Agentic AI Comparison:
Millis AI vs Vapi
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Introduction
This report compares two prominent voice AI platforms: Millis AI and Vapi. Both offer solutions for creating voice agents, but they have distinct features and approaches that cater to different user needs and preferences.
Overview
Vapi
Vapi is a developer-oriented voice AI platform known for its robust infrastructure and advanced features like interruption handling and function calling. It's designed to handle large-scale operations with support for over a million concurrent calls.
Millis AI
Millis AI is a voice AI platform that focuses on providing a user-friendly interface for creating voice agents. It offers a range of features including multi-language support and integration with popular LLMs.
Metrics Comparison
Autonomy
Millis AI: 7
Millis AI offers a good level of autonomy with its 'Choose By Millis' feature that automatically selects the best available LLM model for optimal latency. However, it still requires some user input for configuration.
Vapi: 8
Vapi provides high autonomy with features like proprietary endpointing models and interruption handling. Its ability to scale to over a million concurrent calls without manual intervention also contributes to its autonomy.
Both platforms offer strong autonomy features, but Vapi edges ahead with its more advanced automated systems for handling calls at scale.
Ease of Use
Millis AI: 8
Millis AI is designed with user-friendliness in mind, offering a straightforward pricing structure and easy-to-understand features. Its interface appears to be more accessible to non-technical users.
Vapi: 6
Vapi is more developer-oriented, offering powerful features but requiring more technical knowledge to fully utilize. It provides extensive documentation but may have a steeper learning curve for non-developers.
Millis AI appears to be easier to use for a general audience, while Vapi caters more to developers and technically proficient users.
Flexibility
Millis AI: 7
Millis AI offers flexibility in terms of LLM selection and multi-language support. However, it may have limitations in terms of customization compared to more developer-focused platforms.
Vapi: 9
Vapi excels in flexibility, offering features like function calling, WebRTC streaming, and on-prem provider deployments. It allows users to bring their own models, voices, and backend, providing extensive customization options.
Vapi offers superior flexibility, especially for developers who need fine-grained control over their voice AI implementations.
Cost
Millis AI: 7
Millis AI has a transparent pricing structure with a base rate of $0.02 per minute, plus additional costs for LLM, TTS, and STT services. This can be cost-effective for users who can optimize their usage.
Vapi: 6
Vapi's pricing is not explicitly stated in the provided sources, but it's mentioned to have a base rate of $0.05 per minute with additional costs for various services. This suggests it might be more expensive than some alternatives.
Based on available information, Millis AI appears to have a slight edge in cost-effectiveness, but actual costs would depend on specific usage patterns and required features.
Popularity
Millis AI: 6
Millis AI's popularity is difficult to gauge from the provided sources. It appears to be a relatively new player in the market, with limited information about its user base or market share.
Vapi: 7
Vapi seems to have gained some traction in the developer community, with multiple comparison articles and videos discussing its features. Its ability to handle large-scale operations suggests it has attracted significant users.
Vapi appears to have a slight edge in popularity, particularly among developers, but both platforms are relatively new in the rapidly evolving voice AI market.
Conclusions
Both Millis AI and Vapi offer compelling features for voice AI development, but they cater to different audiences. Millis AI stands out for its ease of use and transparent pricing, making it a good choice for businesses looking for a straightforward voice AI solution. Vapi, on the other hand, excels in flexibility and scalability, making it ideal for developers and businesses requiring advanced customization and large-scale operations. The choice between the two would depend on the specific needs of the user, their technical expertise, and the scale of their voice AI projects.