Ultravox (formerly Fixie.ai) vs Retell AI
Detailed side-by-side comparison to help you choose the right tool
Ultravox (formerly Fixie.ai)
🔴DeveloperVoice AI Tools
Real-time, speech-native voice AI platform that processes audio directly without text conversion, enabling fast, natural voice conversations for AI agents with sub-second latency and preservation of paralinguistic signals.
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FreeRetell AI
🔴DeveloperVoice AI Tools
Voice AI platform for building conversational phone agents with human-like speech, ultra-low latency, and natural turn-taking for call center automation.
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$0.07/minFeature Comparison
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Ultravox (formerly Fixie.ai) - Pros & Cons
Pros
- ✓Speech-native model processes audio directly, eliminating STT→LLM→TTS pipeline latency and producing sub-second response times that feel conversational rather than transactional.
- ✓Preserves paralinguistic information (tone, pace, hesitation) that traditional cascaded pipelines discard, leading to more natural turn-taking and barge-in handling.
- ✓Open-source Ultravox model published on Hugging Face gives teams the option to self-host for cost, latency, or compliance reasons instead of being locked into a proprietary API.
- ✓First-class integration path with telephony providers like Twilio plus WebRTC support, making it practical to ship real phone-call agents and in-app voice without building media plumbing from scratch.
- ✓Tool/function calling is supported inside live voice sessions, so agents can take real actions (lookups, transfers, bookings, CRM writes) rather than only chatting.
- ✓Developer-first surface area: API, JavaScript SDK, and clear primitives for building agents, which suits engineering teams already comfortable with LLM tooling.
Cons
- ✗Pure developer platform with no visual builder or no-code flow designer, so non-engineers cannot stand up an agent without writing code.
- ✗Voice and language coverage is narrower than long-established TTS/STT vendors that have spent years accumulating locales, accents, and voice libraries.
- ✗Speech-native architecture is newer than the cascaded STT+LLM+TTS approach, so tuning, debugging, and observability tooling around it is less mature than the pipeline ecosystem.
- ✗Costs at scale can be hard to predict for high-volume telephony workloads because pricing combines model usage with telephony minutes from third-party providers.
- ✗Branding/identity churn (Fixie.ai → Ultravox) means older documentation, blog posts, and integration guides on the public web can be inconsistent or outdated.
Retell AI - Pros & Cons
Pros
- ✓Sub-second response latency and a tuned turn-taking model produce conversations that interrupt, pause, and recover more naturally than most competing voice agent platforms
- ✓Three build modes (single-prompt, conversation flow, custom LLM) cover both no-code prototyping and deeply customized agent stacks where teams want to bring their own model
- ✓Built-in telephony plus SIP trunk support means teams can ship a working phone agent end-to-end without stitching together Twilio, a TTS vendor, and an LLM provider separately
- ✓HIPAA compliance and SOC 2 controls make it one of the few voice agent platforms that healthcare and financial-services teams can deploy in production without major workarounds
- ✓Strong voice library with multilingual support and voice cloning lets brands match accent, language, and persona to their target market
- ✓Scales to thousands of concurrent calls with batch dialing, making it viable for outbound campaigns and high-volume contact centers, not just demo-scale prototypes
Cons
- ✗Per-minute pricing stacks telephony, voice, and LLM costs separately, so total cost per call can be hard to forecast and gets expensive at high volume compared with self-hosted stacks
- ✗Building robust production agents still requires prompt engineering, function-calling design, and conversation-flow testing — the polished demos hide significant tuning work
- ✗Conversation-flow builder is powerful but can become unwieldy for very complex branching logic, pushing teams toward custom LLM mode where they take on more engineering burden
- ✗Voice cloning and some advanced voices depend on third-party providers, which means quality, latency, and pricing can shift when those upstream vendors change
- ✗Documentation and best practices around edge cases like background noise, accents, and barge-in tuning are still maturing, and teams often learn through trial and error in production
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