API Request Batching: Reduce Network Overhead and Latency

We spent three weeks chasing ghost latency in a dashboard service that was hitting our internal inventory API over 400 times per page load. The culprit wasn't inefficient database queries or bloated serialization; it was the sheer volume of individual HTTP requests hammering our gateway. We were drowning in TCP handshakes and TLS negotiation overhead.

If you’re building distributed systems, you’ve likely hit this wall. When your frontend or microservice layer makes hundreds of tiny, chatty calls to a downstream service, you aren’t just burning CPU cycles—you're murdering your network efficiency. Request batching is the most effective way to turn that chatter into a cohesive conversation.

Why API Request Batching Matters

At its core, request batching is about grouping multiple logical operations into a single physical request. Instead of sending 50 separate POST requests to fetch user metadata, you send one request with an array of IDs.

When we first tried to solve this, we naively stuffed everything into a single massive JSON blob. We quickly learned that the "one size fits all" approach leads to timeouts if the downstream service takes too long to process the entire collection. We had to move toward a more balanced design, keeping the batch size roughly between 20 and 50 items. This sweet spot kept our P99 latency stable while significantly reducing the number of round-trips.

Implementing Batching Patterns

There are two primary ways to approach this: the "Explicit Batch Endpoint" and the "Request Collator."

1. The Explicit Batch Endpoint

This is the most common approach in RESTful API design. You expose a dedicated endpoint, usually something like POST /v1/batch/users, that accepts an array of identifiers.

JSON
// Request payload
{
  "ids": ["user_123", "user_456", "user_789"]
}

The server processes these in a single transaction or a concurrent set of lookups and returns an aggregated response. It’s clean, predictable, and easy to document. However, it requires your clients to be "batch-aware."

2. The Request Collator (Middleware)

If you can't change the client-side code, you can implement a collator at your API gateway or a sidecar proxy. The gateway buffers incoming requests for a few milliseconds (e.g., 5-10ms), groups them, and forwards a single call to the downstream service.

This is much harder to implement correctly. You need to handle partial failures—what happens if 4 out of 5 items in the batch fail? You’ll need to return a 207 Multi-Status response, which is a standard HTTP way of saying "some of this worked, some of it didn't."

Managing Trade-offs and Risks

Batching isn't a silver bullet. You’re trading network overhead for increased complexity in error handling and potential cache fragmentation.

If you're using Idempotency keys: Making Retries Safe in Distributed Systems, remember that a batch request makes retries tricky. If the batch partially succeeds, retrying the whole batch might result in duplicate side effects for the items that actually processed the first time. You must ensure your batch processing logic is atomic or that your downstream services handle partial retries gracefully.

Also, don't forget to monitor your latency. While batching improves network efficiency, it can inadvertently increase the latency of an individual item if it gets stuck behind a slow request in the same batch. If you’re seeing tail latency issues, you might want to look into API Performance: How to Implement Request Hedging for Lower Tail Latency to hedge against those outliers.

Pragmatic Implementation Steps

1. Define a Maximum Batch Size: Start with a hard limit (e.g., 100 items). Reject requests that exceed it with a 413 Payload Too Large.
2. Use a Consistent Response Structure: Always return an object with a results array, even if the batch only contains one item. This prevents your client-side code from needing two different logic paths.
3. Implement Timeouts: Ensure your batch processing has a strict timeout. It's better to fail a batch of 50 than to hang the client for 30 seconds.
4. Monitor Throughput: Use tools like Prometheus or Datadog to track the average batch size. If your average size is 1.1, you’re adding complexity for no gain.

FAQ

Does batching break caching? It can. Standard HTTP caching works on unique URLs. If you batch requests into a POST, you lose the ability to use standard CDN caching. You might need to implement application-level caching or LLM Caching Strategies to Slash Latency and API Costs if your data is read-heavy.

How do I handle partial failures? Use 207 Multi-Status. Return an array of objects where each object contains the id, status_code, and data or error for that specific item.

Is it always better than individual requests? No. If the overhead of the batching logic exceeds the time saved on the network, you're over-engineering. Measure your current network latency first. If you're on a high-speed internal network, the gains might be negligible compared to the added maintenance burden.

I’m still not entirely convinced that complex collators are worth the effort for most teams. We’ve had much more success by forcing our frontend teams to use explicit batch endpoints. It keeps the system observable and makes debugging much simpler. Next time, I’d focus more on enforcing strict size limits at the schema level rather than trying to handle arbitrarily large batches in the backend logic.