Vapi

Vapi's strength is that it solves the unglamorous parts of voice AI — turn detection, interruption handling, latency budgets, telephony connection — and lets you bring your own brain. You wire up Claude or GPT or your fine-tuned model; Vapi makes sure the user can interrupt, that the agent doesn't talk over silences, that there's no awkward pause between sentences. Building this stack from scratch with raw Twilio + OpenAI Realtime takes weeks and never quite works right. Vapi does it in a config file. The dashboard for testing call flows and replaying recordings is also unusually polished for a developer-focused product.

Where it falls short is debuggability when things go wrong in production. When an agent says something unhinged on a real customer call, you want a clear trace of which model produced it, what the prompt looked like at that moment, and what tool the assistant was about to call. Vapi has these signals but they're scattered across the dashboard, and reconstructing a five-minute call to find the bad turn takes effort. Pricing also adds up faster than the per-minute rate suggests once you stack the LLM and voice provider costs on top.

Use Vapi if you're building a voice product and don't want to own the real-time stack. If you need full control of every audio byte and have a team to staff it, OpenAI's Realtime API or LiveKit Agents will give you that control at the cost of more engineering. If you just want a chatbot to answer one phone line for a small business, Retell or Synthflow are simpler.