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Voice Agent API Is Industry’s Only Offering That Delivers The Single, Real-Time API Experience Developers Love, Combined with Full Controllability Enterprises Need. No Need to Stitch Together STT, TTS, and LLM Orchestration. No Black Box Limitations. Priced at $4.50 Per Hour.
Deepgram, the leading voice AI platform for enterprise use cases, announced the general availability (GA) of its Voice Agent API, a single, unified voice-to-voice interface that gives developers full control to build context-aware voice agents that power natural, responsive conversations. Combining speech-to-text, text-to-speech, and large language model (LLM) orchestration with contextualized conversational logic into a unified architecture, the Voice Agent API gives developers the choice of using Deepgram’s fully integrated stack (leveraging industry-leading Nova-3 STT and Aura-2 TTS models) or bringing their own LLM and TTS models. It delivers the simplicity developers love and the controllability enterprises need to deploy real-time, intelligent voice agents at scale. Today, companies like Aircall, Jack in the Box, StreamIt, and OpenPhone are building voice agents with Deepgram to save costs, reduce wait times, and increase customer loyalty.
In today’s market, teams building voice agents are often forced to choose between two extremes: rigid, low-code platforms that lack customization, or DIY toolchains that require stitching together STT, TTS, and LLMs with significant engineering effort. Deepgram’s Voice Agent API eliminates this tradeoff by providing a unified API that simplifies development without sacrificing control. Developers can build faster with less complexity, while enterprises retain full control over orchestration, deployment, and model behavior, without compromising on performance or reliability.
“The future of customer engagement is voice-first,” said Scott Stephenson, CEO of Deepgram. “But most voice systems today are rigid, fragmented, or too slow. With our Voice Agent API, we’re giving developers a powerful yet simple interface to build conversational agents that feel natural, respond instantly, and scale across use cases without compromise.”
“We believe the future of customer communication is intelligent, seamless, and deeply human—and that’s the vision behind Aircall’s AI Voice Agent,” said Scott Chancellor, Chief Executive Officer of Aircall. “To bring it to life, we needed a partner who could match our ambition, and Deepgram delivered. Their advanced Voice Agent API enabled us to build fast without compromising accuracy or reliability. From managing mid-sentence interruptions to enabling natural, human-like conversations, their service performed with precision. Just as importantly, their collaborative approach helped us iterate quickly and push the boundaries of what voice intelligence can deliver in modern business communications.”
“We believe that integrating AI voice agents will be one of the most impactful initiatives for our business operations over the next five years, driving unparalleled efficiency and elevating the quality of our service,” said Doug Cook, CTO of Jack in the Box. “Deepgram is a leader in the industry and will be a strategic partner as we embark on this transformative journey.”
Developer Simplicity and Faster Time to Market
For teams taking the DIY route, the challenge isn’t just connecting models but also building and operating the entire runtime layer that makes real-time conversations work. Teams must manage live audio streaming, accurately detect when a user has finished speaking, coordinate model responses, handle mid-sentence interruptions, and maintain a natural conversational cadence. While some platforms offer partial orchestration features, most APIs do not provide a fully integrated runtime. As a result, developers are often left to manage streaming, session state, and coordination logic across fragmented services, which adds complexity and delays time to production.
Deepgram’s Voice Agent API removes this burden by providing a single, unified API that integrates speech-to-text, LLM reasoning, and text-to-speech with built-in support for real-time conversational dynamics. Capabilities such as barge-in handling and turn-taking prediction are model-driven and managed natively within the platform. This eliminates the need to stitch together multiple vendors or maintain custom orchestration, enabling faster prototyping, reduced complexity, and more time focused on building high-quality experiences.
