It’s that time of year again when we compile the top speech-to-text APIs to keep an eye on in 2025. Whether you’re looking to add voice-based AI into your products to automate customer support, enhance note-taking, supercharge your meetings, or more, this list will help you narrow-in on the right provider for your needs.
But first, let’s begin with a little breakdown of the landscape and how to best evaluate speech-to-text providers in a way that makes the most sense for your business.
Speech-to-text (STT), also known as Automatic Speech Recognition (ASR) or voice recognition, is an AI technology that converts human speech from audio or video into written text. Speech-to-text APIs allow organizations to add these capabilities to new or existing products for a variety of use cases ranging from call bots and voice assistants to AI-powered virtual meeting platforms.
The demand for speech-to-text capabilities has continued to rise and is projected to reach a global market value of $15.87bn by 2030. So it’s no surprise that navigating this landscape can prove challenging. Today, the commercial landscape consists of major cloud providers including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, the famous outlier OpenAI, and specialized contenders like Gladia, Assembly AI, and others featured below.
In recent years, speech-to-text API providers have slowly begun to challenge Big Tech by democratizing access to AI transcription and focusing on improvements in performance and features. As enterprise-grade clients often prioritize production-ready and cost-effective APIs, our list of top API providers will focus on this market segment. But don’t worry–we’ll outline the reasons for excluding cloud providers from our final selection at the end.
It comes as no surprise that what will ultimately make the difference between providers comes down to your specific needs and budget.
Maybe you’re adding STT capabilities to your editing platform and need to transcribe audio across several different languages–in which case language support will be key. Or perhaps you're a contact center that needs to enable its agents with access to transcriptions, fast.
Whatever the case, there are a few common areas to consider that can help make or break your project. We recommend focusing on the following key areas:
When it comes to core STT performance, you’ll usually face a trade-off between accuracy and speed. While the types of models that a provider uses can be a good indicator when evaluating overall performance, it is essential to go beyond standardized metrics like word error rate (WER) when assessing them. Instead, focus on how well the ASR system performs in real-world conditions, such as handling background noise, and diverse speaking styles. Using your own datasets during evaluation is highly recommended, accompanied by a careful assessment of your use case and the kind of transcription technology it requires (asynchronous vs real-time).
Language is another key factor to consider. Because ASR models tend to skew heavily towards English by default, oftentimes a provider claiming to support a number of languages does so at the cost of providing accurate transcriptions in less common, low-resource languages. Features to look for and test include the ability to accurately transcribe a range of languages, automatically detect different languages, and perform code-switching and translations. If you’re curious to learn more about how ASR systems navigate languages, check this article.
It’s one thing to simply transcribe speech-to-text, but it’s another to gain real insights from those transcriptions. That’s where the features and capabilities providers offer begin to stand out in allowing companies to gain a real competitive advantage. This can range from real-time sentiment analysis to word-level timestamps and speaker diarization.
Understand whether pricing is usage-based (per hour or per token) and ensure it aligns with your budget and scalability needs. While the lowest price tag can often look enticing–beware–the cheapest solutions will come with tradeoffs that can lead to even greater costs down the line. The reality is that hardware and software costs in speech-to-text, combined with stringent certification requirements, make it impossible to deliver high-quality transcriptions with a low-cost approach.
Last but not least, it’s important to confirm how your chosen API provider handles data privacy–especially when dealing with highly confidential audio data from customers, internal meetings, and patients–you name it. Enquire with your STT provider about any security-related certifications they have, including SOC 2 Type 1/Type 2, HIPAA, and ISO 27001 or ISO 27701, as well as hosting architecture options.
Now that you know what to look for, let’s get into it! Here’s our list of the top five speech-to-text API providers of 2025.
This list has been compiled based on market research, user interviews, and surveys conducted by Gladia.
Gladia was founded in 2022 in France with a vision to create a powerful speech-to-text API that produces quality transcriptions in more than 100 languages–while also recognizing varying accents.
Models: Whisper-Zero, born out of a complete rework of OpenAI’s Whisper ASR, delivers less hallucinations, improved performance, and an extended feature set for an enterprise-grade experience. Following its original asynchronous API, the company now offers the real-time model, leveraging Whisper’s multilingual capabilities with real-time response (<300 ms latency).
Language support: Gladia supports 100+ languages and is fine-tuned for optimal performance across different accents.
Pricing: Three plans: Free (10h/month), Pro ($0.612 per hour), and Enterprise (custom pricing).
Best known for: Gladia's multilingual capabilities include support for 100+ languages, and the API is able to capture speech even as you switch languages on the fly (known as ‘code-switching’) while maintaining low latency and high accuracy across both real-time and async transcription.
Assembly AI was founded in San Francisco and is a prominent player with a lot of experience in the speech-to-text space that’s made evident by a variety of features that cater to different user needs.
Models: Assembly AI's latest model is Universal-2. This model builds upon their previous models word error rate.
Language support: AssemblyAI provides transcription in 20+ languages.
Pricing: There are three available plans: Free (with a starting credit of $50), Pay-as-you-go (starting at $0.12/hour), and Custom (personalized plan).
Best known for: Assembly AI is the first player in the space to have built an LLM-based LeMUR, enabling companies to build chat-based apps on spoken data.
Deepgram is another California-based pioneer of the speech-to-text API market that stands out for its customizable AI models and speed.
Models: Deepgram’s proprietary model Nova is based on a Transformer architecture.
Language support: Primary focus is on English, while Deepgram’s latest Nova-2 model supports 36 languages.
Pricing: Three plans: Pay-as-you-go ($200 free credit), Growth ($4k+ per year), and Enterprise ($10k+ per year).
Best known for: Deepgram's à la carte offer allows users with sufficient in-house expertise and resources to fine-tune custom models and adapt them to specific industries or use cases.
Speechmatics is a UK-based speech recognition company with a strong global presence.
Models: Speechmatics leverages self-supervised learning (SSL) to train its speech models. As of December 2024, their latest model is Ursa 2.
Language support: Speechmatics boasts support for 50+ languages and dialects.
Pricing: Three plans: Free (8h per month), Pay-As-You-Grow (from $0.30 per hour), Enterprise (custom plan).
Best known for: The company offers a real-time translation service, making it suitable for use cases like media broadcasting.
Rev.ai, a product of Rev.com, is a well-known speech-to-text service from the US that stands out for its use of AI- and human-generated transcripts.
Models: Undisclosed.
Language support: Rev.ai officially supports 58+ languages.
Pricing: Two plans: Pay-as-you-go (depending on the transcription type and additional features) and Enterprise (volume-based pricing).
Best known for: The platform offers a hybrid approach, combining automated transcription with human reviewers, to guarantee enhanced accuracy and quality of transcripts.
When we take into consideration what enterprise-grade clients are generally looking for, it all comes down to five key parameters including speed, accuracy, supported languages, price, and extra features. And in most cases, Big Tech providers just aren’t cutting it when it comes to providing the best value to price ratio.
Let’s look at accuracy benchmarks as an example. Big Tech providers have a WER of 10%-18%, while most startups and specialized providers achieve a WER between the 1-10% range–and that’s just one example we can look at.
One reason to explain this is that speech-to-text is simply not their core business and these capabilities are usually coupled with their wider suite of services. But that alone raises another concern in regard to customization and innovation. Clients looking for added features, language support, or even advancements in speed would likely be better served by more agile providers that are focused exclusively on speech-to-text technology.
Another provider we excluded was OpenAI’s open-source Whisper. To learn why, we’ve created a separate breakdown of the model’s limitations for enterprise users here. Also, here’s a more thorough comparison between OpenAI Whisper, Google Speech-to-Text, and Amazon Transcribe. If you're looking to explore open-source models as an alternative to commercial APIs, here's a rundown of the best speech models out there.
It’s clear that not all solutions are equal, but so long as your use case, budget, and priorities are clear, mapping the available providers to your unique needs will set your project up for success.
At Gladia, we’re no strangers to helping our customers make the right decision–whether that’s using our speech-to-text API or one of the providers listed above.
If you’d like to learn more about our offering including how we’ve adapted Whisper to real-life use cases and applied consistent accuracy, language support, and features such as real-time streaming and speaker diarization–let’s talk! Feel free to book a demo with one of our experts or get started directly.
Product News
Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.
Speech-To-Text
It’s that time of year again when we compile the top speech-to-text APIs to keep an eye on in 2025. Whether you’re looking to add voice-based AI into your products to automate customer support, enhance note-taking, supercharge your meetings, or more, this list will help you narrow-in on the right provider for your needs.
Speech-To-Text
Large Language Models (LLMs) are at the forefront of the democratization of AI and they continue to get more advanced. However, LLMs can suffer from performance issues, and produce inaccurate, misleading, or biased information, leading to poor user experience and creating difficulties for product builders.
