Vercel Enterprise pricing & rate limits

Vercel operates on a custom pricing model that shifts away from standard VPS-style monthly subscriptions. For most enterprise accounts, you are looking at a bespoke contract rather than a fixed price. The platform provides a default workload baseline that includes 3 GB of application RAM. This allocation is shared across your deployments, which differs from the static resource pinning found on bare-metal providers or competitors like DigitalOcean. Because Vercel abstracts the underlying server layer, you are effectively paying for the convenience of global edge distribution rather than individual CPU cycles or specific AMD EPYC clock speeds.

• You are allotted 1000 GB of included egress and bandwidth, which is sufficient for many frontend-heavy applications but can be exhausted quickly by large media assets.
• Exceeding these bandwidth or egress thresholds triggers an overage rate of $0.15/GB, a figure that is significantly higher than the raw network costs found on infrastructure-heavy platforms.
• Storage overages are billed at $0.15/GB, which applies to static assets or cached content that exceeds your provisioned space.
• While the platform allows for unlimited app instances and unlimited monthly visits, your performance remains tethered to the 3 GB RAM limit per app, necessitating careful memory management for Node.js runtimes.

This environment is built for frontend teams and developers who need to deploy React or Next.js applications without managing OS-level patches or container orchestration. The unlimited instance count makes it useful for rapid feature branching and preview deployments, but the cost structure incentivizes developers to optimize bundle sizes and asset delivery. If your application requires heavy background processing or persistent long-running workers, you will likely hit the RAM ceiling or incur significant overage fees quickly. It remains a specialized tool for web-centric delivery where the primary goal is reducing time to production rather than minimizing raw compute costs.