AWS Startups Blog

Amazon Web Services (AWS) is launching its first Global Fintech Accelerator, giving fintech founders the support and mentorship they need to bring smarter financial services solutions to the market by leveraging the power of AI/ML and the cloud.

Reports show that only 1% of venture-backed founders are Black, 1.8% Latino, and 9% women. AWS aims to help change that. Last year, we launched the AWS Impact Accelerator for startups led by underrepresented founders—giving high-potential, pre-seed startups the tools and knowledge to reach key milestones, such as raising funds or being accepted to a seed-stage accelerator program, while creating powerful solutions in the cloud. Read on to find out more about each of the three cohorts we’ve held so far.

When startups build generative artificial intelligence (AI) into their products, selecting a foundation model (FM) is one of the first and most critical steps. Everything from user experience and go-to-market, to hiring and profitability, can be affected by selecting the right model for your use case. Learn about the most impactful aspects to consider when selecting a foundation model to meet your startup’s needs.

Welcome to “The evolving role of the startup CFO” series, which features perspectives from prominent players in the startup ecosystem. These blog posts tackle critical questions, including: What does the role of today’s startup CFO entail and how will it evolve over the lifecycle of a startup? How can we most effectively support CFOs as the cloud increases its dominance within the organization and balance sheet? And can the CFO better navigate—and ultimately enable—the relationship between technical leaders, CTOs, and engineering teams?

Read on to learn from Danel Dayan, investor at Battery Ventures, a global, technology-focused investment firm

AarogyaAI, a healthcare and life sciences startup, is building with artificial intelligence and machine learning (AI/ML) on AWS. AarogyaAI rapidly diagnoses drug resistance in patients caused by bacterial, fungal, and viral pathogens. This allows clinicians to make data-driven treatment decisions and prescribe drugs that effectively treat and increase health outcomes for patients.

Healthcare and life sciences (HCLS) startups recognize that technology is an impactful vehicle for advancing human health at speed and scale. More importantly, HCLS startups are working to do something about it. C2i Genomics, founded in 2019, is one such startup: C2i Genomics is building a whole genome intelligence platform to improve cancer monitoring. Using artificial intelligence (AI) and machine learning (ML) solutions, C2i Genomics’ platform analyzes sequenced genome data to detect the tumor burden of cancer patients via a simple blood test.

The Evolving Role of the Startup CFO” features perspectives from prominent players in the startup ecosystem to help CFOs better navigate—and ultimately enable—the relationship between technical leaders, CTOs, and engineering teams.

Leading our second spotlight is Chris Garber, Guild’s CFO.

AWS is excited to announce the cohort of startups accepted into the global AWS Generative AI Accelerator. The program kicks off May 24th at our San Francisco AWS Startup Loft and closes on July 27th. Over the course of their 10-week program, participants will receive tailored technical advice, dedicated mentorship, an opportunity to pitch their demos to venture capitalists (VCs) in the AWS network, and up to $300,000 in AWS credits. Critically, they will also have the opportunity to foster lifelong connections with their fellow founders and within AWS. Read on to meet the startups.

In the automotive industry, TIER IV is an innovative and disruptive startup that is transforming the vehicle production process and the future of mobility. Founded in 2015 by Shinpei Kato in Japan, TIER IV builds platforms based on open source software—platforms they manage using AWS—that their partners use for building autonomous vehicles.

When choosing the infrastructure for their ML workloads, startups should consider how to best approach training and inference. Training is process by which a model is built and tuned for a specific task by learning from existing data. Inference is the process of using that model to make predictions based on new input data. Over the last five years, AWS has been investing in our own purpose-built accelerators to push the envelope on performance and compute cost for ML workloads. AWS Trainium and AWS Inferentia accelerators enable the lowest cost for training models and running inference in the cloud.