Training data begat large language models—and ever since, they’ve been intimately bound. This year’s honorees include a database tuned for AI agents, a dataset for AI-aided diagnosis of breast tumors, and a platform that gives companies more control over how their data is acquired and used.

Databricks
For bringing databases up to AI speed
According to Databricks, AI agent creation of test and development environments on its platform has grown from 0.1% two years ago to 97% today—a dynamic that traditional database architectures have been ill-equipped to accommodate. In response, the company developed Lakebase, a ground-up rearchitecture of databases that can spin them up and shut them down at AI speeds, facilitating the multitude of small, fast workloads of AI-driven development. In August, Databricks raised $5 billion in a strategic funding round, valuing it at $190 billion. The company’s revenue run rate has grown 80% year over year.

DataPelago For transparently routing code to the optimal processor While GPUs and NPUs are the processor architectures most closely aligned with AI, high-performance computing tasks also take advantage of two other kinds of processors, CPUs and FPGAs. Effectively routing the right computations to the right processors can reduce significant overhead. DataPelago ’s Nucleus modernizes CPU-centric designs of legacy systems and acts as a bridge between data lakes and query engines. It routes computing requests to the most efficient processor type available without requiring any code changes. The company says that its Accelerator for Apache Spark delivers up to 10x faster performance at 80% lower compute costs.

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