Developers often struggle with slow response times and high network costs when sending thousands of separate API calls. The Batch API addresses this by combining multiple independent requests into one operation, reducing latency, bandwidth usage, and connection overhead.

This article explains what Batch API is, how it differs from standard APIs, and how OneRouter’s Batch API enables large-scale asynchronous inference through structured JSONL input and reliable error tracking. It also outlines key efficiency factors such as cost, latency, and throughput, and provides a concise guide to implementation and monitoring.

What is Batch API ?

Batch processing is a powerful approach for handling large volumes of requests efficiently. Instead of processing requests one at a time with immediate responses, batch processing allows you to submit multiple requests together for asynchronous processing. This pattern is particularly useful when:

• You need to process large volumes of data

• Immediate responses are not required

• You want to optimize for cost efficiency

• You're running large-scale evaluations or analyses

Batch processing (batching) allows you to send multiple message requests in a single batch and retrieve the results later (within up to 24 hours). The main goals are to reduce costs by up to 50% and increase throughput for analytical or offline workloads.

Key Difference Between Batch API and Standard API

Normal Request

 Client
 │──► Request 1 (/user/1)
 │       └──► Server Response 1
 ├──► Request 2 (/user/2)
 │       └──► Server Response 2
 └──► Request 3 (/order)
         └──► Server Response 3

Batch Request

Client
 └──► Single Request (/batch)
          ├─ Sub-request 1: GET /user/1
          ├─ Sub-request 2: GET /user/2
          └─ Sub-request 3: POST /order
          ↓
       Server processes all
          ↓
       Combined Response:
          [Result1, Result2, Result3]

Batch API can help:  

• Reduce network latency by sending one combined request instead of many.

• Lower bandwidth and connection overhead, since headers and handshakes are shared.

• Improve client performance, especially on mobile or slow networks.

• Simplify transactional logic, enabling unified error handling or rollback.

• Optimize API Gateway throughput, preventing request flooding.

  
  

Typical Use Cases of Batch API

Key Factors Affecting Batch API Efficiency

How much cost can Batch APIs save compared to real-time APIs?

Industry analysis (Growth-onomics) shows cost reductions of about 20–45%, mainly from fewer network round trips, lower connection overhead, and concentrated processing, though exact savings depend on call frequency, batch size, and system design.

What about latency? can Batch APIs really finish “within 24 hours”?

Batch APIs usually run asynchronously with much higher latency than real-time APIs; many systems execute hourly or daily, so “within 24 hours” depends on the SLA rather than being guaranteed.

Why are Batch APIs better for high-throughput workloads?

By aggregating thousands of requests into one process, Batch APIs reduce per-call overhead and allow parallel execution or caching reuse, often improving throughput by 17–92% in large-scale operations, though this comes at the cost of higher latency.

How to Use Batch API？

OneRouter's Batch API is highly compatible with OpenAI’s interface, so existing code can be reused with minimal changes. It accepts.

Try OneRouter‘s Batch API Service Now!

What Endpoints Does OneRouter Provide for Batch API Operations?

Batch API consolidates many small requests into one efficient workflow. By using OneRouter's Batch API, developers can cut network costs by up to 45%, scale throughput for up to 50,000 requests per batch, and simplify error handling through built-in logging and retrieval endpoints. While it sacrifices real-time speed, it delivers exceptional efficiency for bulk inference, synchronization, and data-processing workloads.

OneRouter provides a unified API that gives you access to hundreds of AI models through a single endpoint, while automatically handling fallbacks and selecting the most cost-effective options. Get started with just a few lines of code using your preferred SDK or framework.