A practical session on cutting AI costs without sacrificing quality. The speakers argue for measuring cost per completed task rather than per-token list prices, show how to right-size models with accuracy-versus-cost Pareto analysis, and walk through four levers: prompt caching, programmatic tool calling, reasoning effort, and batch or flex processing. Customer case studies from Perplexity, Notion, Clio, and Blitzy ground the advice.
Alex Mashrabov, CEO of Higgsfield, explains how agentic systems choose among models and provision their own GPU infrastructure to deliver consistent, multi-step creative media. He covers the trade-offs between speed, controllability, and dedicated deployment, and shares the 10-hour AI-generated live stream as an example of what becomes possible for small teams.
This talk argues against the common practice of picking LLMs based solely on leaderboards, emphasizing that there's no single best model, only the right one for a given request. It introduces Digital Ocean's Inference Router, a customizable, open-source solution that intelligently selects models based on user-defined preferences (cost, latency, task, quality) rather than benchmarks, demonstrating significant cost savings and performance improvements in live demos.
A guide to understanding the differences between Large Language Models (LLMs), Small Language Models (SLMs), and Frontier Models (FMs). Learn the unique strengths of each model type and see practical use cases for document classification, customer support, and incident response to help you choose the right model for your AI project.
A review of five key interviews covering the importance of data-centric AI (DMLR) in specialized fields like law, the challenges of AI benchmarking, strategies for domain-specific model selection using red teaming, the power of AI in predicting human behavior, and the shift towards building causal AI models.