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Overview
Delay when initializing a voice agent system from an idle state. This metric is measured in milliseconds and directly correlates with user satisfaction scores. Industry benchmarks suggest keeping Cold Start Latency under specific thresholds for optimal caller experience.
Use Case: Causes awkward pauses at the beginning of conversations.
Why It Matters
Causes awkward pauses at the beginning of conversations. Optimizing Cold Start Latency directly impacts caller experience, system performance, and operational costs. Even small improvements can significantly enhance user satisfaction.
How It Works
Cold Start Latency is calculated by measuring the time between specific events in the voice agent pipeline. The measurement starts when the triggering event occurs and ends when the measured outcome is achieved. Platforms like Vapi, Retell AI, Deepgram each implement Cold Start Latency with different approaches and optimizations.
Common Issues & Challenges
Hamming AI's testing shows cold starts can cause 3-5x normal latency, severely impacting first impressions. Their analytics reveal cold start issues through p99 latency spikes, particularly after deployments or during low-traffic periods.
Implementation Guide
Follow Hamming AI's recommendations: implement warm-up calls after deployments, use connection pooling to maintain warm connections, and monitor cold start frequency as a key performance indicator.