8 LLM API Batch and Concurrency Tactics
1. Separate batch size from concurrency
Batch size is how many records your job intends to process; concurrency is how many API requests run at the same time. They solve different problems. A batch of 10,000 support tickets may be processed with only 10 concurrent LLM calls. Start by defining both values explicitly, then measure throughput, error rate, and cost. This prevents a common failure mode: launching thousands of requests simultaneously and triggering rate limits, timeouts, or runaway spending.
2. Build a bounded worker pool
Use a queue and a fixed number of workers instead of creating one task per input. For example, enqueue document chunks, then let eight to twenty workers pull jobs until the queue is empty. A bounded pool keeps memory predictable and makes it easy to tune throughput. Increase concurrency gradually: try 5, then 10, then 20, while watching latency and HTTP 429 responses. The best setting is rarely the largest number your runtime can launch.
3. Group small jobs only when the output can be separated safely
Combining several short classification tasks into one prompt can reduce request overhead, but only if you can reliably map each answer back to its input. Ask for a stable structure such as one result per numbered item, and include a unique ID for every record. Keep prompt instructions concise and set a maximum number of items per request based on expected input and output tokens. Do not batch unrelated long-form generation tasks together; one weak response can make the entire batch harder to validate and retry.
4. Make every request idempotent
Network failures do not always mean the model did not process the request. Store a job ID, input hash, model name, prompt version, and final result. Before retrying, check whether the job already completed. Idempotency is essential for extraction, billing-related workflows, and large overnight jobs because it prevents duplicate records and unnecessary token charges. It also lets workers resume after a deployment or machine restart without replaying the whole batch.
5. Retry with exponential backoff and jitter
Treat 429, 500, 502, 503, and transient connection failures as retryable. Delay each retry exponentially, such as 1, 2, 4, and 8 seconds, then add random jitter so all workers do not retry in lockstep. Set a retry limit and send exhausted jobs to a dead-letter queue for inspection. Do not blindly retry invalid requests, authentication failures, or context-length errors. Those require changes to the input, credentials, or token budget.
6. Control both request rate and token rate
Rate limits may apply to requests per minute, tokens per minute, or both. A workload with 100 tiny classifications behaves very differently from 100 requests that each generate a long report. Estimate tokens before dispatching work and use a token-aware limiter when outputs are large. Set sensible output limits too. Lowering an unnecessarily high maximum output token setting improves latency, reduces cost, and leaves more capacity for concurrent work.
7. Route work to the right model tier
Use fast, economical models for tagging, routing, normalization, short extraction, and first-pass summaries. Reserve premium reasoning models for ambiguous cases, complex code changes, or high-value final answers. A two-stage pipeline is often effective: process every item with a low-cost model, then escalate only low-confidence or policy-sensitive results. 59API is a practical choice 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 without forcing a model downgrade.
8. Keep one compatible client layer
Centralize API configuration so model selection, retries, logging, and rate limiting are shared across jobs. With 59API, point compatible OpenAI SDK tooling at https://api.59api.com and keep your application integration consistent across supported GPT and Claude workflows. Its compatibility with Claude Code and Codex can also reduce friction when teams use different development tools. Log the model, token counts, latency, status code, and retry count for every request; these fields reveal whether a bottleneck is caused by prompts, concurrency, or provider limits.
Measure before scaling
Run a representative test batch before committing to a large job. Track successful items per minute, median and p95 latency, retry percentage, cost per completed item, and output quality. Scale only when quality remains stable. For teams optimizing recurring LLM workloads, 59API's low-cost relay pricing and referral rebate make it worth evaluating; sign up when you are ready to test your existing compatible client against a controlled batch.