Deepgram has made a name for itself as one of the go-to startups for voice recognition. Today, the well-funded company announced the launch of Aura, its new real-time text-to-speech API. Aura combines highly realistic voice models with a low-latency API to allow developers to build real-time, conversational AI agents. Backed by large language models (LLMs), these agents can then stand in for customer service agents in call centers and other customer-facing situations. As Deepgram co-founder and CEO Scott Stephenson told me, it’s long been possible to get access to great voice models, but those were expensive and took a long time to compute. Meanwhile, low latency models tend to sound robotic. Deepgram’s Aura combines human-like voice models that render extremely fast (typically in well under half a second) and, as Stephenson noted repeatedly, does so at a low price. “Everybody now is like: ‘hey, we need real-time voice AI bots that can perceive what is being said and that can understand and generate a response — and then they can speak back,’” he said. In his view, it takes a combination of accuracy (which he described as table stakes for a service like this), low latency and acceptable costs to make a product like this worthwhile for businesses, especially when combined with the relatively high cost of accessing LLMs. Deepgram argues that Aura’s pricing currently beats virtually all its competitors at $0.015 per 1,000 characters. That’s not all that far off Google’s pricing for its WaveNet voices at 0.016 per 1,000 characters and Amazon’s Polly’s Neural voices at the same $0.016 per 1,000 characters, but — granted — it is cheaper. Amazon’s highest tier, though, is significantly more expensive.
Full story : Deepgram’s Aura allows developers to build conversational AI agents based on large language model.
