Claude Batch API: Process Thousands of Requests
at 50% Lower Cost

What Is the Claude Batch API?

The Claude Message Batches API is Anthropic's asynchronous processing endpoint for high-volume workloads. Rather than making synchronous requests that return a response immediately, you submit a batch of up to 10,000 independent requests in a single API call, receive a batch ID, and poll for results — or wait for a webhook — when processing is complete. In exchange for this asynchronous model, Anthropic charges 50% of the standard per-token price.

This is not a marginal cost reduction. It is a structural 50% discount on every token processed. For enterprise teams running Claude batch API workloads at scale — document processing pipelines, nightly data enrichment, large-scale content analysis, bulk model evaluation — this changes the economics of Claude deployment fundamentally. A workflow that costs $10,000 per month at standard prices costs $5,000 via the batches API. At $100,000 per month scale, that is $50,000 in monthly savings with no change in output quality.

The batch API is available for all Claude models — Opus 4.6, Sonnet 4.6, and Haiku 4.5 — and is accessible via the same Claude API credentials you already use. There is no separate product to procure or new access to request. If you are currently running synchronous batch-style workloads — looping through thousands of requests sequentially — you are leaving significant money on the table by not switching to the batches endpoint.

How the Claude Batch API Works

The batch API follows a simple submit-and-poll pattern. You construct a list of message requests — each structured identically to a standard /v1/messages API request — and submit them together as a single batch. Each request in the batch is assigned a custom ID that you define, which allows you to match inputs to outputs when results are available. Anthropic returns a batch ID and a status endpoint. You poll that endpoint at intervals (or receive a webhook notification) until the batch status transitions to ended, at which point you retrieve the results file.

Each request in a batch is processed independently. There is no shared context between requests in the same batch — each is a fully self-contained conversation. This is important for use case design: batch processing is for workloads where each unit of work is independent, not for sequential workflows where the output of one request becomes the input to the next.

Implementation Pattern: Python

import anthropic
import json

client = anthropic.Anthropic()

# Build your batch requests
requests = []
for i, document in enumerate(documents_to_process):
    requests.append({
        "custom_id": f"doc-{i}",
        "params": {
            "model": "claude-sonnet-4-6",
            "max_tokens": 1024,
            "messages": [
                {
                    "role": "user",
                    "content": f"Classify the following document and extract key entities:\n\n{document}"
                }
            ]
        }
    })

# Submit the batch (up to 10,000 requests)
batch = client.messages.batches.create(requests=requests)
print(f"Batch submitted: {batch.id}")
print(f"Status: {batch.processing_status}")

# Poll for completion (in production, use webhooks instead)
import time
while batch.processing_status == "in_progress":
    time.sleep(60)
    batch = client.messages.batches.retrieve(batch.id)
    print(f"Status: {batch.processing_status}")

# Retrieve and process results
for result in client.messages.batches.results(batch.id):
    if result.result.type == "succeeded":
        output = result.result.message.content[0].text
        print(f"ID: {result.custom_id} → {output[:100]}...")

Best Enterprise Use Cases for Batch Processing

The batch API is not appropriate for every Claude workload — but for the workloads it suits, it is unambiguously the right choice. The common characteristic of batch-suitable workloads is that the individual processing units are independent, results are not needed in real time, and volume is significant enough that cost matters.

Document Processing Pipelines

Legal, compliance, and finance teams regularly need to process large volumes of documents: contract review, regulatory filing analysis, invoice extraction, policy document classification. These workloads run on fixed schedules or triggered by bulk uploads — not in response to individual user requests. Submitting the entire batch overnight and collecting results in the morning is a natural fit. A team that currently processes 2,000 contracts per month via synchronous calls cuts that workload's API cost from approximately $X to $X/2 by switching to batch — no change to the prompts, models, or output quality required.

Data Enrichment and Categorisation

CRM enrichment, product catalogue categorisation, customer feedback tagging, and log analysis all involve applying a consistent Claude prompt to thousands of records that have been accumulated in a database. These are nightly or weekly batch jobs by nature. Running them through the batch API is the obvious architecture — and the cost savings compound over time as data volumes grow. This is one of the most common workloads we migrate when working with clients on Salesforce, Snowflake, and data warehouse integrations.

Large-Scale Model Evaluation and Testing

Teams running Claude evaluation frameworks need to run hundreds or thousands of test cases across their prompt variants. These evaluation runs are almost always batch workloads — the evaluation happens offline, results are analysed in aggregate, and speed is secondary to cost. Running evaluations via the batch API can cut evaluation infrastructure costs by 50%, which matters when you are running daily or weekly evals as part of a CI/CD pipeline for AI-assisted features.

Bulk Content Generation

Marketing and content teams generating product descriptions, metadata, localised content variants, or SEO-optimised summaries for large catalogues benefit from batch processing. Generating 10,000 product descriptions synchronously involves managing concurrency, rate limiting, and retry logic. The batch API abstracts all of this — you submit the full dataset, Anthropic handles rate management internally, and you collect the complete output set when done.

Combining Batch API with Prompt Caching

The Claude batch API delivers maximum cost efficiency when combined with prompt caching. Prompt caching allows Claude to cache the processed representation of a system prompt or large context block, so that subsequent requests using the same cached content incur a lower per-token cost on the cached portion. When you are processing 10,000 documents with the same system prompt (which is the typical batch scenario), combining prompt caching with batch processing reduces your effective cost to approximately 20-30% of the standard synchronous rate — a 70-80% overall cost reduction.

The implementation is straightforward: mark your system prompt as cacheable using the cache_control parameter, include it in every request in the batch, and allow Anthropic's caching layer to handle the rest. The cache is populated on the first few requests in the batch and then reused across the remaining requests. Our detailed implementation guide is in the Claude prompt caching implementation article.

Constraints and Considerations

The batch API's 50% cost reduction comes with clear tradeoffs that need to be understood before designing a workflow around it. The primary constraint is latency: batches are designed for workloads that can wait up to 24 hours for results. In practice, most batches complete in 1-6 hours depending on volume and current platform load, but you cannot rely on a specific completion time for time-sensitive processing. Any workflow with a hard latency SLA shorter than a few hours should use the synchronous API.

The second constraint is inter-request independence. Each request in a batch is processed in isolation — there is no shared state or streaming context between requests. Agentic workflows where Claude's output in one step feeds into the next step cannot use the batch API for the sequential part of the workflow. You can use batch processing for the first stage (parallel classification or enrichment) and synchronous processing for the second stage (sequential agent reasoning), but mixing within a single dependent chain is not supported.

Finally, the batch API does not support streaming. If your use case involves a downstream consumer that reads Claude's response token-by-token as it is generated, the batch API cannot serve that use case. Read the Claude streaming vs batching guide for a full decision framework on when to use each approach.

Getting Started with the Batch API

Migrating an existing synchronous workload to batch processing requires three changes: restructuring your request loop to build a list of requests rather than sending them one at a time, adding polling or webhook handling to collect results asynchronously, and mapping results back to inputs using the custom IDs. For a well-structured existing workflow, this is a one to two day engineering effort that delivers immediate and permanent cost reduction.

For new workloads, design for batch processing from the start if the use case allows it. Identify the jobs in your data pipeline that run on a schedule or are triggered by bulk events rather than individual user actions. These are your batch candidates. Model the cost at your expected volume using the 50% discount, and size your infrastructure accordingly — batch processing often allows you to run workloads that would be too expensive at standard pricing.

The full batches API documentation covers authentication, request limits, result file format, error handling, and webhook configuration. Our Claude API integration service includes batch architecture design as a standard component, ensuring that your high-volume workloads are built on cost-optimal foundations from day one rather than refactored later.

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