For developers building voice AI agents, Vapi is real-time voice API infrastructure that connects speech recognition, an LLM, and speech synthesis into one low-latency pipeline for phone and web calls.
Presence & Market Position
#76 of 103
GTM Developer Tools
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Vapi is working to become the production infrastructure layer for voice agents, coordinating speech recognition, language models, synthesis, telephony, and web calling through one low-latency API. Its market conversation is active and builder-led, with attention centered on getting voice agents into production, assembling live systems, choosing models, and connecting calls to broader workflow infrastructure. Practical examples place Vapi inside CRM reactivation and multichannel outreach stacks, while enterprise coverage focuses on scaling phone operations. That gives the brand a clear technical identity around production voice AI rather than a finished customer-support application.
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DaytonaAmazon Ring, Intuit, ServiceTitan, New York Life, Kavak, Instawork
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Business Profile
Founded in 2020, Vapi is an independent voice-infrastructure company with 170 employees and $75.2 million in funding. It reported $80 million in ARR and a $500 million valuation in May 2026, reflecting rapid enterprise adoption of voice agents for support and sales calls. Customers include Amazon Ring, Intuit, ServiceTitan, New York Life, Kavak, and Instawork. The company continues to add executive depth and positions itself as the configurable infrastructure beneath enterprise voice products rather than a single packaged agent.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Vapi exposes its platform through APIs, server and web SDKs, a CLI, and an MCP server that turns assistants, phone numbers, and calls into tools for compatible agents. It also works in the other direction: Vapi assistants can connect to external MCP servers and invoke their tools during live calls or chats. This two-way MCP model extends agent control from building voice systems into runtime action.
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