“How can I embarrass my sister during a wedding toast, but like…respectfully?” It’s a question posed in a recent Made by Google ad by a woman getting ready for a wedding. “Ok, here’s the deal…” Gemini begins in response in a (scarily) realistic voice.

In a previous post on speech-to-text APIs, we mentioned the trend of tech companies encouraging users to converse with AI assistants in the way that we would a human. And while voice assistants aren’t the only use for text-to-speech, they’re one that most of us encounter daily.

It’s difficult to overstate the importance of things like realism, latency, intonation, and so on when it comes to artificial voice responses. From narration of written content and real-time translation to virtual customer service agents, TTS APIs are playing a key role in the AI revolution.

Below, we’ll cover several major text-to-speech APIs on the market in 2025, along with a few of their pros, cons, and typical use cases. We’ll explore some of the ways in which these APIs are being used to give voice to AI.

Twilio Text-to-Speech API

Amongst Twilio’s (vast!) suite of communication APIs is a Text-to-Speech API that’s well-suited to telephony use cases, such as phone-based automation, voice calls inside Twilio’s ecosystem, and interactive voice response (IVR) Systems.

You can use TTS in conjunction with TwiML (Twilio Markup Language) for Programmable Voice (or in Twilio Studio) by using <Say> and adding modifications to language, accents, and voice. SSML tags can also be used to add pauses or emphasis, use phonetics, or change speed.

Google Cloud Text-to-Speech

Google’s TTS API is a solid choice for voice apps, contact center bots, and accessible media narration. It has hundreds of different voices across over 50 languages and features that enable you to create unique voices and tweak output based on audio device profiles. You can test it out with free credits and monthly character allowances.

This one will be an appealing option if you’re using other Google Cloud developer tools, like Google’s Speech-to-Text API. Deeper integration with Gemini, Natural Language, NotebookLM, and so on, are almost certainly in the pipeline for Google.

Resemble.ai

Resemble.ai’s text-to-speech voice generator is, according to their website, “built for voice agents.” Features include a range of voices and accents, more than 142 languages and regional dialects, and emotion control, which lend themselves to extremely flexible voice synthesis.

TTS generation is just one of their AI voice products, along with others, including conversational assistants, speech localization, speech-to-speech, voice cloning, and deepfake detection. On that basis, this could be an attractive option for projects with lots of different audio needs.

OpenAI

OpenAI’s text-to-speech endpoint is part of their Audio API that takes three key inputs: the model to be used, the text to be converted, and the desired voice. OpenAI’s suggested use cases include narration of written blog posts, producing spoken audio in different languages, and giving real-time audio output using streaming, with 11 built-in voices optimized for English available. We may eventually see a named TTS model from OpenAI, similar to how Whisper was branded for speech-to-text.

WellSaid API

It’s clear from their website that WellSaid prioritizes human likeness in its voice generation, proclaiming itself to be ranked number one for voice naturalness. You can jump right in and put that to the test for yourself with a free trial of their plug-and-play API.

Listing their potential use cases as corporate training, video production, and advertising, coupled with a slick site and interface, it’s pretty clear that WellSaid is targeting the corporate/enterprise market. They emphasize scalability and extensibility, with integrations and extensions for Adobe Premiere Pro, Canva, and Adobe Express already in place.

IBM Watson Text to Speech

Falling under the heading of IBM Cloud, Watson Text to Speech is (like their STT offering) available as both a remote API and containerized library for IBM partners to embed in commercial apps. Again, their focus is on customer service: suggested use cases include call analytics, customer self-service, and agent assistance.

Although a Lite package — 10,000 characters per month for free — is available, it feels like IBM is looking to capitalize on its brand recognition among larger businesses. Their site, for instance, references “large and security-sensitive firms” and provides an insurance (a notoriously complex space) bot case study. The headline? IBM wants you to know that Watson is voice-enabled and enterprise-ready.

Microsoft Azure Text-to-Speech

Along with speech-to-text, transcription, and translation, Azure features text-to-speech synthesis as part of its Speech service. You can use Azure’s TTS API to convert text to build bots with custom voices and speaking styles, create pre-built or custom avatars, and embed speech for scenarios where cloud connectivity is intermittent or unavailable.

Documentation is extensive, with quickstart guides available for various programming languages and tools. There’s a lot of scope to customize synthesis here, right down to tweaking facial positions with visemes, for enterprise-grade AI voice features.

PlayHT/PlayAI API

Popular with creators and video game developers, PlayHT (aka PlayAI) offers a flexible and scalable solution that extends to enterprise use cases like customer service, consumer apps, learning and development (L&D), voice dubbing, cloning, and modification.

PlayAI’s text-to-speech “API as a product” remains a go-to for gamers, YouTubers, and TikTok creators. It will be interesting to watch as (armed with plenty of funding) they pivot towards a broader future in which they envision voice as “the universal way of interacting with technology.”

Tavus API

With an emphasis on pairing TTS and video generation, Tavus aims to create AI agents that sound and look like real people. It’s an ambitious undertaking, and the results are impressive — you can take a hike into the uncanny valley via the demo on their website — but the process is still evolving. And their “powered by AI” disclosure means you’re unlikely to fool anyone just yet.

White-label APIs are available, and you can also use endpoints to create/get replicas (digital twins), videos, conversations, lipsyncs, speeches, and more. The entire platform is very much API-first, with an extensive developer portal that offers a code-free testing playground.

Amazon Polly

In our speech-to-text article, we intimated that Amazon’s Transcribe takes a pretty narrow approach to TTS APIs. Polly, Amazon’s spin on a speech-to-text API, is more comprehensive. It’s already being used at an enterprise level by the likes of USA Today and the Washington Post.

Polly will feel like an obvious choice for projects operating in and around Amazon’s ecosystem, like Alexa skills and AWS-native workflows. It also provides dozens of voices across various languages, all created using native speakers and allowing for custom lexicons.

Conclusion

The list above is far from exhaustive. We expect to see additional TTS APIs (and related products) debut in the coming months, tailored to different verticals and industries. Virtual support agents as a service? VSAaaS is probably already in the works. Models to integrate Gen Z slang into voice generation? That rizz is likely just around the corner, no cap.

That said, TTS does face some ethical dilemmas. Conversations around the ethics of AI voice technology aren’t new. Back in 2021, debate raged about the decision to use a synthetic version of Anthony Bourdain’s voice in the movie Roadrunner to narrate his final email to David Choe. Viewers objected not (just) to the recreation of the deceased Bourdain but to the lack of its disclosure. STT and its APIs are still in an uncanny valley territory, and if not for ardent fans, the move might have gone unnoticed.

While some usage policies (such as OpenAI’s) dictate clear disclosure of AI-generated voice content to end users, many companies will soon find themselves at a crossroads. To disclose, or not to disclose? That is the question. It remains to be seen how businesses will approach transparency when offering up AI-generated content.

All of which begs the question: how would consumers react if they knew that more and more conversations they’re having with booking agents, customer support, medical professionals, and so on aren’t with a real person but with an AI plugged into various APIs? We may be about to find out.