Batch Processing and Concurrency for LLM APIs
Batch smarter, not harder
When you start sending real traffic through LLM APIs, the first bottleneck is usually not model quality. It is throughput. One request at a time is easy to reason about, but it wastes network round trips, leaves compute idle, and makes costs harder to predict. Batch processing and concurrency solve that problem, but only if you use them deliberately.
If you are building on a relay like 59API, you already have an advantage: cheap pay-as-you-go access to Claude and GPT models through a single OpenAI-compatible base URL, https://api.59api.com. That means you can scale experiments, pipelines, and production workloads without changing your client stack or paying premium routing costs just to test parallelism.
1. Separate the work that can wait
Not every prompt needs an immediate response. Classification jobs, document summaries, extraction tasks, and content rewriting often belong in a queue. Group those requests into batches so your application can process them on a schedule instead of holding a user session open. This reduces overhead and makes retries much easier.
Use batching when the input set is already known. A daily import of 5,000 support tickets is a better batch candidate than a live chat response. For the first case, you can group records, normalize payloads, and submit work in chunks. For the second, keep latency low and avoid large batches that delay the first token.
2. Cap concurrency with a real limit
Concurrency is useful only when you control it. Fire off too many requests and you will hit rate limits, socket pressure, or noisy latency spikes. Set a fixed ceiling per worker, per API key, or per endpoint. Start conservatively, then raise the limit after you measure p95 latency, error rates, and retry volume.
A practical pattern is a worker pool with a queue. Each worker pulls one task, sends one request, and releases the slot when finished. That keeps your system stable even when a model slows down or a vendor throttles burst traffic. Since 59API works with Claude Code, Codex, and any OpenAI SDK, you can apply this pattern without rewriting your client layer.
3. Match batch size to token reality
Bigger batches are not always better. The best size depends on prompt length, completion length, context window, and failure tolerance. If your prompts are short and uniform, larger batches can cut overhead. If each job carries a long context, smaller batches reduce the chance that one oversized request wastes time or exceeds limits.
Measure by tokens, not just by request count. Two requests with 20,000 combined tokens may behave very differently from ten requests with the same count if one contains a giant prompt and the others are tiny. Track input tokens, output tokens, and average completion time per batch size so you can tune with evidence.
4. Use idempotency and retries from day one
Concurrency magnifies failure modes. A single timeout can become dozens of duplicate jobs if your retry logic is sloppy. Assign a stable job ID to every task, store its state, and make retries idempotent. That way, a retry can resume safely instead of creating duplicate records or double-charging your pipeline.
Use exponential backoff with jitter for transient errors. Retry 429s and timeouts, but fail fast on malformed payloads. If you are processing batches asynchronously, write partial results as each job completes so one failed request does not block the entire batch.
5. Parallelize independent steps, not just requests
Many LLM workflows have multiple phases: ingest, normalize, prompt, validate, and store. You do not need to serialize all of them. Let parsing run while another request is in flight. Validate prior output while the next batch is being generated. This kind of pipeline concurrency often matters more than raw request parallelism.
For example, if you are extracting fields from invoices, you can split the workload into document decoding, OCR cleanup, model inference, and schema validation. Each stage can run on its own worker pool. The result is smoother throughput and less idle time across the whole system.
6. Prefer lower-cost models for high-volume jobs
High concurrency can get expensive fast, so model choice matters. Use stronger models where reasoning quality is critical, and cheaper models where the task is repetitive or narrow. With 59API, you can route to Claude Opus, Sonnet, Haiku, Fable, or GPT models through the same relay and keep the pricing aligned with the workload instead of the vendor logo.
This is where low-cost access matters in practice. Batch-heavy systems often spend most of their budget on predictable utility work, not glamorous prompts. A relay that offers official-quality models at some of the lowest prices, plus a referral rebate, makes it easier to keep those pipelines profitable while you scale them.
7. Measure throughput, not just latency
People often optimize a single request in isolation and miss the bigger picture. Your real metrics are requests per minute, tokens per minute, successful retries, and cost per completed job. A slightly slower model can still be the better choice if it finishes more jobs per dollar and stays stable under load.
Keep a simple dashboard. Watch queue depth, worker utilization, error codes, and average spend per batch. If throughput rises while failure rate stays flat, your concurrency level is probably healthy. If latency gets better but retries explode, you have pushed too far.
8. Start small, then scale with data
The safest rollout is a staged one: one batch size, one concurrency limit, one workload. Validate the pattern on a small dataset, then widen the range once you have real measurements. That approach is faster than trying to engineer the perfect setup upfront.
If you want a low-friction place to do that work, sign up for 59API and point your existing OpenAI-compatible client at https://api.59api.com. You get cheap pay-as-you-go access, native official-quality models, and a path to test batch and concurrency strategies without locking yourself into a new SDK.
The core idea is simple: batch where delay is acceptable, cap concurrency where load is bursty, and measure the system as a whole. Do that well, and your LLM pipeline will be cheaper, steadier, and much easier to grow.