Serverless functions vs containers: CI/CD, database connections, cron jobs, and long-running tasks

Serverless and container platforms provide push-to-deploy workflows, managed infrastructure, and automatic scaling, yet they differ in execution models which influence problem-solving for database connections, scheduled jobs, long-running tasks, and deployments. Serverless platforms, such as Cloudflare and Vercel, focus on deploying functions with automatic scaling and offer preview environments for testing, but face limitations like execution time caps, memory constraints, and cold starts. Container platforms, exemplified by Railway, deploy persistent services with customizable build processes and support for Dockerfiles, offering advantages in long-running tasks, consistent latency, and persistent connections. Both platforms integrate with GitHub for automated deployments and isolate environments for pull requests, ensuring secure database connections through environment-based credentials and private networking. However, serverless models require workarounds for cron jobs and long tasks due to inherent constraints, whereas containers provide more flexibility and resource allocation. Rollbacks in both platforms are facilitated through deployment history, though database migrations pose challenges. Ultimately, the choice between serverless and containers depends on whether the workload aligns with serverless constraints or benefits from the flexibility and resources of containers, with Railway offering a container-based platform that simplifies deployment akin to serverless solutions.