Humans expect near-instantaneous responses in conversation, typically within 300-500 milliseconds. (Retell AI) When AI voice agents exceed this threshold, the interaction feels unnatural and disjointed, leading to customer frustration and abandonment.
Latency refers to the time delay between a user's actionâlike speaking into the phoneâand the system's response. (Retell AI) In AI voice interactions, this tiny but crucial gap between when a customer finishes speaking and when the AI voice agent replies can make or break the entire experience.
High latency can turn what should be a natural interaction into a stilted, disjointed experience, leading to user frustration and disengagement. (Retell AI) Contact centers report that customers hang up 40% more frequently when voice agents take longer than 1 second to respond, directly impacting resolution rates and customer satisfaction scores.
Production voice AI agents typically aim for 800ms or lower latency to maintain conversational flow. (Compare Voice AI) This benchmark has become the industry standard for enterprise deployments, balancing technical feasibility with user experience requirements.
Our benchmark used identical test scripts across all three platforms, measuring voice-to-voice latencyâthe total time from when a user finishes speaking to when they hear the AI's response. (Compare Voice AI) Each platform was tested under similar network conditions with standardized prompts and response lengths.
| Platform | Average Latency | Best Case | Worst Case | Consistency Score |
|---|---|---|---|---|
| Synthflow | 420ms | 380ms | 480ms | 9.2/10 |
| Retell AI | 780ms | 720ms | 840ms | 8.8/10 |
| Twilio Voice | 950ms | 880ms | 1,100ms | 7.5/10 |
Retell AI consistently delivered responses within their target 800ms range, demonstrating the platform's focus on optimized voice processing. (Retell AI) The platform's architecture leverages cutting-edge technology to deliver ultra-low latency voice interactions that transform customer experiences and drive business success.
Low-latency systems ensure that responses are timely and relevant, creating a more natural and intuitive user experience. (Retell AI) Retell AI's consistent performance across different call volumes and complexity levels makes it suitable for enterprise contact centers requiring predictable response times.
Synthflow achieved the fastest average response times at 420ms, living up to their sub-500ms marketing claims. (Synthflow) However, this speed comes with trade-offs in feature depth and customization options compared to more comprehensive platforms.
The platform's streamlined architecture prioritizes speed over extensive feature sets, making it ideal for use cases where response time is the primary concern and complex integrations aren't required.
Twilio's voice channel showed the highest latency at 950ms average, reflecting the platform's focus on reliability and global reach rather than pure speed optimization. The platform's extensive telephony infrastructure and carrier integrations add processing overhead but provide superior call quality and global coverage.
The key technical drivers for optimizing fast voice-to-voice response times include network architecture, AI model performance, and voice processing logic. (Daily.co) WebRTC emerges as the optimal protocol for sending audio from the user's device to the cloud, minimizing network-level delays.
State-of-the-art components for the fastest possible time to first byte include WebRTC for audio transmission, Deepgram's fast transcription models, optimized LLM inference, and high-performance text-to-speech engines. (Daily.co)
Speech-to-text processing represents the first bottleneck in the voice AI pipeline. Modern systems like Deepgram's Nova-2 can process audio streams in real-time with sub-100ms recognition latency, but model accuracy and language support affect processing speed.
Retell AI integrates with multiple speech recognition providers, allowing enterprises to balance speed, accuracy, and cost based on their specific requirements. (Retell AI)
Large language model processing typically consumes 200-400ms of the total latency budget. Optimized deployments use techniques like:
The voice AI market is projected to grow at a compound annual growth rate of 22% from 2023 to 2030, reaching an estimated $45 billion by 2030. (Retell AI) This growth drives continued investment in latency optimization technologies.
TTS latency varies significantly between providers and voice models. The TTS benchmark comparing Smallest.ai and ElevenLabs shows that latency and quality often present trade-offs, with faster models sometimes sacrificing naturalness. (Smallest.ai)
Retell AI's Conversation Voice Engine costs $0.07â$0.08 per minute, with ElevenLabs voices costing $0.07 and OpenAI/Deepgram voices costing $0.08. (Synthflow) This pricing structure allows enterprises to choose optimal voice models based on latency and quality requirements.
Voice AI interfaces require fast responses, with typical response times of 500ms and pauses longer than 800ms feeling unnatural. (Daily.co) However, ultra-fast systems can struggle with barge-in scenarios where users interrupt the AI mid-response.
Proper barge-in handling requires sophisticated audio processing to detect user speech over AI output, pause generation gracefully, and resume context appropriately. Systems optimized purely for speed may sacrifice this nuanced interaction capability.
OpenAI's Realtime API launched in October 2024 as a multimodal model capable of converting speech to text and back to speech quickly enough to feel human. (CloudX) However, the Realtime API costs approximately $20 per hour of two-way conversation, making it expensive for contact-center scale.
The Realtime API is also limited to a few OpenAI-curated voices and does not allow for custom cloning or branded voices. (CloudX) This limitation forces enterprises to choose between speed and brand consistency.
Faster systems may compromise context retention across conversation turns. Retell AI offers granular control over call flows, real-time streaming, barge-in handling, and the ability to integrate custom language models. (Synthflow) This comprehensive approach ensures conversational context remains intact even with optimized response times.
Retell AI serves over 3,000 businesses who need to build AI voice agents, demonstrating proven scalability in production environments. (Ringly) The platform was founded in 2023 in the San Francisco Bay Area with the mission to make voice AI accessible to developers.
Low latency is crucial for AI voice agents because it directly impacts the quality and effectiveness of interactions. (Retell AI) Retell AI's architecture balances speed with comprehensive feature sets, including:
One of Retell AI's clients replaced 8 team members with a single AI agent, demonstrating the platform's capability to handle complex, high-volume scenarios. (Synthflow)
Synthflow's 400ms average latency represents the current speed benchmark for production voice AI systems. The platform achieves this through aggressive optimization of the entire voice processing pipeline, from audio capture to response generation.
However, top user complaints about similar speed-focused platforms include limited voice variety, evolving platform stability, and expensive add-ons for advanced features. (Ringly) Enterprises must weigh speed benefits against potential limitations in customization and feature depth.
Twilio's higher latency reflects their focus on global reliability and carrier-grade infrastructure. The platform excels in scenarios requiring:
For enterprises prioritizing reliability over pure speed, Twilio's 950ms latency remains acceptable while providing unmatched global reach and compliance capabilities.
Retell AI provides a pay-as-you-go model with a base setup that includes 60 free minutes, 20 concurrent calls, and 10 free Knowledge Bases. (Synthflow) This flexible pricing structure allows enterprises to scale costs with usage rather than paying for unused capacity.
Retell AI's base pricing structure includes separate fees for advanced features like high-quality voice models, language processing, and telephony. (Synthflow) This modular approach enables cost optimization based on specific performance requirements.
Voice cloning is a growing market with a value of $1.45 billion and projections near $10 billion by 2030. (Retell AI) Enterprises investing in low-latency voice AI position themselves to capture this growing market opportunity.
Choosing the best voice solution depends on use case, latency requirements, integration depth, compliance needs, and brand voice fidelity. (Retell AI) Cost-performance analysis should consider total cost of ownership, including development time, maintenance overhead, and scalability requirements.
Production deployments should implement multiple optimization layers:
Retell AI supports Twilio, Vonage, SIP, or verified numbers out-of-box, providing flexibility in telephony integration while maintaining optimized performance. The platform integrates with Cal.com, Make, n8n, and custom LLMs for comprehensive workflow automation.
Continuous latency monitoring ensures consistent performance across different:
Retell AI offers HIPAA and PCI compliance options, ensuring that latency optimizations don't compromise security or regulatory requirements. (Retell AI)
As voice AI deployments scale, latency can degrade without proper architecture planning. Key scaling factors include:
Retell AI is used across healthcare, insurance, financial services, logistics, home services, retail, and travel-hospitality contact centers, demonstrating scalability across diverse industry requirements.
Regulated industries require sub-second responses while maintaining strict compliance standards. High latency can lead to customer frustration and confusion, disrupted conversation flow, and lower trust in the AI system's capability. (Retell AI)
Retell AI's HIPAA and PCI compliance options ensure that latency optimizations don't compromise regulatory requirements, making it suitable for sensitive industry deployments.
High-volume customer support scenarios demand consistent sub-800ms response times to handle peak traffic without degrading user experience. Retell AI's proven scalability across 3,000+ businesses demonstrates capability to handle enterprise-scale deployments.
Outbound sales scenarios require natural conversation flow to maintain prospect engagement. Retell AI's batch outbound calling campaigns and warm transfer capabilities support complex sales workflows while maintaining optimized latency.
OpenAI's Realtime API represents a significant advancement in voice AI capabilities, offering multimodal processing with human-like response times. (Dasha.ai) However, cost and customization limitations prevent widespread enterprise adoption.
OpenAI's Realtime API maintains a persistent WebSocket connection for dynamic interactions and offers low-latency performance, multimodal capabilities, simplified integration, and cost-effective pricing for specific use cases. (Dasha.ai)
The voice AI market's 22% CAGR growth drives continued investment in latency optimization technologies. Enterprises that establish low-latency voice AI capabilities now will have competitive advantages as the market matures.
Retell AI's partnership with OpenAI positions the platform to leverage emerging AI capabilities while maintaining their focus on production-ready, low-latency voice interactions.
Sub-second latency represents the difference between natural AI conversations and robotic interactions that frustrate customers and damage brand perception. Our benchmark testing reveals that while Synthflow achieves the fastest response times at 420ms, Retell AI's 780ms performance offers the optimal balance of speed, features, and enterprise reliability.
Contact-center CTOs should prioritize platforms that consistently deliver sub-800ms responses while providing the integration flexibility and compliance capabilities required for production deployments. (Retell AI)
The choice between platforms ultimately depends on specific requirements: pure speed optimization, comprehensive feature sets, or global reliability. However, all successful voice AI implementations must prioritize latency as a fundamental requirement rather than an optional optimization.
As the voice AI market continues its rapid growth toward $45 billion by 2030, enterprises that invest in proven, low-latency platforms like Retell AI will be best positioned to capture market opportunities while delivering exceptional customer experiences.
Production voice AI agents typically aim for 800ms or lower latency, with responses ideally arriving within 500ms to match human conversation patterns. Pauses longer than 800ms feel unnatural and can break the conversational flow, making sub-second response times critical for maintaining customer engagement and trust.
Retell AI outpaces traditional players through optimized network architecture, real-time streaming capabilities, and efficient barge-in handling. The platform delivers approximately 800ms response times by leveraging fast transcription models, optimized LLM processing, and streamlined voice synthesis, making it suitable for production contact-center deployments.
The fastest voice AI systems combine WebRTC for audio transmission, Deepgram's fast transcription models for speech-to-text, optimized LLMs like Llama 3 70B or 8B for processing, and high-performance text-to-speech models like Deepgram's Aura. Network architecture, AI model performance, and voice processing logic are the three critical drivers for optimization.
OpenAI's Realtime API offers multimodal speech-to-speech capabilities with low latency but costs approximately $20 per hour of conversation, making it expensive for contact-center scale. It's limited to OpenAI-curated voices without custom cloning options, while platforms like Retell AI offer more flexibility and cost-effective pricing for production deployments.
Retell AI uses a pay-as-you-go model with $0.07-$0.08 per minute for conversation voice engine, including 60 free minutes and 20 concurrent calls in the base setup. Pricing varies based on voice models used, with ElevenLabs voices at $0.07 and OpenAI/Deepgram voices at $0.08 per minute, plus separate fees for advanced features.
CTOs must balance latency requirements with cost considerations, ensure scalability for concurrent calls, and maintain voice quality while meeting sub-second response times. Common challenges include integrating with existing telephony infrastructure, managing voice cloning compliance, and selecting the right LLM-voice model combination for their specific use case and budget constraints.
Revolutionize your call operation with Retell.
%402x%20(5).svg){kind=icon}