Sign in
Skip to solution
easyAI Engineering

When should you use batch APIs instead of real-time LLM calls?

107 views
01

Understand the problem

The 50% discount most pipelines ignore: async processing for anything without a user waiting.

batch-apicostasyncpipelines
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

Stuck? Beam a request up — the console returns a conceptual nudge that guides your logic without spoiling the implementation.

03

Study the solution

Batch APIs accept a file of requests and return results asynchronously (typically within hours) at roughly half the per-token price, with separate, much higher rate limits. Use them whenever no user is waiting: nightly enrichment, classification backfills, embedding-adjacent content generation, eval runs, report genera

Solution ready — 2 min read

Classified // press E to declassify

04

Read the code

Batch job lifecycle
// 1. build JSONL: one request per line, custom_id = idempotency key
const lines = tickets.map((t) => JSON.stringify({
  custom_id: "ticket-" + t.id,
  params: { model, max_tokens: 50,
            messages: [{ role: "user", content: classifyPrompt(t.text) }] },
}));

// 2. submit and record the batch id
const batch = await client.batches.create({ requests: lines });
await jobs.save({ batchId: batch.id, kind: "ticket-classify" });

// 3. on completion webhook/poll: apply successes, re-batch the failures
for (const r of await client.batches.results(batch.id)) {
  if (r.result.type === "succeeded") await applyLabel(r.custom_id, r.result);
  else failures.push(r.custom_id);
}
05

Join the discussion

Discussion (0)

Sign in to join the discussion.

No responses yet. Be the first to share what you think.

Transmission complete // awaiting log

KEEP THE
STREAK ALIVE.

Dossier 15 of 80 decoded in the AI Engineering track. One more won't hurt.

Back to track