Voice Load Testing: How to Simulate 10,000 Concurrent Calls
Blog post from Coval
Voice AI systems face unique challenges when scaling due to their requirement for real-time, bidirectional audio streaming, stateful multi-turn conversations, and complex protocol interactions, making standard load testing tools like k6 or JMeter insufficient. Unlike HTTP load testing, which operates on request-response patterns, voice AI testing must handle continuous streams of audio, understand conversational context, and manage specific protocols such as WebSocket, SIP, and RTP. Effective voice AI load testing involves measuring metrics that matter under load, such as p95 response latency, jitter, packet loss, and call completion rate, while also breaking down component-level latency to identify bottlenecks. A comprehensive load testing methodology includes phases of baseline profiling, ramp testing, spike testing, soak testing, and scaling to the target to ensure the system can handle high concurrency levels without degrading performance. Simulation platforms like Coval provide an effective alternative to building custom tooling, offering AI-driven conversational agents that generate realistic audio and stress-test both infrastructure capacity and conversational quality.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.