Migrating from AWS to a VPS for better performance and privacy

AWS to VPS: A More Secure, Private, and Cost-Effective Cloud Alternative for AI Workloads

Businesses leveraging AWS for AI workloads often benefit from its scalability and vast service offerings, but these advantages can come with high and unpredictable costs. To reduce expenses, a company may consider transitioning to a self-managed Virtual Private Server (VPS) solution. This move involves migrating compute workloads, data storage, and AI-related services from AWS to one or more VPS providers offering dedicated resources at a fraction of the cost. The transition typically starts with an assessment of current AWS usage—identifying storage volumes, compute needs (like GPU or CPU usage), and networking patterns. Based on these metrics, the company can provision similarly sized VPS instances with providers that offer high-performance servers, dedicated GPUs, or NVMe-based storage at lower flat rates.

Once the infrastructure is in place, the business would need to replicate data and configure services such as container orchestration (e.g., using Docker or Kubernetes), database management, and AI model deployment pipelines. Security protocols and backup systems must also be implemented to match or exceed AWS’s standards. While this shift introduces more management overhead—since the company now handles updates, uptime, and scaling—it provides a more predictable and often significantly lower monthly cost. For AI workloads that require persistent compute power or large-scale storage but not the full ecosystem of AWS services, a well-managed VPS setup can be a cost-effective and performance-efficient alternative.

In addition to cost savings, migrating away from AWS gives organizations greater control over their data privacy and infrastructure security. By self-managing VPS environments, businesses can implement custom encryption policies, restrict third-party access, and ensure data locality in preferred jurisdictions—features that are often limited or buried under complex service agreements in large cloud platforms. This added layer of control can be critical for industries handling sensitive or proprietary data, ensuring compliance with internal policies or regional privacy regulations while reducing exposure to platform-level vulnerabilities or surveillance concerns.