Vapi vs Retell AI

Vapi and Retell AI are each other's closest rival in this site's voice-agents category. Both listings already name the other as the primary alternative, and for good reason: both are developer-first, bring-your-own-model orchestration layers, not a closed, all-inclusive product like Bland AI or a no-code builder like Synthflow AI.

A team picks its own speech-to-text, language model and text-to-speech provider on either platform. Each handles the hard real-time problems of a phone call underneath: turn-taking, interruption handling, low latency. Where they diverge is company shape and what ships out of the box.

Vapi was founded in 2023 by University of Waterloo classmates Jordan Dearsley and Nikhil Gupta. It has raised $72M total: a $20M Series A from Bessemer in December 2024, then a $50M Series B from Peak XV in May 2026, at a reported ~$500M valuation. Amazon Ring is a customer, chosen after evaluating more than 40 AI voice vendors.

Retell AI, founded the same year by five co-founders led by CEO Bing Wu, took a leaner path. It raised a single $4.6M seed round from Alt Capital in 2024, through Y Combinator's Winter 2024 batch. By April 2026 it had reached $60M in annualized revenue, up roughly 650% year-over-year, and stayed profitable with a team of only 20 to 25 people.

The product difference tracks the funding difference. Vapi pushes bring-your-own-everything furthest: it ships no owned speech models at all. Its distinguishing feature is squads, multiple specialised agents that hand off to one another within a single call, built for teams running complex, multi-step call flows.

Retell AI ships more of the surrounding product instead: an auto-syncing RAG knowledge base that agents pull from live, batch calling for outbound campaigns at scale, and a built-in AI quality-assurance layer that simulates conversations to test an agent before it goes live. A Vapi developer would otherwise have to build or buy that tooling separately.

Retell AI's own proprietary turn-taking model targets roughly 600ms latency, by its own documentation. Vapi's own documentation claims a sub-500ms average. Those two numbers come from each vendor's own marketing, not one independent benchmark, so treat them as claims rather than a measured result.

Both support Model Context Protocol tool calling. Retell AI documents the more explicit integration: an MCP server for managing agents from MCP-capable clients, plus mid-call MCP client tool calls. Vapi lists MCP support only generically, alongside function calling.

Integrations follow the same split. Retell AI's published list is broader on the contact-centre side: Five9, Amazon Connect, Nice and Talkdesk, alongside Salesforce, HubSpot and Zapier. Vapi's own list stays narrower: Twilio, Vonage and SIP telephony, plus model providers.

Pricing headlines favor Vapi, but both are stacked, component-based rates that run well past the advertised number once a real model and voice are added, re-confirmed live 2026-08-20 against both vendors' own pricing pages. Vapi's Build tier is $0.05/min for its own orchestration fee alone, with model, voice and telephony billed through on top; a practical all-in cost commonly runs $0.13 to $0.33/min. Its Scale tier is a custom annual contract, with HIPAA metered at an extra $2,000/month and Zero Data Retention at $1,000/month on either tier.

Retell AI's pay-as-you-go range is $0.07 to $0.31/min, built from roughly $0.055/min for its own voice infrastructure and $0.015/min for text-to-speech, plus the chosen LLM. GPT-4.1-class models add about $0.045/min; Claude 4.6 Sonnet adds about $0.08/min. Its knowledge base, verified numbers, PII removal and AI QA layer are billed as separate add-ons: $8/month, $10/month, $0.01/min and $0.10/min after 100 free minutes, respectively.

Neither platform has a permanent free tier. Vapi and Retell AI each give roughly $10 in trial credits, and both require a card on file before real usage begins.