Performance & Scalability Testing: Engineering for Vibe Coders

Making sure your prototype can handle real-world use

AI coding assistants make it easy to build prototypes that “just work” on your laptop. But what happens when more users arrive, requests come faster, or data grows bigger than expected? Without thinking about performance and scalability, prototypes can collapse under even modest load.

In this article, we’ll cover load testing, stress testing, and capacity planning… and show how vibe coders can start planning for performance before writing a single line of code.

1. Why performance matters for vibe coders

Vibe coders often focus on features and speed of creation. That works well for a small demo, but performance problems usually appear only after a prototype sees real or simulated usage.

Even simple AI prototypes can become slow or unresponsive if they:

• Query large datasets inefficiently
• Generate multiple simultaneous AI requests
• Process large files without limits

🟢 Pre-prototype habit:

Before building, ask yourself: who will use this, and how many requests could happen at once? Sketch a rough “expected load” scenario to guide design decisions.

2. Load testing: How much can your prototype handle?

Load testing measures how your system behaves under expected usage. You simulate multiple users, concurrent requests, or batch jobs to see if your prototype keeps up.

Key observations include:

• Average response times
• Error rates under typical load
• Resource usage (CPU, memory, network)

🟢 Pre-prototype habit:

Decide early what “normal usage” looks like for your prototype. Even rough numbers (10 users, 50 requests per minute) help you identify potential bottlenecks before coding.

3. Stress testing: Finding the breaking point

Stress testing intentionally pushes your prototype beyond its limits to see how it fails. It answers questions like:

• How many concurrent requests make the system slow or crash?
• How does the system recover after overload?
• Which components fail first under stress?

Stress testing is about understanding boundaries, not fixing everything immediately.

🟢 Pre-prototype habit:

Identify critical functions that must keep running, even under extreme conditions. Decide which failures are acceptable during testing and how you will observe them.

4. Capacity planning: Planning for growth

Capacity planning is the forward-looking side of performance testing. It helps you answer:

• When will your prototype need more resources?
• Which components are likely to become bottlenecks first?
• How much headroom should you leave for unexpected spikes?

For AI-powered prototypes, consider API rate limits, memory-intensive model calls, and data storage growth.

🟢 Pre-prototype habit:

Sketch a simple growth projection. Ask: what happens if user count doubles? How will latency change? This will guide architectural choices even before building.

5. Simple steps for vibe coders

Even if your prototype is small, these habits make performance issues visible early:

• Measure response times for all API calls
• Limit input sizes to reasonable defaults
• Use caching or batching where possible
• Monitor memory and CPU usage in dev environments

🟢 Pre-prototype habit:

Plan basic logging and metrics collection before building. Even simple console logs or local dashboards help you detect bottlenecks during development.

6. Quick pre-prototype checklist

Checklist Item	Why It Matters
Define expected load	Guides design and testing scope
Identify critical functions	Prioritizes what must remain responsive
Set input size limits	Prevents crashes under heavy input
Plan monitoring/logging	Allows early detection of slowdowns
Sketch growth projections	Prepares for future scalability needs
Decide acceptable failure points	Helps during stress testing

7. Closing note

Performance and scalability testing is not about perfection… it’s about foresight. Even lightweight testing and planning give you confidence that your prototype can handle more than just your laptop or a single user.

🟢 Pre-prototype habit:

Think about load, stress, and growth before coding. A few minutes planning can prevent hours of debugging and re-architecting later. Build with boundaries in mind, then let your AI prototype shine under real-world conditions.