Build differentiated software experiences on strong data foundations

Deliver the right data and infrastructure to support secure, commercially viable software innovation

Unlocking generative AI’s potential starts with data

While foundation models are powerful out of the box, they are not tuned to your business needs—since they can’t access your most recent company data or perform domain-specific tasks to fulfill user requests.

The path to unleashing generative AI’s value for Sage is the same as with any kind of AI. Models need training data to deliver accurate outputs. So, the quality of any generative AI solution hinges on the efficiency of your data infrastructure.

We’ve long known that data is a strategic asset to businesses. Unsurprisingly, 44% of business leaders said that their companies are planning to implement data modernization efforts in 2024 to take better advantage of generative AI.³

Overcoming this challenge is essential if Sage is to develop new generative AI powered applications that are aligned to your customer experience, internal knowledge, brand voice, and ethical parameters.

As your existing cloud infrastructure provider, AWS is uniquely positioned to provide you with the technology and cost-efficiencies you need. With a data foundation built on AWS, you can more easily accelerate your adoption and optimization of generative AI.

³ PWC, 2024

How AWS enables efficient generative AI development

Ease of integration
Amazon Bedrock offers the ability to manage large volumes of data and integrate them securely with other AWS services. It is the easiest way for AWS customers to implement valuable, well-governed generative AI solutions.

Cost-efficient infrastructure
Right-fit your choice of model and data infrastructure to your use case to build and train models more cost-effectively. AWS offers the best value and price performance for cloud infrastructure, with the flexibility to select the best options to optimize performance and cost.

Flexibility and scalability
AWS Regions and AZs let you choose how and where you want to run your workloads and FMs. Meanwhile, AWS Local Zones or AWS Wavelength empower you to run apps with single-digit millisecond latencies.

Enable cost-efficient
AI innovation

Cost-effective data infrastructure

Developing, testing, implementing, and scaling a generative AI solution is resource intensive—and the less efficient your infrastructure is, the higher the cost will be. Maximizing the efficiency of your IT systems can be a powerful accelerant and ROI multiplier for generative AI projects.

AWS infrastructure is high-performance and low-cost. Our range of databases, data lakes and warehouses, and analytics tools deliver a proven ROI of 162%.⁴

High-performance, low-cost GPUs

Accelerate your time-to-solution by up to 300% and reduce model training costs by up to 40%⁵ with Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs.

Reduce generative AI model training costs by up to 50%⁶ and deliver high performance with Trainium-powered Trn1 instances.

Deliver up to 2.3x higher throughput and up to 70% lower cost per inference⁷ with AWS Inferentia accelerator-powered Amazon EC2 Inf1 instances.

⁴ AWS for Data
⁵ Compared to previous-generation GPU-based EC2 instances
⁶ Compared to comparable EC2 Instances
⁷ Compared to comparable EC2 Instances

Leonardo leverages generative AI to enable creative professionals and enthusiasts to produce visual assets with unmatched quality, speed, and style consistency. Utilizing AWS Inferentia2, we are able to reduce our costs by 80%, without sacrificing performance, fundamentally changing the value proposition we can offer customers, enabling our most advanced features at a more accessible price point.”

Pete Werner
Head of AI, Leonardo.ai

Ensure AI security
by design

AWS offers the most secure and reliable cloud for your applications. Security is our highest priority. As an AWS customer, you benefit from data centers and network architectures that are built to meet the requirements of the most security-sensitive organizations.

We are committed to safeguarding the security and confidentiality of your workloads. AWS AI infrastructure and services have security and privacy features built-in to give you control over your data. So, you can rest assured that your data is being handled securely across the AI lifecycle including for data preparation, training, and inferencing.

With Amazon Bedrock, you have full control over the data you use to customize the foundation models for your generative AI applications. Your data is encrypted in transit and at rest. Additionally, you can create, manage, and control encryption keys using the AWS Key Management Service (AWS KMS).

Key principles of securing AI infrastructure

Complete isolation of the AI data from the infrastructure operator. We ensure that the infrastructure operator has no access to customer content or AI data, such as AI model weights or the data model’s process.

Ability for customers to isolate AI data from themselves. AWS infrastructure provides a mechanism to allow model weights and data to be loaded into hardware, while remaining isolated and inaccessible by our customers’ users and software.

Protected infrastructure communications. We protect the communication between devices in the ML accelerator infrastructure, ensuring all externally accessible links between devices are encrypted.