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LLM API Batch Processing: Concurrency Patterns That Scale

Batch Work Is a Scheduling Problem

Batch processing with LLM APIs is rarely limited by a simple loop. Production workloads must balance request parallelism, token throughput, rate limits, retry behavior, cost, and downstream database capacity. The goal is not to submit the maximum number of requests at once. It is to complete a known workload quickly while keeping failures, duplicate work, and spend predictable.

First, separate the input collection from execution. Store each task with a stable ID, prompt version, model, status, attempt count, and result location. A durable queue or database table makes the job resumable after a worker restart. It also lets you rerun only failed items instead of paying to process an entire dataset again.

Use Bounded Concurrency, Not Unlimited Parallelism

A common mistake is creating one asynchronous request for every input. Ten thousand simultaneous requests can overwhelm local sockets, trigger provider limits, and make error recovery chaotic. Start with a fixed worker pool, such as 5 to 20 concurrent requests, then tune from observed latency and rate-limit responses.

Estimate a safe ceiling from both requests per minute and tokens per minute. If a model allows 600 requests per minute and typical calls take three seconds, 20 concurrent requests may be reasonable from a request perspective. However, long prompts or large outputs can make token throughput the actual constraint. Track input and output tokens separately because output generation usually determines how long each request occupies capacity.

• Use a semaphore or worker pool to cap in-flight requests.
• Keep separate pools for short classification jobs and long generation jobs.
• Set per-request timeouts so one stalled connection cannot block a worker indefinitely.
• Reduce concurrency automatically after repeated 429 or overload responses.

Batch Inputs Only When the Task Allows It

Combining multiple records into one LLM request can lower HTTP overhead and improve throughput, but it introduces parsing and quality risks. It works well for homogeneous tasks such as sentiment labels, entity extraction, or short product-tag generation. It is less suitable for long documents, high-stakes decisions, or prompts where one malformed item could corrupt the full response.

When grouping records, assign every item an ID and ask the model to return one result per ID in a deterministic order. Keep batches small enough that the output remains comfortably within the model context window. For example, a batch of 10 to 30 short support-ticket classifications is usually easier to validate than a batch of 200. Validate that every expected ID appears exactly once before marking the batch complete. Missing or invalid entries should return to the queue individually.

Make Retries Idempotent and Selective

Retries are essential, but indiscriminate retries multiply cost. Retry transient failures such as 429, 500, 502, 503, connection resets, and timeouts. Do not automatically retry invalid requests, authentication errors, malformed payloads, or context-length failures. Those require a code or data correction.

Use exponential backoff with jitter. A practical schedule begins near one second, doubles each attempt, and adds a small randomized delay. Jitter prevents many workers from retrying at the same instant after a shared outage. Limit attempts, record the final error, and route exhausted tasks to a review queue.

Idempotency matters when a timeout occurs after the provider has already completed work. Persist a task key before submission and ensure workers can safely resume without writing duplicate side effects. For generation jobs that update customer-facing content, save the model response first, then perform the downstream update in a separate, retryable step.

Optimize Cost Without Reducing Model Quality

Model routing should match task difficulty. Use a fast, economical model for extraction, tagging, routing, and validation; reserve stronger models for ambiguous reasoning, nuanced rewriting, or final synthesis. This approach preserves quality where it matters while reducing the average cost per item.

59API is useful for this pattern because it provides pay-as-you-go access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, alongside GPT models. Its OpenAI SDK compatibility means an existing client can generally point to https://api.59api.com while retaining the same queue, concurrency, and observability architecture. That flexibility makes it practical to compare model latency and cost per workload without rebuilding application integrations.

Measure the Right Production Signals

Log model, prompt version, queue delay, request latency, input tokens, output tokens, retry count, error type, and parsed-result validity. Review p50 and p95 latency rather than averages alone. A rising queue delay indicates insufficient capacity; a rising 429 rate indicates excessive concurrency; a rise in invalid structured output suggests prompt or model-routing regression.

Run a small calibration batch before launching a large job. Measure quality, tokens, and failure rate at several concurrency levels, then set a conservative initial limit. For developers scaling recurring LLM workloads, signing up for 59API can provide a low-cost relay option, including a referral rebate, while preserving compatibility with familiar OpenAI-style tooling.