Batch Inference
Processing multiple ML predictions as a group at scheduled intervals rather than one-at-a-time on demand, optimizing for throughput and cost over latency.
Batch inference processes predictions in bulk — running your model on thousands or millions of inputs at once, typically on a schedule (hourly, nightly). This contrasts with real-time inference, where predictions are generated on-demand for each request.
Batch inference is dramatically cheaper than real-time for several reasons: GPU utilization is higher when processing full batches, API providers offer 50% discounts for batch endpoints, and you can use spot/preemptible instances since timing isn't critical. A nightly batch job processing 100K recommendations might cost $50, while the same predictions served real-time could cost $500+.
Common growth use cases for batch inference: precomputing content recommendations for all users, generating personalized email content for campaigns, scoring all accounts for churn risk, embedding new content for search indexes, and generating SEO meta descriptions for product pages. The pattern is simple: if the prediction can be slightly stale (hours, not seconds), batch it. Reserve real-time inference for interactive features where freshness matters.
Related Terms
Real-Time Inference
Generating ML predictions on-demand as requests arrive, typically with latency requirements under 200ms for user-facing features.
Model Serving
The infrastructure and systems that host trained ML models and handle inference requests in production, optimizing for latency, throughput, and cost.
MLOps
The set of practices combining machine learning, DevOps, and data engineering to reliably deploy, monitor, and maintain ML models in production.
Cosine Similarity
A measure of similarity between two vectors based on the cosine of the angle between them, ranging from -1 (opposite) to 1 (identical), commonly used to compare embeddings.
Dimensionality Reduction
Techniques that reduce the number of dimensions in high-dimensional data while preserving meaningful structure, used for visualization, compression, and noise removal.
Data Pipeline
An automated sequence of data processing steps that moves data from source systems through transformations to destination systems, enabling reliable and repeatable data flows across an organization.
Further Reading
LLM Cost Optimization: Cut Your API Bill by 80%
Spending $10K+/month on OpenAI or Anthropic? Here are the exact tactics that reduced our LLM costs from $15K to $3K/month without sacrificing quality.
AI-Powered Personalization at Scale: From Segments to Individuals
Traditional segmentation is dead. Learn how to build individual-level personalization systems with embeddings, real-time inference, and behavioral prediction models that adapt to every user.