Twilio vs Vapi vs LiveKit: Choosing the Right Voice Stack

Every team building a voice AI product eventually faces the same infrastructure decision: Twilio, Vapi, or LiveKit (or some combination). The wrong choice costs you weeks of rework. The right choice depends on your latency requirements, your team's ops capacity, your pricing constraints, and whether you're building for a use case that each platform was actually designed for.

Twilio is the oldest and most battle-tested option. Its strength is its telephony infrastructure — global PSTN coverage, reliable SIP trunking, rock-solid number management across 180+ countries, and a mature API surface that every integration your enterprise client uses probably already knows how to talk to. Twilio's weakness, for AI voice specifically, is that it was built before LLMs and its architecture shows it. Integrating an AI brain into a Twilio voice flow means working with Media Streams (WebSocket audio), assembling your own STT/LLM/TTS pipeline, and managing the synchronization yourself. This is not a small amount of work.

Pricing on Twilio compounds quickly. The per-minute call cost alone is $0.0085/min outbound for US numbers, plus STT costs, LLM costs, TTS costs, and any programmable voice add-ons. A high-volume outbound campaign of 10,000 minutes per month — not unusual for a mid-size sales or collections operation — lands at $400+ per month in Twilio telephony alone before you've paid for a single token. For teams at scale, Twilio negotiates volume pricing, but you need to be large to get meaningful discounts.

Vapi was purpose-built for AI voice agents and shows it at every level of the stack. The default path — connect a phone number, provide a system prompt, pick STT/LLM/TTS providers — produces a working agent in under an hour. Vapi handles the streaming pipeline, manages turn-taking logic, provides built-in call recording and transcription, and exposes webhooks for every event in the conversation lifecycle. The STT and TTS provider abstraction lets you swap between Deepgram, ElevenLabs, and other providers without touching your agent logic.

Vapi's latency profile is excellent for most use cases. The platform adds minimal overhead on top of the underlying provider latencies, and its default turn-detection logic works well for professional conversation patterns. Where Vapi struggles is in highly customized scenarios — custom STT models, on-premise LLM deployment, or conversations that require real-time audio analysis beyond voice activity detection. The abstraction that makes Vapi fast to start with becomes a constraint when you need to operate below the platform's abstraction layer.

LiveKit is the open-source real-time audio/video infrastructure layer that Vapi and several other platforms are built on top of. Using LiveKit directly gives you the lowest possible latency (no platform overhead), complete control over every component in the pipeline, and no per-minute pricing beyond your infrastructure costs. The tradeoff is that you're assembling the entire stack yourself: SIP trunking, STT integration, LLM routing, TTS synthesis, client SDKs, and the WebRTC plumbing that makes it all work in real time.

For teams with strong infrastructure engineering capacity who are building voice AI at scale where the per-minute cost of a managed platform is material, LiveKit is the right choice. For teams that want to ship fast and don't want to manage real-time infrastructure, Vapi is the better starting point. Twilio makes sense when you're deep in an existing Twilio ecosystem or need specific telephony features (IVR, conferencing, complex SIP routing) that pure AI-voice platforms don't expose.

Our actual stack for most client deployments: Vapi for the AI voice layer, Twilio for telephony routing and number management when complex carrier requirements exist, and LiveKit when we need sub-500ms latency or custom audio processing. Using two or three of these in combination is more common than picking just one.