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LLM API Batches: A Safe Concurrency Quick Start

Processing 10,000 support tickets or document summaries does not require 10,000 simultaneous LLM requests. It requires a queue, bounded concurrency, and reliable bookkeeping. This quick-start guide shows how to build an LLM API batch pipeline that runs faster than sequential calls without turning rate limits and retries into unexpected costs.

1. Choose the right kind of batching

Client-side batching means your application queues many independent requests and processes several concurrently. It works with ordinary API endpoints. A provider batch API is different: you submit a job for asynchronous processing under separate availability, pricing, and turnaround rules. Do not assume a relay exposes every provider's batch endpoint or discount; confirm support first.

For a quick start, use client-side concurrency. Keep one logical task per request so you can validate, retry, and track each result independently. Combining unrelated tasks into one prompt can complicate output parsing and make partial failures expensive.

2. Configure one reusable API client

59API provides low-cost, pay-as-you-go access to multiple model families through an OpenAI-compatible interface. Use https://api.59api.com as the service base URL, checking its documentation for any endpoint-specific path required by your SDK. Store your API key in an environment variable, never in source control.

Create one reusable asynchronous client per worker process rather than opening a new client for every request. Configure connection pooling, request timeouts, and retry behavior explicitly. Check the SDK's built-in retries before adding your own, or one failed request may trigger several nested retry attempts.

For example, benchmark Gemini 2.5 Flash on straightforward extraction and Claude Haiku 4.5 on classification against your own quality requirements. 59API's low-cost access to native models without downgraded substitutes makes these workload comparisons practical; measure cost per accepted result, not just cost per request.

3. Size concurrency from both rate limits

Start with four concurrent requests. Increase only after measuring latency, token usage, and throttling. Your sustainable request rate is constrained by both requests per minute and tokens per minute.

• Example limits: 60 requests per minute and 60,000 tokens per minute.
• Estimated request size: 800 total input and output tokens, allowing approximately 75 requests per minute under the token budget.
• Effective ceiling: the lower limit, or 60 requests per minute.
• Observed latency: eight seconds per request suggests approximately eight in-flight requests could saturate that ceiling. Try six first to leave headroom.

These are illustrative limits, not published 59API quotas. Use your account's actual limits and confirm how tokens are counted. A semaphore limits simultaneous work, but does not enforce requests per minute. Pair it with a request-rate limiter and a token budget that reserves estimated usage before dispatch.

4. Build a bounded, resumable worker loop

Read records incrementally into a bounded queue rather than creating thousands of asynchronous tasks at once. Give every record a stable job ID. Each worker should acquire rate-limit capacity, send one request, validate the response, and persist the result before taking another job.

Record the job ID, model, prompt version, status, attempt count, latency, token usage, and error category. On restart, skip completed IDs and resume unfinished work. If downstream consumers need original ordering, store an input index and sort results afterward; completion order will vary.

5. Retry selectively and protect your budget

Retry throttling and transient server failures with capped exponential backoff and random jitter. Honor Retry-After when provided. For a starter policy, allow three total attempts, begin around one second, and cap waits at 30 seconds. Authentication failures and invalid parameters need fixes, not repeated requests.

A timeout does not prove the provider stopped processing. Retrying may duplicate work and charges. Use idempotency features only where explicitly supported, and deduplicate stored results by job ID. Apply a sensible output-token cap and stop dispatching when your run's spending threshold is reached.

6. Benchmark before scaling

Run 100 representative records first. Track accepted results per minute, p95 latency, retry rate, and total cost including failures. Increase concurrency gradually, and reduce it when throttling rises. Scale worker processes only after accounting for their combined traffic.

If you want an inexpensive place to test this pipeline across model families, sign up for 59API and start with a small, measured batch.

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