Vapi

Vapi is a developer platform for building, testing, and deploying voice AI agents that handle real phone calls with human-like latency.

Overview

Vapi gives developers the plumbing voice agents need: telephony, streaming speech recognition, turn-taking logic, interruption handling, and tool calls, all behind a clean API. You bring your own models — or pick from supported speech and language providers — and Vapi handles the hard real-time parts like endpointing and background noise resilience. A web-based call tester lets teams simulate conversations, inspect transcripts, and tune prompts before going live, and analytics expose latency, drop rates, and outcome conversion per call. The platform scales from a solo developer’s first demo to enterprise call volumes with concurrent-call controls and compliance features. Its provider-agnostic design has made it a favorite for agencies and product teams shipping voice experiences quickly. The platform’s provider marketplace means teams can swap speech engines and language models without rewriting their agent, which matters in production where cost, latency, and accuracy trade-offs shift constantly.

Key Features

• Provider-agnostic stack for speech, language, and telephony
• Human-grade turn-taking with interruption and backchannel handling
• In-browser call testing with transcript and latency inspection
• Tool calls and webhooks that connect agents to business systems
• Concurrency controls and compliance features for enterprise volume
• Swappable speech and language providers without rewriting agent logic

Pricing

Vapi charges for platform usage on top of underlying model and telephony costs, with free trial credit for development. Volume pricing and enterprise agreements cover dedicated capacity, SLAs, and support.

Who Is It For

Developers and agencies building production voice agents — appointment lines, support hotlines, outbound campaigns — who want control over models without rebuilding real-time telephony infrastructure. It fits engineering teams that treat voice as a product surface and want the freedom to optimize models continuously rather than inherit a vendor’s fixed stack.