Batch processing
- Definition
- Running multiple AI inference requests together to maximize throughput and reduce per-request cost. Batch processing is how companies handle large-scale data labeling, content generation, and analytics workloads efficiently.
- Why it matters
- Real-time inference is expensive. If your workload can tolerate latency, batch processing slashes costs by 50% or more. Every major API provider now offers batch endpoints at steep discounts precisely because batching lets them utilize GPUs more efficiently during off-peak hours. For engineering leaders, the decision of what to batch versus what to serve in real-time is a core architectural choice that directly impacts your AI infrastructure bill. Workflows like nightly content generation, weekly report summarization, and bulk classification are natural batch candidates.
- In practice
- OpenAI's Batch API offers 50% cost reduction for requests that can tolerate 24-hour completion windows. Anthropic's Message Batches API processes up to 100,000 requests per batch with similar discounts. Companies like Scale AI batch millions of data labeling requests daily. In practice, many enterprises run hybrid architectures: real-time inference for user-facing features and batch processing for analytics, content generation, and model evaluation, often using the same models through different API endpoints.
Quick answers
- What is Batch processing?
- Running multiple AI inference requests together to maximize throughput and reduce per-request cost. Batch processing is how companies handle large-scale data labeling, content generation, and analytics workloads efficiently.
- Why does Batch processing matter?
- Real-time inference is expensive. If your workload can tolerate latency, batch processing slashes costs by 50% or more. Every major API provider now offers batch endpoints at steep discounts precisely because batching lets them utilize GPUs more efficiently during off-peak hours. For engineering leaders, the decision of what to batch versus what to serve in real-time is a core architectural choice that directly impacts your AI infrastructure bill. Workflows like nightly content generation, weekly report summarization, and bulk classification are natural batch candidates.
- How is Batch processing used in practice?
- OpenAI's Batch API offers 50% cost reduction for requests that can tolerate 24-hour completion windows. Anthropic's Message Batches API processes up to 100,000 requests per batch with similar discounts. Companies like Scale AI batch millions of data labeling requests daily. In practice, many enterprises run hybrid architectures: real-time inference for user-facing features and batch processing for analytics, content generation, and model evaluation, often using the same models through different API endpoints.
Related terms
Inference
The process of running a trained model to generate predictions or outputs from new inputs. Inference cost per token is the key economic metric for AI deployment and is falling rapidly.
Throughput
The number of tokens or requests an AI system can process per second. High throughput is essential for batch processing, high-traffic applications, and cost-efficient inference at scale.
Inference cost
The expense of running an AI model in production, typically measured per million tokens. Inference costs have dropped 10-100x in the past two years, enabling new business models and use cases.
Inference economics
The study of costs, pricing models, and margin structures around running AI models in production, encompassing hardware costs, model efficiency, pricing strategies, and the competitive dynamics of the inference market.
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