Why Batch Processing is not a Good Savings Option for LLM End Users

When companies like OpenAI and Anthropic tout their batch processing APIs with promises of "50% cost savings," it sounds like something any organization using AI should pursue. The marketing is compelling: process thousands of queries at half the price. But here's the reality that FinOps teams often discover after digging deeper—batch processing isn't actually accessible to most business users. It's really a developer tool, creating a significant gap between cost optimization promises and practical implementation.

The Technical Barrier

Unlike the user-friendly chat interfaces that have made AI accessible to everyone, batch processing requires serious technical expertise. Users must format requests in JSON, implement polling mechanisms to check processing status, handle file uploads and downloads, and manage error states across thousands of asynch operations. There's no "batch mode" button in ChatGPT or Claude's web interface—accessing these savings requires API keys, programming knowledge, and infrastructure to handle delayed responses.

This technical complexity means that when a marketing team wants to analyze thousands of customer reviews using batch processing, they can't simply switch to a different mode in their AI tool. They need to involve developers who can build the integration, manage the workflow, and create systems to handle the results that the Batch API might not return for 24 hours.

The Organizational Reality

This potential disconnect between FinOps recommendations and user capabilities can create a frustrating dynamic. FinOps teams identify batch processing as a major cost optimization opportunity, estimating significant savings from switching non-urgent AI tasks to batch mode. Business stakeholders get excited about cutting their AI bills in half. But when they try to implement these savings, they discover they need engineering resources—resources that may not be available for an even longer time than the batched results.

This leads to what some might call a "batch processing paradox": the teams that would benefit most from AI cost savings (marketing, finance, customer service) are the least equipped to implement the technical solutions that would deliver those savings. Meanwhile, the technical teams who could implement batch processing are often focused on building products rather than optimizing internal workflows. Moreover, there are plenty of other cost savings for synchronous processing that can be implemented easily without waiting up to 24 hours for a response.

When Batch Processing Actually Works

Batch processing savings materialize in specific scenarios where technical implementation is already part of the workflow. Engineering teams building AI-powered features can architect their systems to use batch APIs from the start. Data science teams processing large datasets can refactor their pipelines to submit jobs overnight rather than processing data in real-time. Platform teams can optimize existing automated workflows that don't require immediate responses.

The most successful batch processing implementations often happen when technical users are solving their own problems. A development team that needs to classify thousands of support tickets, generate bulk documentation, or analyze code repositories can design systems that naturally leverage batch processing's cost advantages.

The Real Cost Optimization Options

For non-technical teams, the practical path to AI cost optimization looks very different. Rather than batch processing, it’s more productive to focus on caching, optimizing synchronous model utilization, and assessing the investment that’s really needed to reduce TTFT (Time-to-First-Token) or TIBT (Time-in-between-Token) latency.   These changes don't require engineering resources and don't dramatically compromise end user experience.

It is possible to bridge the engineering - user gap by having technical teams build internal tools that make batch processing accessible to business users. You could create a custom interface that lets the marketing team upload a CSV of customer reviews and receive analysis results the next day, for example. But building these tools still requires dedicated development time, ongoing maintenance, and still might be a poor experience for end users that need much faster access to data.

Focus on Optimizing Synchronous Price/Performance

When AI companies promote batch processing savings, they're effectively marketing to developers.

A better option is to build FinOps around Synchronous Price/Performance, and optimize cache hit rates, the cost of reducing TTFT and TIBT milliseconds, minimizing queue times due to maxed out resources, and other techniques I’ll be discussing in an upcoming post regarding the top FinOps KPIs for Inference.