Batching Operations: Engineering for Vibe Coders

When building prototypes quickly you often need to process multiple items, make multiple API calls, or write multiple database entries. Sending each item individually can be slow, inefficient, and even costly. Batching operations lets you group work together so your prototype runs faster and behaves more predictably. This article explains batching, why it matters, and how to plan for it before you write code.

1. What batching really is

Batching is the practice of combining multiple operations into a single request or process. Instead of sending 100 separate API calls, you send one request with 100 items. Instead of writing 1,000 rows one at a time, you write them all at once in a batch. The goal is to reduce overhead, improve throughput, and make your prototype more efficient.

🟢 Pre‑prototype habit: Identify repeated operations in your prototype that could be grouped and estimate how much overhead batching could save.

2. Why batching matters for prototypes

Prototypes often prioritize speed of coding over speed of execution. That is fine until the prototype starts talking to multiple services or handling large volumes of data. Without batching, every small operation adds network latency, CPU time, or database load. Batching reduces that cost and prevents your prototype from feeling slow or unresponsive.

🟢 Pre‑prototype habit: List all operations that will run frequently or in loops and note which ones could benefit from batching.

3. How batching works

At its simplest, batching groups multiple operations into one larger operation. This can happen at different layers:

• API batching: Send multiple requests in a single call.
• Database batching: Insert or update multiple rows together.
• Processing batching: Handle multiple items in memory before saving or sending them.

The result is fewer round trips, lower overhead, and higher throughput. It also makes monitoring and error handling easier, because you deal with one batch instead of hundreds of individual operations.

🟢 Pre‑prototype habit: Sketch how your data flows and where multiple items are handled together versus individually.

4. When batching is a bad idea

Batching is not always better. Very large batches can cause memory spikes, long waits, or harder-to-handle failures. Sometimes sending items individually is better for latency-sensitive operations or when early partial results matter. Batching is a tradeoff: fewer requests, higher efficiency, but potentially higher impact if a batch fails.

🟢 Pre‑prototype habit: For each operation, estimate batch size limits that balance efficiency with reliability and partial result needs.

5. Handling failures in batches

Batching changes how you handle errors. If one item in a batch fails, do you retry the whole batch, only the failed items, or skip them? Your strategy depends on the type of operation and your prototype’s tolerance for incomplete results. Designing this before coding avoids tricky edge cases later.

🟢 Pre‑prototype habit: Decide your batch error handling strategy before implementing: full retry, partial retry, skip, or log for later review.

6. Common batching patterns

• Fixed-size batches: Group N items per batch.
• Time-window batches: Collect items for T seconds and send together.
• Hybrid batching: Use size and time limits together to balance throughput and latency.

Choose the pattern that fits your prototype’s needs and the behavior of the services or systems you are calling.

🟢 Pre‑prototype habit: Choose a batching pattern for each frequent operation and write it down before starting code.

7. Quick pre-prototype checklist

Checklist Item	Why It Matters
Identify repeated operations	Target batching opportunities early
Estimate batch sizes	Balance efficiency with memory and reliability
Define error handling	Prevent batch failures from breaking your prototype
Choose batching pattern	Ensure predictable throughput and latency

🟢 Pre‑prototype habit: Fill out this checklist for your prototype before writing any code to guide your batching strategy.

Closing note

Batching operations is a simple but powerful way to improve prototype performance and reliability. Thinking about it before coding helps you avoid slow, chatty, or fragile prototypes. A little planning can make your code faster, cleaner, and easier to debug.

See the full list of free resources for vibe coders!

Still have questions or want to talk about your projects or your plans? Set up a free 30 minute consultation with me!