Claude API Batch Processing Multiple Requests

If you're searching for how to handle Claude API batch processing, you're probably trying to do one of two things: send a large number of prompts without hitting rate limits, or process a queue of jobs (summaries, classifications, extractions) as efficiently as possible. The short answer is that "batch processing" with the Claude API almost always means controlled concurrency — sending multiple requests in parallel, within limits, with retries and backoff — rather than a single magic "batch" call that processes everything at once.

This article walks through the practical patterns for doing that: how to structure concurrent requests, how to avoid rate-limit errors, how to handle partial failures, and how to think about cost and latency trade-offs when you're processing hundreds or thousands of prompts.

Why "batch processing" is really concurrency management

The Claude API, like most LLM APIs, processes one request per call. There's no endpoint where you upload 500 prompts and get 500 completions back in one response. Instead, batch processing means:

1. Splitting your workload into individual requests
2. Sending several of them at the same time (concurrency)
3. Respecting rate limits (requests per minute, tokens per minute)
4. Retrying failures without duplicating work
5. Collecting and matching results back to their original inputs

Get these five things right and you can process large volumes reliably. Get them wrong and you'll see 429 errors, dropped results, or runaway costs.

Pattern 1: Worker pool with bounded concurrency

The most common and robust pattern is a worker pool — a fixed number of "slots" that pull jobs from a queue and process them in parallel. This avoids firing off thousands of requests simultaneously while still getting real throughput.

async function processBatch(items, handler, concurrency = 5) {
  const queue = [...items];
  const results = [];

  async function worker() {
    while (queue.length) {
      const item = queue.shift();
      try {
        const result = await handler(item);
        results.push({ item, result, ok: true });
      } catch (err) {
        results.push({ item, error: err.message, ok: false });
      }
    }
  }

  await Promise.all(Array.from({ length: concurrency }, worker));
  return results;
}

You then pass a handler function that makes the actual API call:

async function callClaude(prompt) {
  const res = await fetch("https://api.subtoapi.app/v1/messages", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: "claude-sonnet-4-5",
      max_tokens: 500,
      messages: [{ role: "user", content: prompt }]
    })
  });

  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  return res.json();
}

Start with a concurrency of 3–5 and increase gradually while watching for 429 responses. If you're building this against SubToAPI, usage metadata returned with each response (token counts, request IDs) makes it straightforward to log and reconcile a batch run — see /docs/messages for the exact response shape.

Pattern 2: Rate-aware queuing

If you know your account's rate limit (requests per minute or tokens per minute), you can pace requests instead of just bounding concurrency. A simple token-bucket or fixed-interval queue works well:

function createRateLimiter(requestsPerMinute) {
  const intervalMs = 60000 / requestsPerMinute;
  let lastCall = 0;

  return async function throttle() {
    const now = Date.now();
    const wait = Math.max(0, lastCall + intervalMs - now);
    lastCall = now + wait;
    if (wait > 0) await new Promise(r => setTimeout(r, wait));
  };
}

Call throttle() before each request. This smooths out bursts and keeps you under the limit even if your worker pool is large.

Handling partial failures and retries

With any real batch job, some requests will fail — timeouts, transient 5xx errors, or rate limits. Don't let one failure kill the whole run. Instead:

• Catch errors per-item, not per-batch
• Retry with exponential backoff (e.g., 1s, 2s, 4s, capped at 3–5 attempts)
• Log which items failed permanently so you can re-run just those
• Keep an index/ID mapping so results come back matched to inputs, even out of order

async function withRetry(fn, retries = 3) {
  for (let i = 0; i <= retries; i++) {
    try {
      return await fn();
    } catch (err) {
      if (i === retries) throw err;
      await new Promise(r => setTimeout(r, 2 ** i * 1000));
    }
  }
}

Cost and latency trade-offs

Higher concurrency finishes a batch faster, but it doesn't reduce total token cost — you're still paying per token across all requests. The real cost lever is prompt design: shorter system prompts, trimmed context, and setting a sensible max_tokens cap per request. If you're running the same batch job repeatedly, cache static parts of your prompt where you can, and only vary the per-item content.

For teams running recurring batch jobs — nightly classification runs, content pipelines, data enrichment — it's worth centralizing API access rather than scattering keys across scripts and services. SubToAPI issues scoped sub_live_ keys per application, so you can run a batch job under its own key, track its usage separately on the dashboard, and rotate or revoke it without touching other integrations. Check /pricing for plan details and /docs/quickstart to get a key generating requests in minutes.

Streaming vs. non-streaming for batches

For batch jobs, you almost always want non-streaming responses — you need the final output, not incremental tokens, and streaming adds connection overhead that doesn't help when you're running dozens of requests in parallel. Reserve streaming (see /docs/streaming) for interactive use cases like chat UIs, not bulk processing.

Practical checklist

• Use a worker pool with bounded concurrency (start at 3–5)
• Add a rate limiter if you know your requests-per-minute ceiling
• Retry transient failures with exponential backoff, cap retries
• Track request/result pairs by ID, not by array order
• Use non-streaming responses for batch jobs
• Set max_tokens deliberately — don't let a few long outputs blow your budget
• Log token usage per request to catch cost anomalies early

What's the difference between batch processing and parallel requests?

Parallel requests just means sending multiple calls at the same time. Batch processing is the broader workflow — splitting a large job into items, running them with controlled parallelism, retrying failures, and collecting results in order. Parallelism is the mechanism; batching is the process.

How many concurrent Claude API requests can I send?

It depends on your account's rate limits, which vary by plan and model. There's no universal number — start conservatively (3–5 concurrent requests), monitor for 429 responses, and scale up gradually based on what your account tolerates.

Does the Claude API have a dedicated batch endpoint?

Standard usage is one request per call; "batching" is implemented client-side with concurrency control, queuing, and retries as described above. If you need a single API key and simplified usage tracking across batch jobs, see /docs for setup details.