How OpenEvidence Built the Medical AI That Doctors Actually Trust

OpenEvidence is a clinical decision support platform used by over 40% of doctors in the United States, growing rapidly by supporting more than 20 million consultations in a single month in January 2026. In healthcare, even small errors can lead to fatal consequences, so securing both startup-level speed and hospital-grade reliability at the same time was a critical challenge.

To achieve this, the company adopted a hybrid architecture combining a backend based on Python and Google Cloud Platform (GCP) with a frontend based on Next.js and Vercel. The backend handles data collection and model orchestration, while the frontend implements automatic deployment and rapid scaling through Vercel.

The Vercel infrastructure played the following key roles in OpenEvidence's growth:

• Rapid development and deployment: Deployments complete within 5 minutes of a code commit, and preview URLs for each branch enable immediate workflow testing.
• Unlimited scalability: Stable response times are maintained even during traffic surges, without separate infrastructure provisioning.
• Cost efficiency: By adopting Fluid compute, serverless costs were cut by 90% while maintaining low latency and high performance.

As a result, OpenEvidence was able to minimize the burden of infrastructure management and focus on the product experience, building a solution that combines a modern user experience with hospital-grade stability.