Serverless for AI in 2026: The easiest way to compute

AI teams often waste time and money on infrastructure. Simple training jobs end up running on complex setups, GPUs sit idle between runs, and debugging takes too long.

In this webinar, we’ll show how serverless works for AI in 2026. You’ll see how Nebius Cloud and Token Factory let you run training and inference without managing clusters, and when Serverless Jobs are a better choice than dedicated GPUs.

You’ll get simple decision guidelines, practical patterns you can reuse, a live demo, and time for live Q&A.

What you’ll learn:

• What “Serverless AI” really includes (and what it doesn’t)
• Jobs vs Endpoints: how to choose in minutes
• 3 ready-to-use patterns: training, batch jobs, dev/eval serving
• How to control costs and debug failures faster
• The 6 most common objections with honest answers

We’ll also show where serverless works best, where it doesn’t, and what it really means for GPU workloads with a demo you can try yourself.

Who should attend
Built for ML engineers and MLOps teams who use containers and on-demand GPUs and don’t want Kubernetes for simple single-node workloads