ElevenLabs

What is ElevenLabs?

ElevenLabs is an AI voice platform for teams that need speech to sound convincingly human, whether that means a support agent answering calls in real time, an app reading text aloud in 74 languages, or a person with voice loss speaking again in a digital version of their own voice. It was founded in 2022 by Piotr Dąbkowski and Mati Staniszewski, childhood friends from Warsaw, Poland, with an early mission centered on making high-quality content accessible across languages. In a short time, it grew into one of the best-funded companies in voice AI, with backing from Sequoia, Andreessen Horowitz, and ICONIQ Growth, more than $781 million in funding, and reported annual recurring revenue of $330 million as of early 2026.

What stands out in our research is that ElevenLabs is not just a text-to-speech API anymore. The company now spans three product tracks: ElevenAPI for developers, ElevenAgents for conversational agents across phone, chat, email, and WhatsApp, and ElevenCreative for creators working with speech, music, images, and video. That breadth matters because many teams start by testing a voice in a demo, then end up needing transcription, voice cloning, monitoring, channel deployment, and compliance controls. ElevenLabs is built to keep those teams on the same platform as they grow.

The platform is used by a wide mix of customers. Duolingo uses it for language learning voices. Revolut uses it for customer interactions at very high volume. Perplexity uses it for its AI-generated podcast work. The Ukrainian government has used it to add audio interfaces to public services. There is also a quieter side of the company that matters just as much: its Impact Program has helped restore more than 3,000 voices for people with permanent voice loss. That range, from enterprise automation to accessibility, tells you what ElevenLabs really is. It is voice infrastructure, not just a voice generator.

Key Features

• Multiple speech models: ElevenLabs gives builders a real choice instead of one default model. Eleven v3 supports 74 languages and focuses on emotional range and context, Eleven Flash v2.5 delivers sub-75 ms time-to-first-audio for live conversations, and Multilingual v2 supports 29 languages for expressive long-form content. That matters because a phone agent, an audiobook narrator, and an in-app assistant do not need the same trade-offs.

• Real-time speech-to-text with Scribe v2 Realtime: The speech recognition side supports 90+ languages and delivers partial transcriptions in about 150 ms. In practice, this is what makes interruption handling possible, users can cut off an agent naturally and the system can react fast enough to feel conversational rather than scripted.

• Voice cloning: Instant Voice Cloning can create a usable clone from as little as 30 seconds to 1 minute of audio. For teams building branded assistants or accessibility tools, this lowers the barrier dramatically, but our research also shows quality depends heavily on source audio quality, with ElevenLabs recommending 1 to 2 minutes of clean audio and specific recording levels between -23 dB and -18 dB RMS.

• Large voice library and marketplace: Paid users get access to a library of more than 10,000 voices, and the marketplace adds nearly 10,000 community-contributed voices on top. This saves teams from recording custom voices for every project, and it creates unusual depth in accents, styles, ages, and character voices that many competitors still struggle to match.

• Emotional control with Audio Tags: Eleven v3 supports tags like [excited], [hesitant], [sigh], or [calm] embedded directly in text. This is one of the clearest differences between ElevenLabs and utility-focused TTS providers, because it gives builders a way to shape delivery line by line instead of just picking a voice and hoping for the best.

• Omnichannel agent deployment: With ElevenAgents, one agent can be deployed across phone, web chat, email, and WhatsApp. For support teams, this reduces the usual fragmentation where voice bots, chatbots, and messaging automations all live in separate systems with different logic and different analytics.

• Streaming and latency optimization: ElevenLabs supports streaming audio, WebSocket-based interaction, and model choices built around latency constraints. The company has published technical guidance around streaming, parallel processing, and edge deployment, which tells us it understands real-time voice as a systems problem, not just a model benchmark.

• Dubbing and translation: The dubbing product supports 29 languages and can preserve the original speaker’s voice characteristics while translating video content. This is why companies like Perplexity use ElevenLabs for international content reach, it turns one piece of media into many without rebuilding the production process from scratch.

• Developer SDKs and integrations: Official support includes Python and JavaScript for the core API, plus agents libraries for JavaScript, React, React Native, Python, Swift, Kotlin, and Flutter. There is also Zapier support for 8,000+ apps, which matters for teams that need voice systems to connect to CRMs, help desks, scheduling tools, and internal workflows.

• Safety and compliance controls: ElevenLabs requires AI disclosure for agent deployments, offers an AI Speech Classifier to detect audio generated on its platform, and supports enterprise needs around SOC 2, HIPAA, and GDPR in the right plans and setups. For any company deploying voice at scale, these are not side features, they are part of whether a project can ship at all.

Use Cases

One of the clearest enterprise stories in our research comes from Revolut, which uses ElevenLabs infrastructure for voice agents handling millions of customer interactions weekly. That is the kind of deployment that tests more than voice quality. It tests latency, consistency, monitoring, and whether the system can hold up when conversations are no longer demos but daily operations. For visitors building customer support agents, this is one of the strongest signals that ElevenLabs can operate as production infrastructure.

In healthcare, EliseAI reports 66% lower cost per call and says 88% of calls are handled by AI using ElevenLabs agents in healthcare contexts. That is an unusually concrete outcome. Healthcare is also one of the harder sectors to serve because voice agents need to sound calm, accurate, and trustworthy while fitting into scheduling and privacy-sensitive workflows. The fact that ElevenLabs has a dedicated medical answering service story, rather than just generic voice tooling, says a lot about where the product has matured.

In education, Duolingo uses ElevenLabs for language learning experiences. This is a strong fit for the platform’s multilingual depth and expressive delivery. A language app does not just need words spoken correctly, it needs voices that sound natural enough for learners to internalize pronunciation, rhythm, and emotion. That is a different standard from a utility IVR.

In media, Perplexity AI uses ElevenLabs for "Discover Daily," described as the first AI-generated podcast with dynamic content and voice personalities. That is a good example of ElevenLabs being used not just to read text aloud, but to create a repeatable content workflow where voice is part of the product itself. Washington Post, HarperCollins, and Bertelsmann are also named customers, with Bertelsmann reporting 36 businesses using ElevenLabs for content creation.

There are also accessibility stories that are more personal than corporate. Through the Impact Program, ElevenLabs provides free access for people with permanent voice loss, blindness, and nonprofits in healthcare, education, and culture. The company says it has restored more than 3,000 voices. This is one of the most compelling real-world use cases we found because it shows the platform doing something voice AI is uniquely suited for: preserving identity, not just automating tasks.

Strengths and Weaknesses

Strengths:

• It covers the whole journey from prototype to enterprise deployment. Many teams start with text-to-speech, then discover they also need transcription, voice cloning, agent orchestration, channel deployment, and analytics. ElevenLabs has built around that progression. Our research shows teams can start on the free tier, move into API work, then scale into enterprise plans with SLAs and compliance support without changing platforms.

• The voices are expressive in a way many enterprise tools still are not. This comes through most clearly in Eleven v3 and its Audio Tags feature. Compared with Azure Text-to-Speech, our research suggests ElevenLabs is stronger on naturalness and emotional delivery, while Azure is stronger on pronunciation precision. If your product needs empathy, narration, or character, ElevenLabs has the better story.

• It is strong in multilingual work. Eleven v3 supports 74 languages, Scribe handles 90+ languages, and dubbing supports 29 languages. That is why customers ranging from Duolingo to Perplexity and public-sector deployments like the Ukrainian government show up in the research. For global products, ElevenLabs feels built with multilingual expansion in mind, not patched into English-first tooling later.

• The company has real enterprise traction, not just hype. Revolut, Square, Duolingo, Meesho, Immobiliare, and major publishers are not edge-case logos. They point to a platform that has crossed into mainstream adoption. The company’s scale, $781 million in funding and reported $330 million ARR, also matters here because buyers of infrastructure care about vendor durability.

• Voice cloning is unusually accessible. Being able to clone from 30 to 60 seconds of audio is a meaningful usability advantage. Older systems often needed far more data and more technical setup. For accessibility, creator workflows, and branded assistants, that makes experimentation much easier.

Weaknesses:

• It is not the absolute winner on every technical axis. Our research points to Cartesia as faster in some scenarios, with around 90 ms time-to-first-audio and a voice-agent-specific focus. If your project lives or dies on latency, for example live interruption-heavy calling, Cartesia deserves a serious look.

• Some competitors may beat it on raw voice quality or openness. Fish Audio reportedly ranks above ElevenLabs in TTS-Arena blind tests and offers an open-source model plus more than 2 million community voices. For teams that want self-hosting or more control over the stack, ElevenLabs can feel more closed and platform-centric.

• Pricing can get expensive if your agents are verbose. ElevenLabs charges by characters, and support agents can burn through those fast. A team that lets its AI produce long-winded responses may discover the bill is driven as much by prompt design as by user volume. This is manageable, but it is a real operational constraint.

• Voice cloning quality is very sensitive to input quality. The marketing story is "clone from a minute of audio," but the practical story is stricter. If the audio is noisy, inconsistent, or emotionally uneven, results will also be inconsistent. Teams often underestimate how much recording discipline is needed for a reliable clone.

• The product has become broad enough that it can feel like a platform, not a simple tool. That is a strength for larger teams, but a downside for buyers who just want one narrow capability and no surrounding ecosystem. Someone who only needs the fastest possible TTS endpoint, or only wants a large voice catalog, may find a specialist competitor easier to reason about.

Pricing

• Free: $0 Includes 10,000 characters/month. Good for evaluation and quick prototypes, but not enough for serious production traffic.

• Starter: $5/month Includes 30,000 characters/month. This fits light personal use or early testing, though most app teams will outgrow it quickly.

• Creator: $11/month Includes 100,000 characters/month. A practical entry point for active development or modest content production.

• Pro: $99/month Includes 500,000 characters/month. This is where many serious pilots will land, especially if you are testing with real users.

• Scale: $330/month Includes 2,000,000 characters/month. Better suited to high-volume deployments and larger teams.

The important part is not just the monthly fee, it is the character model underneath it. A 5-minute podcast episode can consume roughly 17,500 characters, and customer service conversations can run 5,000 to 20,000 characters of output. That means costs can ramp faster than teams expect once an agent is live and users start talking a lot.

There are a few pricing details worth watching. Flash v2.5 uses a 0.5x credit multiplier, so it costs half as many credits as standard models. That can materially change economics for real-time agents. Overages also matter, for example the Pro plan overage rate is cited at $0.30 per 1,000 characters, so going 100,000 characters over would add $30. Voice cloning and dubbing have separate pricing logic, and enterprise API deployments may move into custom infrastructure pricing. Compared with alternatives, ElevenLabs is generally competitive, but character-based billing means product design choices, especially response length, directly affect spend.

Alternatives

Cartesia is the alternative we would point to first for teams obsessed with latency. Its architecture is built around speed, and our research cites roughly 90 ms time-to-first-audio. If you are building a voice agent where every pause feels costly, Cartesia may be the cleaner fit. ElevenLabs still has stronger breadth, more expressive voices, and a more developed ecosystem, but Cartesia is the sharper specialist.

PlayHT is a better-known option for teams that care about voice variety and broad language coverage for content workflows. It offers 600+ voices across 140+ languages and also supports conversational use cases with PlayDialog and Twilio integration. Compared with ElevenLabs, PlayHT feels more creator and marketing oriented, while ElevenLabs feels more balanced between creators, developers, and enterprise agent teams.

Fish Audio is the one to watch if you care most about voice quality benchmarks, lower cost, or self-hosting flexibility. Our research notes that Fish Audio tops some blind quality tests and offers an open-source model plus a huge voice community. ElevenLabs still has stronger enterprise packaging and a more mature agent platform, but Fish Audio may appeal more to teams that want lower-level control.

Azure Text-to-Speech is the conservative enterprise choice. It offers dependable infrastructure, strong pronunciation accuracy, and easy adoption for companies already deep in Microsoft’s ecosystem. The trade-off is that it tends to sound more mechanical and less emotionally rich than ElevenLabs. If your use case is transactional and precision-first, Azure can be enough. If voice personality matters, ElevenLabs usually tells the better story.

FAQ

What is ElevenLabs best for?

We think it is best for apps and teams that need natural-sounding speech, multilingual support, or voice agents across multiple channels. It is especially strong when voice quality is part of the user experience, not just a utility feature.

Is ElevenLabs just a text-to-speech tool?

No. It also includes speech-to-text, voice cloning, dubbing, and a full agent platform called ElevenAgents for phone, chat, email, and WhatsApp.

How do I get started?

Start on the free tier and test a few voices with your actual content. If you are building an app, the next step is usually a small API prototype using the Python or JavaScript SDK.

How long to set up?

Simple text-to-speech testing takes minutes. A basic API integration can be done in a day or two, while a production voice agent with business systems, disclosures, and monitoring will take longer.

Does ElevenLabs support real-time voice agents?

Yes. Eleven Flash v2.5 is designed for real-time interactions with sub-75 ms time-to-first-audio, and Scribe v2 Realtime can return partial transcriptions in around 150 ms.

How many languages does ElevenLabs support?

It depends on the feature. Eleven v3 supports 74 languages, Scribe supports 90+ languages, and dubbing supports 29 languages.

Can I clone a voice?

Yes. Instant Voice Cloning can work from 30 seconds to 1 minute of audio, though better recordings usually lead to better results.

Is ElevenLabs good for enterprise use?

Yes, based on the customers in our research, including Revolut, Square, Duolingo, and major publishers. It also offers enterprise support for compliance and dedicated infrastructure.

What does ElevenLabs cost in practice?

Small experiments are cheap, but production costs depend on how many characters your app generates. Verbose agents can become expensive faster than teams expect.

Is ElevenLabs the fastest option?

Not necessarily. It is fast enough for many real-time use cases, but Cartesia is a stronger candidate if minimum latency is your top requirement.

Is ElevenLabs the best quality option?

It is one of the strongest mainstream options, especially for emotional delivery. That said, our research found Fish Audio scoring higher in some blind quality tests.

Does ElevenLabs have safety controls?

Yes. It requires disclosure for AI agents, offers an AI Speech Classifier for detecting audio generated on its platform, and has moderation and policy controls around misuse.