It also increases productivity. As many companies have discovered, AI-powered tools can automate routine tasks, generate content, and provide intelligent assistance, freeing up human workers to focus on higher-value creative and strategic work.
Democratizing AI levels the playing field. Small businesses and startups can now access the same powerful AI capabilities as large enterprises, letting them compete more effectively and bring innovative solutions to market faster.
At the same time, companies can customize AI solutions for their specific needs. Organizations can fine-tune models for their unique use cases and data, rather than relying solely on general-purpose, one-size-fits-all solutions.
AWS’s democratization efforts emphasize ethical AI principles and best practices, helping to ensure that generative AI is developed and deployed responsibly across industries.
The Power of Choice
To be sure, no single generative AI solution works for all companies. Businesses have diverse needs, resources, and levels of AI expertise. Some are just beginning to experiment, while others are ready to develop and deploy custom models at scale. That’s why our approach emphasizes flexibility and choice. Our vision is to meet builders and organizations wherever they are on their generative AI journey.
For those just getting started, AWS offers no-code tools such as Amazon SageMaker Canvas that abstract away complexity, allowing business users to leverage foundation models through a visual interface. This democratizes access and enables rapid experimentation without significant AI knowledge.
As organizations progress, AWS provides fully managed services like Amazon Bedrock for building and scaling generative AI applications with powerful foundation models. This allows developers and AI engineers to harness the latest advances in areas such as retrieval augmented generation and agentic workflows, while maintaining full control and customizability.
For those looking for more flexibility and control, Amazon SageMaker lets you train, evaluate, fine-tune with advanced techniques, and deploy foundation models with fine-grain controls. This includes a variety of compute solutions including AWS’s purpose-built AI chips Trainium and Inferentia, simplified distributed training environments, automated model optimization, and flexible model deployment options to help organizations balance performance and cost.
This flexibility allows you to mix and match services based on your skill set and use case requirements. For instance, a data scientist might build custom models using AWS’s advanced training capabilities, share them via custom model import capabilities, and make them available to application developers via API. This seamless collaboration empowers teams to leverage their respective strengths while working toward a common goal.
Real-World Impact
The democratization of generative AI is already driving innovation across industries. For example, Workday, a leading provider of solutions that help organizations manage their people and money, focuses on developing AI-powered products. To streamline model development and free engineers from infrastructure maintenance, the company adopted SageMaker, allowing Workday to rapidly iterate and deploy complex models, including LLMs, to production.
Similarly, Perplexity, a startup building a conversational “answer” engine (as opposed to a search engine), faced the challenge of optimizing its LLMs for accuracy and precision while answering more than 250 million user queries each month. By leveraging AWS’s advanced ML infrastructure, Perplexity reduces model training time by up to 40 percent, handles more than 500,000 queries per hour without compromising latency, and enables developers to focus on model fine-tuning instead of managing infrastructure. The flexibility to allocate tailored computing resources further optimized Perplexity’s workflows.
