Batching Requests For LLMs

2025-11-11

Introduction

Batching requests for large language models is not merely a performance trick; it is a design philosophy that unlocks scalable, predictable, and cost-aware AI at production scale. In real-world systems, users interact asynchronously with intelligent services that must feel instant, even when the underlying models are expensive and latency-bound. Leading products—from ChatGPT to Gemini, Claude, Copilot, DeepSeek, Midjourney, and OpenAI Whisper—rely on sophisticated batching strategies to balance latency, throughput, and cost. The core idea is simple in spirit: instead of sending every user request to an LLM in isolation, collect small groups of requests, process them together as a batch, and then return the results. The payoff is substantial: higher throughput per dollar, tighter latency budgets, and more predictable performance under load. This masterclass explores how batching works in practice, what system designers must consider, and how to apply these ideas to real-world AI deployments across customer support, coding assistants, content generation, and multimodal workflows.

Applied Context & Problem Statement

Imagine a mid-sized SaaS platform that provides an AI-powered customer support assistant. On a typical workday, thousands of users send inquiries ranging from account questions to complex troubleshooting steps. If each request were routed to an LLM one by one, you would incur per-request overhead, switch between tenants and contexts, and contend with variable network latency. The result would be high average latency, long tails, and skyrocketing costs as the provider scales. Batching—processing multiple prompts in a single inference call—addresses these issues by amortizing fixed overhead, improving hardware utilization, and enabling more stable throughput. Yet batching also introduces challenges. Latency-sensitive users require timely answers, and intra-batch coherence must be preserved so that each response remains correct and contextually relevant. In practice, teams must design batching policies that respect latency SLOs, avoid starvations of smaller users or high-priority tasks, and manage the prompt context so that the batch’s combined input remains within the model’s context window and the desired quality thresholds. The same tradeoffs show up whether you’re serving a live chat assistant, a code completion tool like Copilot, an audio-to-text pipeline with Whisper, or a multimodal generator that combines text, image, and video prompts. In production, batching is the lever that turns expensive AI into a reliable service with controllable economics and user experience.

Core Concepts & Practical Intuition

At its core, batching is about batching the right things in the right way. You want to maximize the work you can fit into a single model call without violating latency targets or degrading output quality. The principal dimensions to optimize are throughput, latency, and cost. Throughput measures how much work you complete per unit time, latency captures the time from user request to answer, and cost reflects the resources consumed by inference. These dimensions are interdependent. Larger batch sizes tend to increase throughput and reduce per-token cost due to better resource utilization, but they can inflate tail latency if your system cannot assemble batches quickly enough or if some requests require tight, per-user personalization. The art is in designing a batching window and a batching policy that yields the best compromise for your workload and business constraints.

In practice, you will encounter two broad batching patterns. The first is time-based micro-batching: you collect incoming requests for a short, bounded interval (for example, 5 to 100 milliseconds) and then issue a single batch to the model. This approach smooths traffic, reduces per-call overhead, and tends to perform well for interactive workloads with moderate latency budgets. The second pattern is size-based batching: you accumulate a batch up to a target number of requests (say 4 to 32) or until a maximum wait time is reached, whichever comes first. Small, frequent batches are great for low-latency needs, while larger batches maximize throughput when latency slack allows. In real production systems, teams often combine both patterns with prioritization rules and backpressure to ensure fairness and SLA adherence.

A key challenge is preserving per-user or per-tenant context within a batch. For single-turn interactions, it is straightforward to pass prompts with minimal history. For multi-turn conversations, you must thread context across requests in the batch without leaking cross-tenant information or mixing session states. This is where session management, prompt engineering, and careful orchestration matter. We also need to respect model constraints, particularly the context window. If a batch’s combined prompts exceed the model’s token limit, you must split the batch or compress prompts intelligently, sometimes trading off context richness for feasibility. The practical upshot is that batching is as much about content strategy as it is about timing and scheduling.

From an engineering standpoint, a successful batching system is a carefully engineered pipeline. Ingested user requests flow through an API gateway, a batching orchestrator, and a model inference core, then out through a response router. Along this path, teams implement caching to reuse common prompts, deduplication to avoid repeating identical work, and routing logic to select among different models such as ChatGPT, Gemini, Claude, or Mistral depending on latency, cost, or capabilities. Streaming responses complicate batching only modestly: you can form batches for the initial prompt and then stream subsequent tokens as they become available, preserving a responsive user experience while maintaining batching efficiency.

A practical nuance is the distinction between online and offline workflows. In some scenarios, you can accumulate batches across user requests to maximize throughput during peak hours, while during critical moments you revert to single-request, low-latency mode to honor strict SLOs. Other complexities include privacy and data governance—especially when handling sensitive user data—where teams must enforce telemetry minimization, ephemeral caches, and strict data retention policies. Across these realities, successful batching strategies blend policy, engineering discipline, and system design to ensure that AI services scale responsibly and reliably.

Engineering Perspective

From a system-design lens, batching requires a soft but firm boundary between throughput-focused goals and latency-sensitive user experiences. An effective architecture typically introduces a batching layer that sits between the API surface and the model inference engine. Requests arrive, metadata such as tenant IDs and session context are attached, and a batch builder aggregates prompts into groupings that respect the model’s context window, temperature constraints, and any policy constraints. The batch is then dispatched to the inference service, which could be hosted by a public API like OpenAI’s GPT family, a private deployment of Mistral or Claude-style models, or a hybrid setup where a retrieval-augmented generator leverages Active Recall via DeepSeek or other knowledge bases. When results return, a response router disassembles the batch outputs, associates them back with the originating requests, and streams or delivers the results to clients.

Practical workflows emerge around data pipelines and governance. In a typical enterprise deployment, you maintain a queue per model and a central policy engine that defines prioritization rules, per-tenant quotas, and fallback behavior. Observability is non-negotiable: you monitor batch sizes, inter-arrival times, tail latency, cache hit rates, and per-batch cost. This data informs decisions about how aggressively to batch, whether to widen the batching window, or when to apply per-tenant isolation to avoid “noisy neighbor” effects. In production, this translates into tangible business outcomes: faster response times for high-priority users, more consistent support quality, and lower per-request costs as the hardware is better utilized. You might observe that a customer-support workflow using ChatGPT or Claude achieves higher throughput during business hours while preserving strict latency for high-value accounts through policy-based preemption or dedicated lanes.

The multi-model reality adds another layer. Modern LLM ecosystems often involve model routers that select among ChatGPT, Gemini, Claude, or locally hosted models such as Mistral. A batching system must be model-aware, balancing batch composition not only by prompt content but also by model capabilities, latency profiles, and pricing. In practice, this means your orchestrator can opportunistically group requests that are suitable for a particular model, or route within the batch based on the requested modality or system constraints. When integrating with multimodal engines—where text, images, or audio are part of the prompt—batching must manage cross-modal payloads, ensuring that the combined token and image data fit within the target model’s constraints. This kind of orchestration is central to modern AI platforms, whether powering a developer tool like Copilot, a creative assistant such as a generative image pipeline, or a voice-enabled assistant using Whisper for transcription.

Real-World Use Cases

Consider a customer-support platform that handles thousands of chats daily. A batching strategy enables rapid, consistent replies by grouping inquiries from multiple users that share similar contexts or prompts, then running them through a single inference call. If the system detects risk of latency creep, it tightens the batching window or reduces batch size to ensure response times stay within a defined SLA. This approach aligns with how consumer-grade assistants and enterprise agents are deployed at scale, whether the underlying models are ChatGPT, Claude, Gemini, or a private Mistral deployment. In practice, teams track not only throughput and latency but also the quality of the responses across batches, fine-tuning prompts to minimize drift and ensure that the coalesced prompts do not create conflicting instructions within a batch.

For developers building code assistants like Copilot, batching can operate at a slightly different scale. Multiple editors across teams might submit code completion requests that are similar in structure or intent. A batching layer can aggregate these prompts, provide context from shared repositories, and issue a batch inference that returns multiple completion candidates. The key here is to preserve per-request context while leveraging the shared context to improve quality and reduce cost. In such environments, caching plays a crucial role: identical or near-identical prompts can reuse outputs, avoiding redundant inferences and shaving milliseconds off response times.

In multimedia and audio workflows, batching is an enabler for efficiency. OpenAI Whisper, used for transcribing large volumes of calls or podcasts, benefits from batching of audio chunks that share processing characteristics. Grouping segments with similar speech patterns, languages, or noise levels allows the model to operate more efficiently and produce consistent transcription latency across the batch. Similarly, a multimodal pipeline might combine text prompts with image prompts processed by a model like Gemini or a Mistral-based system, batching the multi-modal inputs so that the entire payload fits within the model’s context window while maintaining a coherent output across the batch.

Even creative content platforms can leverage batching to scale. While image generation with Midjourney often runs as a single-request workflow, batch processing can be practical for tasks like batch captioning, batch style transfer, or batch post-processing on generated artwork. The engineering payoff is consistently the same: higher throughput per unit cost and more predictable performance, provided you respect model constraints and user experience requirements.

Future Outlook

The batching conversation will continue to evolve as LLMs grow in capability and as per-request costs shift with pricing models and hardware improvements. We can expect smarter batching policies that adapt in real time to workload characteristics, demand surges, and model availability. Techniques such as adaptive batching, where a system learns optimal batch sizes and time windows from live traffic patterns, will become mainstream. In practice, this means your system may automatically tune batch size, wait times, and routing decisions based on observed latency, success rates, and cost signals. In addition, retrieval-augmented generation will become more batching-friendly as the cost of looking up relevant documents declines and as caches become richer with context from broader user cohorts. The result is a future where AI services feel instant and economical, even as they scale to millions of users and thousands of concurrent conversations.

Multi-model orchestration will also mature. As Gemini, Claude, Mistral, and other providers extend their capabilities, batching will need to consider model heterogeneity at a finer granularity. We will see more sophisticated queueing disciplines, fairness policies, and per-tenant quality-of-service guarantees. Privacy-preserving batch strategies will gain traction, enabling organizations to batch requests in a way that minimizes cross-tenant data exposure while still achieving the efficiency benefits of batch execution. In the realm of enterprise AI, these developments will drive faster, cheaper, and more reliable deployments across customer support, professional software development, content creation, and accessibility workloads.

Conclusion

Batching requests for LLMs is a strategic, systems-level capability that translates theoretical efficiency gains into tangible, real-world benefits. It demands a careful balance between latency constraints, throughput targets, and cost considerations, all while preserving context, privacy, and quality. The practical lessons are clear: design a robust batching orchestrator, respect model constraints, implement intelligent routing across models, and couple batching with caching and data governance to unlock scalable AI that feels responsive and reliable. When done well, batching enables you to serve more users, deliver consistent experiences, and deploy AI services that truly scale in production—whether you’re powering a chat assistant, a coding collaborator, a multimodal generator, or a voice-enabled workflow. As you experiment with batch-oriented architectures, you’ll discover how the economics of AI systems can bend in your favor, enabling richer capabilities at lower marginal cost and with predictable performance under load.

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