Startup News

Success in generative AI isn't reserved for those with the biggest budgets, but for those with clear plans, flexible platforms, and patience.

The common narrative is clear: start with a focused pilot tied to value, get your data and governance right, demonstrate early impact, train

LLMs changed how enterprises build applications, but smaller AI models provide power and reduces total cost of ownership.

AI inference attacks drain enterprise budgets, derail regulatory compliance and destroy new AI deployment ROI.

Learn how enterprises evaluate open versus closed AI models to optimize costs, security, and performance across different business use cases.

How wisely IT and business leaders plan and choose infrastructure can keep them from being doomed to pilot purgatory or AI damnation.

AI models can get fatigued, prompt ops can help manage, measure, monitor and tune prompts.

If you're unsure about the load of different AI workloads, start with the cloud and keep a close eye on the associated costs by tagging every resource with the responsible team.

Forecasting is a fundamentally new capability that is missing from the current purview of generative AI. Here's how Kumo is changing that.

OpenAI now includes tracing and eval tools with the API stack to help teams define what success looks like and track how agents perform

In this GB Spotlight experts from Reaktor + FastSpring discuss the Apple v Epic ruling and what it means for direct to consumer monetization

Enterprise teams hit a scaling wall when managing AI agents across departments. Writer's May Habib explains why traditional software development fails for agents and what Fortune 500 companies are doing instead.