Realtime API vs Whisper vs TTS API: What's the difference for voice AI?

Everyone's chasing that perfect customer support experience: an AI that just gets it, responding instantly and naturally. The goal is a seamless conversation where a voice AI understands the problem and solves it right away. But actually building that is a whole different story. The tech is complicated, and your first big decision, how to piece it all together, is one of the most important you'll make.

You've probably come across the main options: the old-school method of stringing together separate Whisper (for speech-to-text) and TTS (for text-to-speech) APIs, and the newer, all-in-one Realtime API.

This guide will walk you through these options, compare the good and the bad, and help you figure out if it's worth building a solution from the ground up or using a platform that does all the heavy lifting for you.

What are these APIs?

Before we get into a big comparison, let's quickly get on the same page about what each of these things actually does. Once you get what they do individually, it’s much easier to see how they work together (or why they sometimes don’t).

What is a Text-to-Speech (TTS) API?

A Text-to-Speech (TTS) API is what turns written text into spoken audio. It’s the "voice" of your AI, reading out the generated response for the user to hear. There are plenty of options out there, like OpenAI's TTS, ElevenLabs, and Google TTS. Quality and cost can be all over the map. For example, some users have found that OpenAI's TTS is way cheaper than ElevenLabs, costing around $0.015 per minute while some of ElevenLabs' plans can run you over $0.10 per minute.

What is the Whisper API?

The Whisper API is OpenAI’s well-known Speech-to-Text (STT) model. It does the exact opposite of TTS: it takes spoken audio and transcribes it into written text. This is the "ears" of your AI. It listens to what a user says and translates it into text that a large language model (LLM) can actually understand. While Whisper is a popular choice, it isn't the only one. Alternatives like Deepgram and Google Speech-to-Text have their own strengths when it comes to accuracy, speed, and price.

What is the OpenAI Realtime API?

The OpenAI Realtime API is a more recent, end-to-end model built to handle the entire conversation in one shot. It takes audio in and gives audio out, basically bundling the jobs of STT, LLM processing, and TTS into a single, streamlined process.

The big win here is that it was designed from the ground up for low-latency, real-time chats. It can handle interruptions and even pick up on emotional cues in someone's voice, which is something the chained-API approach really struggles with.

The traditional approach: Chaining Whisper and TTS APIs

For a long time, if you wanted to build a voice agent, you had to wire together a bunch of separate services. This "STT → LLM → TTS" pipeline is flexible, but it comes with some serious drawbacks that can make or break the user experience.

How the traditional STT → LLM → TTS pipeline works

The whole thing is a multi-step chain reaction, and every single step adds a little bit of delay:

1. A user speaks. Their audio gets captured and sent to an STT API like Whisper to be turned into text.

2. That text transcript is then fed to an LLM, like GPT-4o, to figure out what the user meant and come up with a response.

3. Finally, the LLM’s text response gets sent over to a TTS API, which turns it back into audio for the user to hear.

It seems logical enough, but in a real conversation, all those little delays add up and create a lag that you can really feel.

Pros and cons of the traditional pipeline

So, why would anyone go this route? It really boils down to one word: control.

• Pros:

  • Total Control: You get to pick and choose what you think is the best model for each job. You could use Deepgram for its amazing STT, GPT-4o for its brainpower, and ElevenLabs for its super realistic voices.

  • Flexibility: You can stick custom logic in between the steps. For instance, after transcribing the user's speech, you could run a script to check your customer database before the LLM even sees the text.

• Cons:

  • Painfully High Latency: This is the big one. Chaining APIs creates that awkward "walkie-talkie" feeling where users can't naturally interrupt. The total time from when a user finishes talking to when they hear a reply can easily stretch to over a second, which just feels clunky.

  • It's Complicated: Juggling three separate API calls, handling potential errors for each, and stitching it all together is a ton of engineering work. This isn't something you knock out over a weekend.

  • You Lose Important Info: When you turn audio into plain text, you throw away a lot of useful information. The LLM might see the words "I guess that's fine," but it has no idea if the user said it with a frustrated sigh or a cheerful tone. That context is just gone.

The modern approach: A single Realtime API for voice

To crush the latency problem and make conversations feel more human, end-to-end models like OpenAI's Realtime API have really shaken things up. This method is fundamentally different from the old pipeline.

How the Realtime API streamlines voice conversations

Instead of passing data between different models, the Realtime API uses a single, multimodal model (like GPT-4o) that was trained to understand audio directly and generate audio responses. It all happens over a steady connection, which lets audio flow back and forth continuously.

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This gets rid of all the handoffs between different services, which dramatically cuts down on latency. OpenAI says the average response time is just 232 milliseconds. It also allows for cool features like Voice Activity Detection (VAD), which helps the AI know when a user is done talking, and the ability to handle interruptions smoothly, just like in a real chat.

Pros and cons of the Realtime API

This might sound like the perfect solution, but there are still a few trade-offs to think about.

• Pros:

  • Super Low Latency: This is the main reason you'd use it. Conversations feel fluid and natural, a lot closer to how people actually talk.

  • Deeper Understanding: Because the model "hears" the audio directly, it can pick up on tone, emotion, and other little things in the user's voice. This can lead to more empathetic and aware responses.

  • Much Simpler: From a developer's point of view, it's just one API call. That’s a whole lot easier than managing a three-part pipeline.

• Cons:

  • Less Control: You're basically locked into OpenAI's ecosystem. You can't just swap out their speech-to-text or text-to-speech parts if you find something you like better.

  • A Bit Unreliable: It's still pretty new tech, and it's not perfect. Users have run into bugs like the AI voice cutting out mid-sentence or the VAD being a little flaky.

  • It Can "Paper Over" Mistakes: Sometimes the transcription underneath isn't perfect. While the powerful LLM can often guess the user's intent anyway, this can sometimes lead to the AI answering a slightly different question. One analysis from Jambonz.org found that while the conversational flow was excellent, the actual transcription accuracy wasn't as good as competitors like Deepgram.

Realtime API vs Whisper vs TTS API: A practical comparison

So, how do you actually pick one? It all comes down to what you’re trying to do. Let's compare these two approaches based on what matters most for a customer support team.

Cost breakdown and predictability

Cost is a massive factor, and with APIs, it can get complicated fast. The traditional pipeline means you're paying for at least three different things:

• STT: OpenAI's "gpt-4o-transcribe" is about $0.006/minute.

• LLM: GPT-4o costs $5 per million input tokens.

• TTS: OpenAI's TTS is around $0.015/minute.

The Realtime API makes billing a bit simpler, but you're still paying for audio and text tokens. For instance, with GPT-4o, audio input tokens can be $40 per million. The main point is that with any API-level approach, costs are tied to usage and can be really hard to predict, especially if your support volume suddenly spikes.

Development complexity and control

To be blunt, the traditional pipeline gives you more control but demands a dedicated engineering team to build, maintain, and tweak it. It’s a pretty big investment.

The Realtime API is much easier to get started with if you just want a basic voice agent. But it gives you less visibility and control over what’s happening behind the scenes. You're completely dependent on OpenAI to fix bugs and add key features that are still missing, like speaker diarization (telling who is speaking when).

The real challenge beyond APIs: Do you build or buy?

Looking at all the technical details, one thing becomes pretty clear: building a high-quality, reliable voice AI agent from scratch is a huge undertaking. You have to:

• Choose, integrate, and manage a bunch of complicated APIs.

• Deal with real-time audio streaming and all the headaches that come with it.

• Connect the AI to all your knowledge sources, like help docs, old tickets, and internal wikis.

• Build custom workflows for escalations, ticket tagging, and routing.

• Keep a constant eye on performance and unpredictable costs.

This is a full-time job for an entire engineering team, pulling them away from working on your actual product. This is where using a platform becomes a much more attractive option. Instead of trying to build the engine from scratch, you can just get in and drive.

That's exactly why we built eesel AI. We handle all the messy, underlying AI complexity so you can focus on what you're best at: delivering incredible customer support.

While we've been talking about voice, the core problems of integration, knowledge management, and workflow automation are the same for text-based support, too. With eesel AI, you get an AI agent that plugs right into your existing helpdesk and knowledge sources in just a few minutes.

• No complex engineering: Our one-click integrations with tools like Zendesk, Freshdesk, and Intercom mean you can be up and running in minutes, not months.

• Unified knowledge: We automatically train the AI on your past tickets, help center articles, and internal knowledge from places like Confluence or Google Docs. There’s no manual training or setup needed.

• Total control: Our workflow engine is fully customizable, letting you decide exactly which tickets the AI handles and what it can do, all from a simple dashboard.

• Predictable cost: We offer straightforward plans with no hidden per-resolution fees, so you won't get any nasty surprises on your bill at the end of the month.

Choose the right path for your AI strategy

The choice between the Realtime API vs Whisper vs TTS API really comes down to your goals and your resources.

• The traditional STT+TTS pipeline gives you the most control but comes with high latency and a lot of complexity.

• The Realtime API offers a much more natural conversational feel but is less flexible and still needs a lot of development to become a fully working support agent.

For most support teams, trying to "build" this yourself is a costly and time-consuming distraction. A platform like eesel AI gives you all the power of a custom-built AI solution with the simplicity of an off-the-shelf tool. You can automate your frontline support, give your human agents a boost, and make customers happier without writing a single line of code.

Ready to see how easy it can be?

Start your free trial and launch your first AI support agent in minutes with eesel AI.