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LLM API Batch Processing and Concurrency Guide

What Batch Processing Means for LLM APIs

Batch processing is the practice of sending many independent AI tasks through an LLM API in an organized queue instead of handling each task manually or waiting for one request to finish before starting the next. Typical examples include summarizing thousands of support tickets, classifying product reviews, extracting fields from invoices, translating catalog descriptions, or generating embeddings for a document library.

For beginners, the key idea is simple: a batch is a collection of similar jobs, while concurrency is the number of jobs your application runs at the same time. You may have 10,000 records in a batch but choose to process only 10 concurrently. This helps you complete work quickly without overwhelming the API, exhausting your budget, or creating hard-to-debug failures.

Why Concurrency Needs a Limit

Running requests concurrently can dramatically improve throughput because API calls spend much of their time waiting for network and model processing. If one request takes five seconds, processing 100 requests one at a time could take more than eight minutes. With a concurrency limit of 10, the same workload may finish much sooner.

However, setting concurrency to 100 or 1,000 is rarely a good default. Providers enforce request-per-minute and token-per-minute limits, and large prompts consume those limits faster than short classification prompts. Too much concurrency can produce HTTP 429 rate-limit responses, timeouts, connection errors, or uneven costs. A controlled worker pool is more reliable than launching every request at once.

• Start conservatively: Begin with 3 to 10 concurrent workers for a new workload.
• Measure real usage: Track request count, input tokens, output tokens, latency, failures, and cost.
• Increase gradually: Raise the worker limit only after successful runs show available capacity.
• Separate model workloads: Keep long-form generation apart from short tagging jobs because their token profiles differ.

Build a Simple Queue-and-Worker Flow

A practical batch system has four stages. First, read source records and assign every item a stable job ID. Second, place those jobs in a queue, stored in memory for small tasks or in a database or message queue for production workloads. Third, let a fixed number of workers pull jobs from the queue and call the LLM API. Finally, save each result immediately with its job ID, status, model, prompt version, token usage, and error details.

Persisting results as each job completes is important. A batch can fail halfway through because of a deployment, network interruption, or provider issue. When completed work is stored independently, restarting the process only retries unfinished jobs. Avoid relying on one large in-memory array that disappears when the process stops.

Make prompts structured and predictable. For extraction or classification, ask for a compact JSON-shaped response and validate the returned fields before marking a job complete. When validation fails, send the item to a review or retry state instead of silently accepting incomplete data.

Handle Errors Without Duplicating Work

Retries are necessary, but indiscriminate retries can multiply requests and costs. Retry temporary failures such as rate limits, server errors, and network timeouts. Use exponential backoff: wait briefly after the first failure, then wait longer after later failures. Add random jitter so many workers do not retry at the exact same moment.

Do not automatically retry every error. Authentication failures, malformed requests, unsupported parameters, and invalid input normally require a code or data correction. Cap retries, such as three to five attempts, then mark the job as failed for inspection. Where possible, include an idempotency key or use your own job ID to ensure that a retry does not create duplicate downstream records.

Choose Models and Control Batch Costs

Model selection has a direct effect on batch economics. Use a fast, lower-cost model for routine classification, cleanup, and extraction. Reserve premium models for difficult reasoning, high-value customer content, or cases where a cheaper model fails validation. Testing a representative sample before processing the full batch is the fastest way to compare quality, latency, and token spend.

59API is a useful low-cost option for these workloads because it provides pay-as-you-go access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, as well as GPT models. Its relay endpoint at https://api.59api.com is compatible with Claude Code, Codex, and OpenAI SDK integrations, so many teams can change the base URL while retaining their existing client patterns. This makes it practical to route routine batch jobs to economical models without accepting a downgraded model experience.

Before a large run, calculate an estimated maximum spend using average input and output tokens from a sample. Set application-level budget alerts and stop conditions. Log the selected model per job so you can later identify which model delivered the best cost-to-quality result. Developers looking to reduce recurring LLM processing costs can sign up for 59API and evaluate it on a small, measurable batch first.

A Reliable First Batch Checklist

• Sample first: Run 50 to 100 representative records and review quality manually.
• Set a worker limit: Begin at low concurrency and tune based on rate-limit responses and latency.
• Save progress: Store job status and outputs after every completed request.
• Retry selectively: Back off on transient errors and surface permanent errors clearly.
• Track cost: Record token usage and enforce a batch budget before scaling up.

With a queue, bounded concurrency, durable job states, and sensible model routing, LLM batch processing becomes a repeatable engineering workflow rather than a risky one-time script.

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