SageMaker and Rekognition for ID Verification

• Goal: Verify government-issued IDs submitted by users for authenticity and prevent fraud.
• Solution: They used Amazon SageMaker to create AI models that can detect tampered IDs and Amazon Rekognition for face matching, ensuring the person matches their ID.
• Benefit: By using AWS, Jumio scaled its identity verification system globally and complied with privacy laws in over 200 countries, providing secure and efficient ID verification for users.

Working and implementing efficiently each AWS tool and service requires AWS expertise. Connect with the right AWS expertise partner to kickstart your Gen AI Journey.

Start Your Gen AI Journey with OnGraph’s AWS Expertise

OnGraph is an AWS Premier Consulting Partner. Our team of 20+ certified professionals follows best practices to deliver generative AI capabilities for your applications. We work with clients in the healthcare, supply chain, marketing, and SaaS industries, providing AI solutions with robust architecture.

OnGraph’s AWS Case Study

OnGraph’s expertise was evident when one of the clients, Pirkx, wanted to create a data analytics and machine learning platform for the healthcare and benefits industry. The objective was to manage requests, improve customer insights, and enhance decision-making. Accurately integrating and analyzing data from disparate sources like customer records, operational metrics, and engagement data was challenging.

OnGraph used AWS Glue for data extraction and transformation and Amazon S3 as a centralized data lake to solve these challenges. For secure processing, we leveraged Amazon Redshift and Athena for analytics, while Amazon SageMaker facilitated predictive modeling. AWS QuickSight provided interactive dashboards, ensuring stakeholders could access insights in real time. We used AWS IAM and KMS to ensure system security and data compliance.

Our comprehensive approach:

• Reduced ETL processing time by 70%
• Improved churn prediction accuracy by 85% and personalized recommendations by 90%
• Enhanced decision-making speed by 40%
• Achieved a 35% reduction in infrastructure costs while ensuring secure data handling

Building robust data analytics and machine learning solutions with AWS tools requires the right expertise. If you are looking to implement a similar solution and are uncertain about which AWS services to use, OnGraph can help. Contact us now to discuss your requirements and challenges with our AWS consultants.