The hardest part of running AI at scale often is the bill rather than performance. As your application ingests and generates massive amounts of data, costs climb faster than the database itself: overprovisioned clusters, bolt-on cache layers, storage that never stops growing, and cloud networking charges hiding in every hop. How do you keep it from spiralling?

Join us as a developer and a database expert walk through the most common culprits behind runaway AI database costs and the technical strategies, with their tradeoffs, to rein them in. We'll demonstrate and apply each one on a sample application: a fast feature store with an integrated RAG pipeline, powered by ScyllaDB Cloud.

In this webinar, you'll learn how to:

• Spot how AI workload costs spiral out of control at scale
• Apply efficiency strategies to unify databases, eliminate cache layers, and cut cloud networking charges
• Prepare the database to sustain performance even through traffic spikes