Speech-To-Text
When choosing a Text-to-Speech API, developers face crucial practical questions: Which provider delivers the right balance of latency, voice quality, control, and scalability in real production systems?
Speech-To-Text
Automatic speech recognition (ASR), aka Speech-to-Text (STT) technology, is a constantly evolving field. Knowing which ASR model is right for your product or service can be challenging. CTC, encoder-decoder, transducer, and speech LLMs—each with distinct tradeoffs. What does it all mean? And what do you choose?!
Speech-To-Text
Choosing between AssemblyAI and Deepgram for your speech-to-text needs often comes down to answering these critical questions:
When choosing a Text-to-Speech API, developers face crucial practical questions: Which provider delivers the right balance of latency, voice quality, control, and scalability in real production systems?
To find some answers, we tested multiple TTS providers across common developer workflows. Based on latency behavior, voice quality, speech control methods, language support, and integration effort, we shortlisted the top seven APIs. The comparison focuses on real execution behavior rather than isolated sample outputs.
In this blog, we break down how TTS APIs work and highlight the top services in 2026 based on latency, voice quality, flexibility models, and developer experience.
For speed, we evaluated multiple text-to-speech APIs and have shortlisted four providers, with the best performance, based on latency behavior, voice quality, speech control methods, and developer experience:
Produced highly natural and expressive voices. However, higher end-to-end latency makes it better suited for offline or non-real-time generation.
Stood out for reliability, predictable latency, and strong SSML support. It is well-suited for IVR systems and transactional voice use cases.
Delivered the most natural and expressive voices in our tests. It also offers strong multilingual support, but requires careful SSML tuning.
Showed strong real-time streaming behavior with smooth audio transitions. It fits well for low-latency conversational voice systems.
A text-to-speech API converts text handled by a system into synthesized audio through a programmatic interface. Developers send text and receive spoken output without building or maintaining speech synthesis models.
These APIs handle speech generation, scaling, and audio delivery. This allows teams to add voice output quickly while relying on managed infrastructure for reliability and performance.
Most modern TTS APIs support both batch generation and real-time streaming. This makes them suitable for voice agents, IVR systems, accessibility tools, and interactive applications.
Core capabilities typically include accepting plain text or SSML for speech control, selecting voices or styles, and returning audio as files or streams in formats such as MP3, WAV, or PCM.
With the above context in mind, let's dive into the best TTS APIs and understand when to use them.
ElevenLabs is recognized for producing ultra-realistic, highly expressive speech and advanced voice cloning capabilities. Its technology leverages neural network-based TTS models that closely mimic human intonation and prosody, making it particularly appealing for creative and content-driven applications.
Developers building interactive AI voice applications, conversational agents, or enterprise tools that need highly realistic, expressive speech.
The premium pricing tier may be higher than that of other providers. The ecosystem for enterprise integration is smaller compared with major cloud platforms.
ElevenLabs
Amazon Polly is AWS’s TTS service that combines neural and standard voice options, providing high-quality speech synthesis with reliable scalability. It fully integrates with the AWS ecosystem, enabling developers to leverage other cloud services, such as Lambda, S3, and Lex, for voice-driven applications.
Developers and teams already using AWS services, especially for voice assistants, IVR systems, or scalable SaaS applications.
The API relies on AWS infrastructure. Offline or on-premise capabilities are limited.
Google Cloud Text-to-Speech offers WaveNet-based voices and broad multilingual support, making it a strong choice for applications targeting international audiences. It integrates seamlessly with Google Cloud services, including Dialogflow, for conversational AI, and supports multiple voice variants per language.
Applications with a global reach requiring multiple languages and accents, and projects leveraging the Google Cloud ecosystem.
Voice customization is more limited compared with specialized providers. Some advanced features may require additional configuration.
OpenAI TTS is part of OpenAI’s audio API suite and is designed to work seamlessly with GPT-based conversational AI. Its simplicity and consistent quality make it ideal for developers looking to add voice output to interactive chatbots and virtual assistants.
Conversational AI projects, interactive virtual assistants, and applications that are already leveraging OpenAI models.
Options for voice cloning and custom voice creation are limited. Language coverage is limited compared with cloud-native providers.
Cartesia is purpose-built for ultra-low latency applications and prioritizes streaming-first TTS for real-time interactions. Its architecture allows speech to begin generating almost immediately, making it a top choice for telephony, live assistants, and interactive applications where responsiveness is critical.
Latency-sensitive use cases, including live telephony, call centers, and real-time AI voice agents.
Language coverage is smaller compared with major cloud providers. Advanced voice cloning features may not be available.
Microsoft Azure TTS provides enterprise-grade, neural network-based speech synthesis with extensive multilingual support and customizable voices. Its service includes Custom Neural Voices, which allow developers to create branded or unique voices. Azure TTS integrates seamlessly with other Microsoft Azure services, making it a strong option for large-scale applications.
Enterprise applications, global products requiring multiple languages, and projects needing custom voice branding.
Custom voice creation may require approval and compliance with ethical guidelines. Pricing can be higher for enterprise-tier usage.
IBM Watson TTS is a well-established service offering neural TTS voices, SSML support, and options for custom voice models. It focuses on delivering natural-sounding speech with reliable performance, suitable for business applications such as virtual assistants and interactive voice response (IVR) systems.
Business applications, IVR systems, chatbots, and enterprise voice deployments.
Fewer languages than cloud-native competitors; latency may vary depending on the deployment region.
Before choosing a TTS API, it’s helpful to review how the top providers stack up across key technical and practical criteria. Comparing voice quality, latency, language coverage, voice cloning, and pricing provides a quick reference for decision-making.
Moving on from architectural considerations into reviewing practical performance, this section grounds the discussion around real API behavior.
Design patterns and feature lists only go so far. Our evaluations revealed tradeoffs across providers. Some APIs prioritize speed, while others focus on richer, more natural voices at the cost of longer response times. Production choices depend on how TTS systems behave under real execution conditions.
We tested several text-to-speech APIs using the same end-to-end method to compare real-world performance. Latency refers to the total time it takes to send a request and receive the complete audio file. This includes network overhead, text processing, speech generation, audio encoding, and response delivery. It does not measure the time to the first audio.
Plain text synthesis exposed clear differences across providers.
Beyond average latency, execution patterns revealed additional nuance. In some services, SSML-based synthesis was faster than plain text, which runs counter to typical expectations. Individual runs also showed noticeable variability, with some requests completing much faster or slower than the mean. This behavior appears linked to internal stability adjustments and voice generation complexity, highlighting why averages alone do not capture real-world performance.
Voice quality varied more than latency across providers.
Some providers deliver highly natural, expressive voices that enhance engagement and increase response times. Others produce faster, more consistent output with simpler voices, which can feel robotic or less dynamic. Choosing a TTS API often requires developers to balance expressiveness against speed and consistency.
Control mechanisms differed across APIs.
All APIs were usable in practice, though setup effort varied. Cloud platforms required additional configuration steps, while API key–based services enabled faster iteration. Documentation quality was generally strong across providers.
During hands-on testing, parameter-based APIs were faster to iterate on because developers could adjust expressiveness without modifying the input text. SSML-based APIs often require more trial and error, as changes to pitch, rate, or emphasis in one part of the text could affect other sections. This increased testing time and introduced noticeable developer friction during fine-tuning.
Here is the breakdown of our findings:
These results provide a practical baseline. With individual TTS behavior understood, the next section examines how these systems fit into complete voice pipelines alongside speech-to-text.
Modern voice applications require bidirectional communication. Users speak, systems process the input, and responses are delivered as natural speech. Behind the scenes, this happens through speech-to-text (STT), which captures and converts spoken words into text, and text-to-speech (TTS), which turns written responses into audio. Well-designed pipelines ensure interactions feel smooth and human-like, which is essential for voice agents, IVR systems, and meeting assistants.
To build an effective voice pipeline, developers need to understand how the different components interact. This includes how STT captures input, how the system processes it, and how TTS generates the output. Breaking down these stages helps clarify design decisions and highlights key performance considerations for real-time applications.
A bidirectional voice pipeline starts when a user speaks. STT captures the speech and converts it to text. The application processes the text to determine meaning or intent. Finally, TTS generates the spoken response back to the user.
This loop enables real‑time conversational experiences. Voice agents, interactive voice response systems, and meeting assistants all rely on this pattern. Reducing delays at each stage makes interactions feel more natural and human‑like.
TTS and STT work together to create seamless voice interactions. STT converts the user’s speech into text, which the system then interprets to generate a response. Errors in transcription or delays in STT can affect how accurately and quickly TTS delivers the spoken reply.
Looking at both together helps maintain consistent timing and smoother conversations. Using TTS alongside a strong STT service, such as Gladia, improves transcription accuracy and reduces latency. This results in a better experience for real-time voice applications.
End‑to‑end latency is the total time from capturing a user’s speech to delivering the spoken response. It includes the time for STT, application logic, language processing, and TTS.
Streaming both STT and TTS can reduce the pause between user input and system response. Minimizing network hops, deploying services closer to users, and using efficient protocols like WebRTC also help. Real‑world latency tests show that well‑tuned pipelines can achieve sub‑1.5‑second end‑to‑end delays, improving user experience.
With the top 7 TTS providers in mind, here are the key factors to help you decide which one fits your needs:
Ultimately, the best TTS API depends on your project’s goals and constraints. For developers building full voice pipelines, combining TTS with real-time transcription provides insight into latency, accuracy, and overall responsiveness. On the STT side of your workflow, you can request a Gladia demo to evaluate real-time transcription alongside your selected TTS solution.
Yes, synthesized voices from commercial TTS APIs are typically subject to licensing terms that restrict usage, redistribution, and voice cloning without consent. Always review the provider's terms of service.
TTS APIs are managed services with hosted infrastructure, support, and usage-based pricing. On the other hand, open-source models require self-hosting and maintenance but offer more customization and no per-request costs.
Most commercial TTS APIs support SSML (Speech Synthesis Markup Language), which allows developers to control pronunciation, pauses, emphasis, and prosody for more natural-sounding output.
