A media company processing 1M input tokens and 500K output tokens using Gemini 2.5 Flash paid $2.55 for real-time processing but only $0.775 using batch mode—a 69.6% cost reduction. This 50% discount is consistent across OpenAI, Azure, and Google Vertex AI, but it comes with a 24-hour processing window that fundamentally changes your architecture decisions. Understanding when to trade latency for cost isn’t just optimization; it’s the difference between a profitable AI product and one that bleeds money at scale.
The economics of LLM deployment have shifted dramatically. As of January 2025, OpenAI’s GPT-4.1 costs $1.00 per 1M input tokens and $4.00 per 1M output tokens in real-time mode. The same model in batch mode drops to $0.50 input and $2.00 output—a direct 50% savings that compounds at scale. For a system processing 100M tokens daily, this represents a $300,000 monthly difference between real-time and batch strategies.
But cost isn’t the only variable. Batch processing introduces latency constraints: a 24-hour completion window means you can’t use it for customer-facing chatbots or real-time decision systems. However, for background jobs, data analysis, content generation, and model evaluation pipelines, batch processing transforms LLMs from a luxury into a viable business model.
The key insight is that batch and real-time aren’t competing approaches—they’re complementary tools. Production systems use both: real-time for interactive experiences, batch for everything else. The challenge is knowing which workloads belong in each bucket and how to architect systems that can switch between them as requirements evolve.
Batch processing fundamentally changes the request-response paradigm. Instead of waiting for immediate responses, you submit a file of requests and retrieve results later. This asynchronous model unlocks the 50% discount but introduces new operational considerations.
File Creation: You create a JSONL file where each line contains a complete API request with a custom_id
Upload: The file is uploaded to the provider’s storage system
Job Creation: A batch job is created with a 24-hour completion window
Processing: The provider processes requests asynchronously, respecting your quota limits
Result Retrieval: You download results once the job completes
The critical difference is quota isolation. Batch API rate limits are completely separate from synchronous API limits. This means a massive batch job won’t impact your real-time service capacity—a crucial feature for production systems.
The pattern is clear: batch processing becomes economically essential at scales above 1M tokens per day. Below that threshold, the operational complexity may outweigh the savings.
Decision Framework: When to Use Batch vs Real-Time
The Mistake: Building customer-facing features that depend on batch jobs completing within 24 hours.
Reality: While providers aim for 24-hour completion, jobs can take longer. OpenAI’s Batch API FAQ states: “If a batch expires (i.e. it could not be completed within the SLA time window), then remaining work is cancelled and any already completed work is returned” openai.com.
Solution: Design for eventual completion. Use batch for non-urgent workloads and implement fallback mechanisms for time-sensitive requests.
The Mistake: Budgeting only for visible output tokens, not accounting for hidden reasoning tokens.
Reality: As noted in OpenAI’s pricing documentation: “While reasoning tokens are not visible via the API, they still occupy space in the model’s context window and are billed as output tokens” platform.openai.com.
Solution: Add 10-20% buffer to output token estimates for reasoning models (o3, o4-mini, GPT-5 series).
The Mistake: Submitting massive batch jobs without checking quota limits, causing failures.
Reality: Azure OpenAI Global Batch has enqueued token quotas that vary by subscription tier. For example, GPT-4.1 has a 5B token limit for Enterprise/MCA-E, but only 200M for default subscriptions learn.microsoft.com.
Solution: Implement exponential backoff with retry logic. Azure supports automatic retry queuing with exponential backoff in supported regions.
The Mistake: Hitting the default 500-file limit per resource.
Reality: Without expiration settings, you’re limited to 500 batch files. Setting expiration (14-30 days) increases this to 10,000 files learn.microsoft.com.
Solution: Always set expires_after when uploading files.
The Mistake: Treating cancelled batches as complete failures.
Reality: “When you cancel the job, any remaining work is canceled and any already completed work is returned. You’ll be charged for any completed work” learn.microsoft.com.
Solution: Always check for and process partial results from cancelled jobs.
Batch vs real-time cost/latency comparison with use-case matrix
Interactive widget derived from “Batch Processing vs Real-Time: The Cost-Latency Tradeoff” that lets readers explore batch vs real-time cost/latency comparison with use-case matrix.
Batch processing delivers a consistent 50% cost discount across OpenAI, Azure, and Google Vertex AI, making it essential for workloads exceeding 1M tokens per day. The tradeoff is a 24-hour processing window that restricts batch to background operations while preserving real-time APIs for interactive experiences. Production systems should implement both: real-time for user-facing features and batch for cost-sensitive, high-volume processing. Critical implementation considerations include separate quota management, exponential backoff for rate limits, handling partial results on cancellation, and accounting for hidden reasoning tokens in cost calculations.
