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Batch LLM API Calls Without Overloading Your Budget

Quick start: batch work, not uncontrolled requests

Processing 10,000 support tickets or generating embeddings for a document collection is not an interactive chat workload. You need predictable throughput, recoverable failures, and a spending limit. The fastest reliable approach to batch processing with LLM APIs is usually a queue feeding a small, bounded pool of concurrent requests.

59API is a low-cost, pay-as-you-go relay for Claude and GPT models, offering native official-quality models without downgrades. Its compatibility with OpenAI SDKs, Claude Code, and Codex makes it useful for developers who want economical model access without rebuilding their tooling.

1. Choose the right kind of batch processing

Client-side batching means your application schedules many ordinary API requests. You control concurrency, retries, and persistence, and results arrive individually. This is the approach covered below.

Provider-managed batch APIs accept a submitted job and return results asynchronously, sometimes with different pricing or completion windows. Do not assume a relay supports those endpoints or passes through their discounts. Confirm 59API's current endpoint support before designing around a provider-specific batch feature.

2. Make every task independently recoverable

Start with a JSONL file or database table containing one task per record. Each record should include a stable task ID, input, requested model, prompt version, and status. Keep the original input separate from the generated output.

• Use explicit states: pending, running, succeeded, and failed.
• Persist results immediately: save each completed response rather than waiting for the entire job.
• Track attempts: record timestamps, errors, token usage, and any request identifier returned by the API.
• Resume selectively: restart unfinished tasks instead of rerunning successful ones.

Configure your SDK to use 59API's endpoint at https://api.59api.com, following its documented SDK base-path requirements. Load the API key from an environment variable or secret manager, and use a currently supported model identifier rather than guessing an alias.

3. Set concurrency from rate limits, not ambition

Concurrency controls how many requests are in flight. It does not, by itself, enforce requests per minute or tokens per minute. Use both a concurrency cap and a paced rate limiter.

Suppose your documented limits are 120 requests per minute and 60,000 tokens per minute. If each task consumes approximately 1,500 counted tokens, your token budget allows roughly 40 tasks per minute, even though the request limit permits more. Actual accounting rules may include reserved output tokens, so check how your endpoint calculates usage.

Start below that ceiling: 30 tasks per minute, with three concurrent requests if average latency is six seconds. In Python, an asyncio.Semaphore can cap in-flight calls; a separate limiter should pace request starts. Feed workers through a bounded queue instead of creating thousands of waiting tasks.

4. Retry carefully and preserve completed work

Retry temporary network failures, rate-limit responses, and appropriate server errors with capped exponential backoff and random jitter. Honor Retry-After when present. Do not repeatedly retry authentication errors, invalid model names, or malformed requests.

Set a finite timeout and attempt limit, such as three attempts per task. After exhaustion, move the task to a failure queue for inspection. A timeout does not prove the server did no work: retrying can duplicate inference and charges. Use idempotency features only where documented, and deduplicate any downstream writes or tool actions.

5. Measure cost before scaling

Run a representative 100-task pilot. Measure success rate, latency, input and output tokens, retry frequency, and cost per successful result. Route straightforward classification to a smaller model when quality checks support it; reserve stronger models for difficult cases. Set output limits and validate structured responses before accepting them.

59API's inexpensive pay-as-you-go access makes these experiments accessible, while its referral rebate offers an additional savings opportunity under the applicable terms. If you are ready to test the workflow, consider signing up for 59API and running a small pilot before increasing throughput.

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