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Load Testing AI: Aiming at a Moving Target
AI-powered features are everywhere, but most teams are still load testing these like traditional APIs, and that's a problem. AI systems behave differently under pressure with latency spikes, token limits, cascading failures, and unpredictable cost explosions.
This webinar examines how to load test AI-backed applications realistically, accounting for non-deterministic responses, variable execution times, and external dependencies. We'll break down common failure patterns seen in production AI systems and walk through practical strategies to simulate real user behavior at scale. A short demo will show how to design scenarios that test both performance and resilience.
What you'll learn:
- How AI workloads break the rules and why traditional load testing falls short
- AI-specific failure modes like latency spikes, token limits, cascading failures, and cost explosions
- Realistic load modeling for non-deterministic responses and variable execution times at scale
- Multi-dimensional testing that goes beyond throughput to cover latency, cost, and resilience
- Turning performance insights into production readiness with actionable next steps
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