Claude API Batch Processing for Large Documents

What "batch processing large documents" actually means for Claude

If you're searching for this, you probably have hundreds or thousands of PDFs, contracts, support tickets, or transcripts that need to go through Claude — for summarization, extraction, classification, or Q&A — without a human clicking a button for each one. The core challenge isn't calling the API once; it's doing it reliably at scale: staying under context limits, avoiding rate-limit failures, controlling cost, and making sure one bad document doesn't kill the whole job.

The short answer: you split large documents into chunks that fit comfortably inside Claude's context window, you process those chunks concurrently but within rate limits, you track partial failures and retry them individually, and you aggregate results afterward. There's no single "batch endpoint magic switch" — it's an engineering pattern you build around the standard Messages API. Below is exactly how to build it.

Step 1: Decide how to split the document

Claude models have large context windows (up to 200K tokens depending on the model), so many documents don't need splitting at all. But if you're processing legal contracts, research papers, or transcripts that exceed that, or if you want to process many documents in parallel cheaply, chunking still matters.

Practical chunking rules:

• Chunk by semantic boundary, not fixed character count. Split on headings, paragraphs, or page boundaries when possible. Fixed-size chunking mid-sentence hurts extraction accuracy.
• Overlap chunks by 10–15% so context isn't lost at boundaries (e.g., a clause that spans two chunks).
• Keep each chunk well under the model's max context, leaving room for your system prompt, instructions, and the expected output length.
• Attach metadata to each chunk (document ID, page range, chunk index) so you can reassemble results in order later.

function chunkText(text, maxChars = 12000, overlap = 1500) {
  const chunks = [];
  let start = 0;
  while (start < text.length) {
    const end = Math.min(start + maxChars, text.length);
    chunks.push(text.slice(start, end));
    start = end - overlap;
  }
  return chunks;
}

Step 2: Process chunks with controlled concurrency

Sending 500 requests at once will trip rate limits immediately. Sending them one at a time is slow and wastes money on idle time. The right pattern is a worker pool with a fixed concurrency limit (typically 5–20 concurrent requests depending on your plan and model).

async function processInBatches(items, worker, concurrency = 8) {
  const results = [];
  let index = 0;

  async function runWorker() {
    while (index < items.length) {
      const current = index++;
      try {
        results[current] = await worker(items[current], current);
      } catch (err) {
        results[current] = { error: err.message, item: items[current] };
      }
    }
  }

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

This pattern keeps throughput high while bounding the number of in-flight requests. Each worker catches its own errors so one failed chunk doesn't abort the batch — you just mark it for retry.

Step 3: Build retry logic for rate limits and timeouts

At scale, you will hit 429s and occasional 5xx errors. Treat these as expected, not exceptional:

async function callWithRetry(fn, retries = 4) {
  for (let attempt = 0; attempt <= retries; attempt++) {
    try {
      return await fn();
    } catch (err) {
      const status = err.status || err.response?.status;
      if (attempt === retries || (status !== 429 && status < 500)) throw err;
      const delay = 1000 * Math.pow(2, attempt) + Math.random() * 300;
      await new Promise((r) => setTimeout(r, delay));
    }
  }
}

Exponential backoff with jitter is enough for almost all batch jobs. If you're still hitting rate limits consistently, lower your concurrency rather than increasing retry counts — retries just delay the inevitable.

Step 4: Aggregate and reconcile results

Once chunks are processed, you need to stitch outputs back together in document order using the metadata you attached in Step 1. For summarization or extraction tasks, a common pattern is a two-pass approach:

1. Pass 1 — summarize or extract structured data from each chunk independently.
2. Pass 2 — feed all chunk-level outputs (not the raw document) back into Claude in a single call to produce a final consolidated summary or merged JSON object.

This keeps the second pass cheap because chunk summaries are much shorter than the original document, and it avoids re-sending the full document multiple times.

Step 5: Control cost and track usage per job

Batch jobs can get expensive fast if you're not tracking token usage per document. At minimum, log input/output tokens per chunk and per job so you can catch runaway documents (scanned PDFs with OCR noise are a common culprit — they bloat token counts) before they bloat your bill.

If you're running batch jobs as part of a product rather than a one-off script, this is where routing requests through a managed API layer pays off. SubToAPI turns your existing Claude access into a standard HTTPS API with per-key usage metadata, so you can see exactly how many tokens a batch job consumed, issue separate sub_live_... keys per job or team, and avoid building your own usage-tracking dashboard. Setup is in the quickstart docs, and the Messages API reference covers request formatting if you're adapting an existing batch script.

Putting it together

A production-grade batch pipeline for large documents looks like:

1. Extract text from source files (PDF/OCR/HTML as needed).
2. Chunk by semantic boundary with overlap.
3. Process chunks through a concurrency-limited worker pool.
4. Retry failed chunks with exponential backoff.
5. Reconcile chunk outputs in document order.
6. Run a consolidation pass if a single final output is needed.
7. Log token usage per document and per job.

None of this requires special "batch" infrastructure from Anthropic — it's standard distributed-systems thinking applied to API calls. The failure modes you need to design for are rate limits, partial failures, and cost blowouts, in that order.

Questions

Does Claude have a native batch API endpoint? Anthropic's Messages API is request/response based; batch processing is a pattern you build on top of it using chunking, concurrency control, and retries, not a separate endpoint you call once for thousands of documents.

How large can a single document be before I need to chunk it? It depends on the model's context window (up to 200K tokens), but you should leave headroom for your prompt and expected output. If a document plus instructions exceeds roughly 80% of the context limit, chunk it.

What's the biggest mistake in batch document pipelines? Sending all requests at once without concurrency limits or retry logic. It causes cascading 429 errors and makes a single bad document stall the entire job instead of failing gracefully and continuing.