Simplifying LLM batch inference
Blog post from Portkey
AI teams often face challenges when processing high-volume workloads, as running individual requests in real-time is impractical for offline or large-scale tasks, making batching a preferred solution. Batching, supported by providers like OpenAI, Azure, Bedrock, and Vertex, allows grouping requests for asynchronous processing, offering advantages such as cost savings and bypassing API rate limits. However, implementing batching is complex due to issues like provider-specific file uploads, continuous monitoring, retrieving batch outputs, and opaque pricing, which can hinder efficiency and governance. Portkey's AI gateway addresses these challenges by providing a unified, automated workflow that simplifies the batching process across various providers. It offers streamlined file handling, automatic monitoring, direct batch output retrieval, and transparent cost tracking. Additionally, Portkey enhances batching capabilities with features like immediate batch processing, per-request model selection, and retry configurations, allowing for flexible, efficient, and secure management of asynchronous and near real-time AI workloads.
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