OpenAI Batch API: The English Vocabulary You Need
Master the English vocabulary for OpenAI Batch API: asynchronous processing, request queuing, throughput, latency trade-offs, and cost optimisation terms explained for IT professionals.
The OpenAI Batch API lets you send large volumes of LLM requests asynchronously and receive results later — at a lower cost. But to use it effectively, or to discuss it with your team, you need to know the specific English vocabulary around batch processing, asynchronous systems, and API cost management. This guide covers the key terms with real examples from engineering conversations.
Core Batch Processing Vocabulary
Batch (noun/verb)
A batch is a group of items processed together as a single unit, rather than one by one. As a verb, “to batch” means to group requests for grouped processing.
“Instead of sending each prompt individually, we batch them overnight and pull the results in the morning.”
In IT collocations: submit a batch, process a batch, batch size, batch job, batch upload.
Asynchronous Processing
Asynchronous processing (often shortened to “async”) means tasks are submitted and executed independently — the caller does not wait for the result. The OpenAI Batch API is asynchronous: you submit a file of requests and poll for results later.
Contrast with synchronous requests, where the caller blocks and waits for each response before continuing.
“We switched to asynchronous processing to avoid blocking the main thread while waiting for LLM responses.”
Request Queue
A request queue is a data structure (or system component) that holds incoming tasks waiting to be processed. Batch systems typically place submitted jobs into a queue and process them in order.
“The batch job sits in the request queue — expected completion is within 24 hours.”
Throughput
Throughput is the number of requests, operations, or units a system processes per unit of time (e.g., requests per second, jobs per hour). Higher throughput means the system handles more work overall.
“The Batch API gives us much higher throughput than the real-time endpoint, since OpenAI processes batches when their servers have spare capacity.”
Latency
Latency is the delay between submitting a request and receiving a response. Batch APIs trade higher latency (hours instead of milliseconds) for lower cost and higher throughput.
“For our nightly data enrichment pipeline, high latency is acceptable — we just need the results by morning.”
File and Payload Vocabulary
JSONL (JSON Lines)
JSONL (pronounced “JSON Lines”) is a file format where each line is a valid, self-contained JSON object. The OpenAI Batch API uses .jsonl files to submit requests and return results.
“Prepare your batch as a JSONL file — one request object per line.”
Payload
The payload is the data content of a request or response — the actual information being sent, as opposed to headers or metadata.
“Each line in the JSONL file contains a payload with the model, messages, and a custom ID.”
Custom ID
A custom ID is a unique identifier you assign to each request in a batch, so you can match results back to the original inputs when the batch completes.
“Always include a meaningful custom ID — otherwise matching 10,000 results to their inputs becomes a nightmare.”
API Mechanics and Cost Vocabulary
Rate Limit
A rate limit is a restriction on how many requests you can make in a given time window. Batch APIs typically have separate, more generous rate limits than real-time endpoints.
“We hit the rate limit on the chat completions endpoint, so we migrated the bulk jobs to the Batch API.”
Cost per Token / Token Pricing
LLM APIs charge per token — each unit of text processed. Batch API pricing is typically 50% lower than real-time pricing for the same model.
“Switching to the Batch API cuts our token costs in half for non-urgent inference jobs.”
Polling
Polling is the practice of repeatedly checking a system for a status update at regular intervals. Since the Batch API is asynchronous, you poll the batch status endpoint until the job completes.
“Our script polls the batch status every 5 minutes and downloads the output file once it shows
completed.”
Webhook (alternative to polling)
A webhook is a callback mechanism: instead of polling, the server sends an HTTP request to your endpoint when the job is done. The OpenAI Batch API uses polling, but webhooks are common in async systems generally.
Status and Lifecycle Vocabulary
| Term | Meaning |
|---|---|
validating | The API is checking the uploaded file format |
in_progress | The batch is being processed |
completed | All requests finished — output file is ready |
failed | The batch could not be completed |
expired | The batch was not completed within the 24-hour window |
cancelling / cancelled | You requested cancellation |
“We monitor the batch status and trigger a Slack alert if it transitions to
failedorexpired.”
Key Collocations
Learning these collocations will help you sound natural in English technical discussions:
- submit a batch — “We submit a batch every night at 23:00.”
- batch processing pipeline — “The batch processing pipeline runs on a scheduled cron job.”
- process in bulk — “It’s more efficient to process these prompts in bulk.”
- poll for results — “The worker polls for results every few minutes.”
- output file — “Download the output file once the status is
completed.” - token usage — “Monitor token usage per batch to keep costs predictable.”
- trade latency for cost — “We trade latency for cost on non-real-time workflows.”
- error rate — “Check the error rate in the output — some requests may have failed individually.”
Phrases for Code Reviews and Technical Discussions
When discussing batch API work with your team, these phrases come up often:
- “This endpoint doesn’t need a real-time response — let’s move it to the Batch API to reduce costs.”
- “Make sure every request in the JSONL file has a unique custom ID, otherwise we can’t reconcile the results.”
- “The batch expired — we need to add a retry mechanism for failed or expired jobs.”
- “What’s the expected turnaround time on this batch? If it’s more than 24 hours, we need a fallback.”
- “We should validate the JSONL format before uploading — malformed files cause the entire batch to fail.”
Practice
Take a batch processing feature you have worked on (or are planning to build) and write a short technical proposal in English — 3 to 5 sentences — explaining why you would use the OpenAI Batch API instead of the real-time endpoint. Use at least 5 terms from this guide: batch, asynchronous, throughput, latency, token pricing, polling, custom ID. Then read it aloud — pronunciation practice matters as much as writing.