When teams plan a Claude integration, they usually picture the interactive case: a user asks, the model answers, someone is watching the spinner. But a large share of enterprise LLM work has no one waiting at all — classifying last quarter's tickets, summarizing a backlog of contracts, enriching a product catalog. Treating those two shapes of work the same way wastes money and engineering effort. The first architectural sorting question for any Claude workload is simple: does a human need this answer now?
Two modes, two sets of rules
Real-time (synchronous) requests are the default Messages API pattern: send a request, get a response in the same connection. Latency matters, so you stream responses for anything long, keep prompts lean, and often choose faster models such as Claude Haiku 4.5 for simple interactive tasks. Real-time traffic also drives your quota planning, because it spikes with user activity and cannot be smoothed.
Batch processing flips every one of those constraints. You submit a large set of requests, and results come back asynchronously — the work completes over hours rather than seconds. Nobody watches a spinner, so you can use the most capable model, enable extended thinking generously, and process volumes that would be unthinkable as live traffic. Anthropic's Batch API is the purpose-built vehicle for this: submit many requests as a job, poll or collect results when done.
The availability catch every architect should know
Here is the fact that surprises teams late in platform selection: the Batch API is not available on Amazon Bedrock or Google Vertex AI. As of July 2026, it is available on Claude Platform on AWS (the Anthropic-operated option with same-day API parity) and not available on Microsoft Foundry. If your architecture leans heavily on native batch processing and your organization is committed to Bedrock or Vertex AI, you need to know this before you commit, not after.
Teams on Bedrock or Vertex AI are not locked out of batch-shaped work — they simply build it themselves: a queue, a worker pool making ordinary synchronous requests at a controlled rate under their quota, and standard retry handling. This "self-managed batch" pattern works fine and is common. What you give up is the convenience of a managed job lifecycle, so budget some engineering time for queueing, rate control, and result collection. The full picture of what each platform supports is in the feature gaps article.
Sorting real workloads
Most enterprise use cases sort cleanly once you ask who is waiting.
| Workload | Mode | Why |
|---|---|---|
| Customer-facing chat or support assistant | Real-time, streamed | A person is waiting on every token |
| Document backlog summarization | Batch | Volume is high, deadline is soft |
| Email/ticket triage on arrival | Real-time (but not interactive) | Needs minutes, not milliseconds — a small queue works well |
| Nightly report generation | Batch | Runs on a schedule, nobody waits |
| Catalog or data enrichment | Batch | Rerunnable, throughput-bound work |
The middle row deserves a note: much "real-time" enterprise work is actually near-real-time. A triage pipeline that responds within two minutes is indistinguishable from instant for the business outcome, but far easier to operate — a short queue absorbs spikes and smooths your rate-limit consumption.
Cost and capacity implications
Batching helps costs in two ways even before any pricing differences: you can schedule heavy work off-peak so it does not compete with interactive traffic for quota, and you can right-size models deliberately per job instead of defaulting to one model everywhere. It also simplifies capacity planning — batch volume is predictable, so the quota headroom you negotiate with your platform (see quotas and rate limits) can be sized to your interactive peak rather than your total volume.
The practical takeaway for a platform decision: inventory your intended workloads, mark each batch or real-time, and check the batch column against your platform shortlist. Five minutes with that table prevents an unpleasant discovery in month three.
Where to go next
Platform feature differences matter beyond batch — read what Bedrock and Vertex don't support before committing, and streaming responses for the real-time side of the house. The feature matrix has the summary view.