#on-premises

Teradata has launched its Autonomous Knowledge Platform, a new flagship offering that brings together data, analytics, AI development, agent orchestration, and governance across cloud, on-premises, and hybrid environments. The target customer is an enterprise that has moved beyond testing AI assistants and is now asking harder questions: which data agents can use, what actions they can take, how much they will cost to run, and who is accountable when something goes wrong. The company said the platform builds on its existing database engine and governance infrastructure, while adding new capabilities and more tightly integrating existing ones, including AI Studio, the Tera natural-language workspace, Tera Agents, Elastic Compute on Teradata Cloud, and the upcoming Teradata Factory for on-premises AI workloads. Teradata is entering a competitive market with this. Snowflake, Databricks, Microsoft, Oracle, and Salesforce are all trying to persuade customers that their platforms should beco

Three key advantages of SLMs Division of labor: Modern AI architecture uses routers to send routine tasks to 7B-parameter SLMs, reserving trillion-parameter LLMs only for complex reasoning. Economic efficiency: For high-volume, repetitive tasks, SLMs can reduce cloud inference costs by up to 90% while providing near-instant latency. Privacy at the edge: Because SLMs can run locally on-device or on-premises, they reduce the data leakage risks inherent in sending sensitive telemetry to the public cloud. Large language models (LLMs) are the workhorses of AI, supporting ever more sophisticated capabilities and workflows, and approaching near-human level performance. But sometimes more isn’t always better — it’s just more. Specialized data and limited capabilities are just fine for some workflows. This realization is driving the evolution of small language models (SLMs), rather than one-size-fits-all LLMs. SLMs — coming in the form of domain-specific models, statistical langua