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Overview
Total time from when a caller stops speaking to when they hear the voice agent's response, including all processing steps. This metric is measured in milliseconds and directly correlates with user satisfaction scores. Industry benchmarks suggest keeping End-to-End Latency under specific thresholds for optimal caller experience.
Use Case: The actual delay callers experience - includes ASR, LLM processing, and TTS generation time.
Why It Matters
The actual delay callers experience - includes ASR, LLM processing, and TTS generation time. Optimizing End-to-End Latency directly impacts caller experience, system performance, and operational costs. Even small improvements can significantly enhance user satisfaction.
How It Works
End-to-End 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 Deepgram, Vapi, Retell AI each implement End-to-End Latency with different approaches and optimizations.
Common Issues & Challenges
According to Hamming AI, teams often focus on individual component latency while ignoring cumulative effects. Their analytics platform provides unified end-to-end latency tracking with one-click drill-down to identify bottlenecks.
Implementation Guide
To optimize End-to-End Latency, start by establishing baseline measurements using monitoring tools. Set realistic targets based on your use case - customer service applications typically require performance within industry benchmarks. Implement caching strategies, optimize model selection, and use edge deployment where possible.