Deployment Targets: Engineering for Vibe Coders
Vibe coding makes it easy to create prototypes fast, but where your prototype runs matters almost as much as how it works. The deployment target affects reliability, scalability, cost, and how easy it is to share or extend your work.
In this article, we’ll break down common deployment targets: local, on-premises, cloud (VMs and serverless), hybrid setups… and touch on containers. The goal is to help vibe coders consider deployment early, even before writing the first line of code.
1. Local deployment: your laptop or desktop
Running a prototype locally is the fastest way to start. You have full control, no setup in the cloud, and minimal friction.
But there are limits:
- Only one user or a small number of users can realistically access it.
- Dependencies and OS differences can make sharing difficult.
- Your prototype is tied to your machine; if it fails, it’s harder to reproduce.
🟢 Pre-prototype habit:
Decide early whether your prototype will stay local for testing only or eventually need to scale. Plan what will need to change when moving off your laptop (dependencies, paths, credentials).
2. On-premises deployment: servers you control
On-prem deployment uses your own servers or organization’s hardware. It provides more control than local deployment and is often required for sensitive data.
Considerations:
- Setup and maintenance can be time-consuming.
- Scaling often requires purchasing or configuring new hardware.
- Network accessibility and security are your responsibility.
🟢 Pre-prototype habit:
If on-prem is a possibility, define hardware requirements and access policies before building. Know how you’ll handle updates, backups, and scaling manually or semi-automatically.
3. Cloud deployment: VM vs serverless
Cloud deployment offers flexible, scalable options:
VMs (Virtual Machines):
- Full OS control, similar to on-prem.
- Can host multiple services or databases.
- You manage updates, scaling, and resource allocation.
Serverless:
- You only pay for execution; infrastructure is abstracted.
- Automatically scales with demand.
- Limited runtime and resource constraints.
Cloud makes it easy to scale prototypes or share them globally but introduces network, cost, and security considerations.
🟢 Pre-prototype habit:
Before coding, choose a cloud model that matches expected use: VMs for more control, serverless for lightweight, event-driven prototypes. Sketch rough cost and scaling considerations.
4. Hybrid deployment: mixing targets
Some prototypes benefit from a hybrid approach: part local/on-prem, part cloud. For example, sensitive processing may stay on-prem while AI inference uses serverless cloud functions.
Benefits: flexibility, performance, compliance. Drawbacks: complexity in networking, deployment automation, and monitoring.
🟢 Pre-prototype habit:
If you anticipate a hybrid setup, map which components run where, and note dependencies, authentication, and data flow between environments.
5. Containers: portability and repeatability
Containers (e.g., Docker) package your application, dependencies, and environment together. They make it easier to move a prototype from local → cloud → on-prem without breaking.
Key points:
- Containers help standardize environments, reducing “works on my machine” issues.
- Orchestrators like Kubernetes add scaling and deployment automation.
- Learning curve exists, but even a single Dockerfile can save hours in debugging and onboarding.
🟢 Pre-prototype habit:
Decide whether your prototype would benefit from containers. Even a basic Dockerfile for your app ensures it can run consistently across environments.
6. Quick pre-prototype checklist
| Checklist Item | Why It Matters |
|---|---|
| Choose initial deployment target | Guides architecture, dependencies, and sharing options |
| Define scaling and user expectations | Prevents surprises if more users arrive |
| Plan for sensitive data or compliance | Determines on-prem, hybrid, or cloud requirements |
| Decide on containers | Ensures portability and environment consistency |
| Map component locations in hybrid setups | Prevents network and dependency issues later |
| Sketch costs and monitoring needs | Avoids unexpected bills or downtime |
Closing note
Deployment choices influence how your prototype grows, scales, and survives in the real world. Thinking about targets and containers before coding helps you design flexible, shareable, and resilient AI prototypes.
