Link to headingHeroku vs Vercel: comparison guide
Many of us at Vercel grew up on Heroku, and there's a reason git push deployment caught on: it works. Vercel keeps that same workflow, but extends it with automatic preview deployments, global distribution across our edge network, framework-defined infrastructure and auto-scaling that works for both traditional web applications and AI applications or agents. It's the same simplicity you loved, with infrastructure that scales automatically. Get started today.
Link to headingKey differences
The fundamental difference between Heroku and Vercel lies in their architectural approach to hosting and scaling applications.
Architecture: Heroku uses a container-based model with dynos that run continuously, similar to traditional servers. Vercel deploys serverless functions that execute on-demand across a global network, eliminating idle server costs and reducing cold start times through optimized infrastructure.
Deployment model: Both platforms support git-based deployment, but Vercel extends this model with automatic preview environments. This ensures each commit maps one-to-one to a preview deployment.
Development workflow: Vercel optimizes for modern frontend and backend frameworks and short-lived serverless functions. Tools like the Workflow Development Kit (WDK) enable durable functions with enhanced reliability, allowing applications to adapt their structure for better performance.
Scalability of serverless with server-like concurrency: With Fluid compute, Vercel brings traditional autoscaling capabilities to serverless infrastructure and extends them to run invocations concurrently. This introduces Active CPU pricing, where you are only charged when your CPU is actively used, not for I/O or idle time.
Link to headingExecution models
Heroku's dyno model provides predictable, always-on containers that handle requests immediately. This works well for applications with consistent traffic patterns or those requiring long-running backend processes.
Vercel Functions execute in response to requests, scaling automatically from zero to handle traffic spikes. Functions have execution time limits that vary by runtime and plan, with longer limits available on Pro and Enterprise, but offer better resource utilization for sporadic or variable workloads.
The choice depends on your application's characteristics. Database-heavy applications with complex queries might prefer Heroku's persistent connections, while API-driven applications benefit from Vercel's automatic scaling and global distribution.
Link to headingFramework support
Heroku supports virtually any language or framework through buildpacks, making it compatible with legacy applications, microservices, and monolithic architectures. This flexibility comes with the responsibility of managing runtime environments and dependencies.
Vercel provides first-class support for modern frontend and backend frameworks including Next.js, React, Svelte, Nuxt, Hono, Flask, and FastAPI. The platform automatically configures build processes and optimizes deployments for these frameworks, reducing configuration overhead.
Both platforms handle static sites effectively, but Vercel's global CDN network and automatic optimization features provide performance advantages for frontend-heavy and high-traffic applications.
Link to headingAI infrastructure
Heroku does not currently provide native tooling specifically designed for AI workloads. Teams building AI applications on Heroku must manually integrate distinct providers, manage separate billing accounts, and build their own tools for caching and observability.
Vercel integrates these capabilities directly into the platform. AI Gateway provides a unified interface for accessing models, allowing teams to switch model providers without rewriting code. It also centralizes billing and provides observability into usage, performance, and costs. For application development, the AI SDK streamlines the implementation of streaming interfaces and agentic workflows, helping developers build AI features without managing the underlying integration plumbing. Vercel Workflow durably orchestrates complex, long-running, multi-step agent jobs.
Link to headingStorage and data solutions
Heroku offers add-ons for databases, caching, and file storage through its marketplace. Popular options include Heroku Postgres, Redis, and various third-party services.
Vercel integrates with modern database providers through its marketplace, including Neon, Supabase, AWS, MongoDB and more. Many of these partnerships provide optimized configurations and, in some cases, simplified provisioning or bundled benefits for Vercel users. The marketplace also offers unified billing and account management, eliminating the need to maintain separate billing relationships for each service.
For file storage, Vercel applications can use Vercel Blob for scalable static asset storage like images and videos, or AWS S3 for existing workflows.
Link to headingEnvironment management
Heroku provides environment variable management through its CLI and dashboard, with support for multiple environments through pipelines. Configuration changes require application restarts.
Vercel handles environment variables through its dashboard and CLI, with support for branch-specific variables and automatic encryption. Changes deploy instantly without downtime, and the platform supports preview deployments with isolated configurations.
Both platforms support secrets management, but Vercel's approach integrates more tightly with modern development workflows that rely on feature branches and pull request previews.
Link to headingMonitoring and observability
Heroku includes basic monitoring through its dashboard and supports third-party APM tools through add-ons. Log aggregation and alerting require additional services and configuration.
Vercel provides built-in analytics for web performance, function execution, user interactions, and AI workloads. Real-time logs, performance insights, and error tracking are included without additional setup. Enterprise plans include advanced monitoring and alerting features.
The platforms take different approaches to observability. Heroku offers flexibility to choose monitoring tools, while Vercel provides fully integrated observability optimized for serverless architectures and AI applications.
Link to headingSecurity and compliance
Heroku operates on AWS infrastructure and provides SOC 2 Type II compliance, with additional security features available through Enterprise plans. Applications inherit AWS's security model while maintaining control over their own security implementations.
Vercel maintains SOC 2 Type II and ISO 27001 and many other certifications. The platform provides automatic security headers, DDoS protection, and SSL certificates. Enterprise plans include additional security controls and compliance features.
Both platforms handle infrastructure security, but application-level security remains the developer's responsibility regardless of the hosting choice.
Link to headingPricing considerations
Heroku pricing is based on dyno hours and add-on usage. Basic dynos sleep after 30 minutes of inactivity on the Eco plan, while Standard and Performance dynos run continuously. Costs can be predictable but may include charges for idle time.
Vercel pricing combines function execution time, bandwidth, and build minutes. The Hobby plan includes generous limits for personal projects, while Pro and Enterprise plans offer higher limits and additional features. Costs scale with actual usage rather than allocated resources, within defined plan limits.
The economic comparison depends on traffic patterns, application complexity, and required features. Applications with steady traffic might find Heroku more predictable, while applications with variable usage often benefit from Vercel's usage-based model.
Link to headingMigrating from Heroku to Vercel
Moving from Heroku to Vercel requires planning around architectural differences and feature requirements.
Assessment: Review your application's dependencies, background jobs, database connections, and third-party integrations. Identify components that need adaptation for serverless architecture, such as long-running processes or persistent connections.
Incremental approach: Consider migrating components gradually. Start with frontend assets or stateless API endpoints, then move core application logic. This reduces risk and allows teams to learn Vercel's patterns before committing fully.
Database considerations: Plan database migration carefully, especially for applications using Heroku Postgres. Consider database providers that offer migration tools or hire specialists for complex schemas. Test connection patterns since serverless functions handle database connections differently than persistent dynos.
Environment setup: Configure environment variables, secrets, and integrations before migration. Set up monitoring and logging to ensure visibility during and after the transition.
Link to headingGetting started
If you decide to migrate, sign up and start with the migration strategy section above. The specific next steps depend on your application. For traditional web applications, begin with the assessment phase to review your current architecture and dependencies.
For AI-powered applications, consider whether rebuilding with Vercel's AI SDK and serverless architecture would better leverage the platform's capabilities and simplify your implementation.
No matter your migration path, you can use Vercel's preview deployments to test and validate changes before serving your application to production traffic.