The inference market is fragmenting into specialized segments for different workloads, similar to how the database market splintered into relational, document, and other categories.

The inference market is splitting along three latency tiers: real-time (sub-100ms) for voice assistants and autonomous vehicles; near-real-time (100ms-2s) for chatbots and code completion; and batch (seconds to hours) for document processing and content generation.

Different modalities (image, video, audio, text) and deployment contexts (cloud, edge devices, on-premise) create distinct infrastructure requirements. For example, Apple runs a 3-billion-parameter model on-device for Apple Intelligence, while image generation requires 50 sequential passes through the model—different architectural constraints than text-based chatbots.

The database market fragmented into Oracle, MongoDB, Databricks, and Snowflake across relational, document, and other categories. A $100B inference market fragmenting the same way creates room for similar specialized winners.