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Summary
- ElevenLabs and Bland.ai are conversational AI platforms that allow users to develop customizable voice agents for various applications.
- ElevenLabs builds its own TTS and STT models in-house, offering latency advantages and enhanced control.
- Bland.ai provides customizable voice agents, primarily focusing on phone call automation and business process integration.
- Both platforms offer integration with external APIs and support for telephony systems, including Twilio.
Overview
ElevenLabs and Bland.ai are versatile conversational AI orchestration platforms that offer businesses the tools to build and manage voice agents. ElevenLabs stands out for its in-house development of TTS and STT models, which enhance latency and quality. Meanwhile, Bland.ai offers customizable voice agents tailored to telemarketing. Both platforms support API integration and provide telephony integration options, catering to various user needs.
Introduction to ElevenLabs and Bland.ai
Conversational AI orchestration platforms, like ElevenLabs and Bland.ai, enable developers to create customizable voice agents. These voice agents now handle customer support calls, train 911 dispatchers, and power new journalistic experiences.
Most platforms combine speech to text (STT), a large language model (LLM), and text to speech (TTS), along with built-in turn-taking and interruption handling, to support natural, human-like conversations. Many companies, like Bland.ai, partner with other organizations to provide each of these components externally and handle the orchestration of the various external processes. This has significant drawbacks in terms of reliability and latency.
In contrast, ElevenLabs is both a research and product company that creates foundational audio models and offers a packaged solution. This integrated approach allows ElevenLabs to optimize latency by eliminating the need for multiple server calls, providing users with the highest quality TTS and STT in-house, as well as ensuring better reliability.
Feature comparison
To gain a better understanding of how the two platforms compare, let’s take a look at their features side by side:
| Provider | ElevenLabs | Bland.ai |
|---|---|---|
| Includes an extensive voice library | Includes an extensive voice library with over 5,000 voices across 32 languages and numerous regional accents. Users can design new voices from a text prompt or clone their own. | Offers a library of human-like voices with basic customization. Voice cloning is available at an additional cost. |
| Latency | Uses the Flash model, which is the fastest, most human-like TTS available. Also has an advantage for end-to-end latency, saving two server calls through in-house TTS and STT. | Operates on self-hosted, end-to-end infrastructure for latency but relies on third party models. |
| Tools & API Calls | Provides server tools to call third-party apps or APIs to fetch real-time information or take actions. Also offers client tools to trigger browser events, run client-side functions, or send notifications to a UI. | Provides API access for developers to integrate AI phone call capabilities. Client tools are not supported. Custom prompts and conversational pathways can be created but may require coding expertise. |
| Languages | Offers thousands of voice across 30+ languages. Agents can be multilingual with custom voices for each language. ElevenLabs supports switching languages during conversations, unlike Bland. | Primarily supports English; multilingual support is available for enterprise clients at an additional cost. |
| Concurrency | Concurrency by tier for ElevenLabs base plans is available here. Custom limits are available to handle scale for the largest enterprises. | The standard plan supports up to 1,000 calls daily, while enterprise plans can handle up to 20,000 calls per hour. |
| LLM | Allows users to select from leading models from OpenAI, Anthropic, Google, and DeepSeek. Custom LLM integration is available at no extra cost. | Utilizes proprietary LLMs for lower-latency conversations and higher reliability. Custom LLM integration is available only for enterprise clients. |
| Knowledge Base Management | Allows users to import files, URLs, or plain text to equip their agents with relevant, domain-specific information. Offers low-latency retrieval augmented generation to ground conversations in enterprise data. | Supports integration with external APIs and knowledge bases to provide real-time information during calls. |
| Telephony Integrations | Offers PCM 8000 Hz or μ-law 8000 Hz sample rates for integration with any provider. For additional information, refer to the Twilio quickstart guide. | Integrates with existing telephony systems, primarily through Twilio. Custom telephony integrations are available for enterprise clients. |
| Data Retention | By default, ElevenLabs retains conversation data for 2 years. Users can modify this period to any number of days, unlimited retention, or immediate deletion. ElevenLabs offers a Zero Retention Mode which ensures data is never persisted and ensures HIPAA compliance. | Data retention policies are customizable, with options for immediate deletion or extended retention periods, depending on client requirements. |
| Tracking & Analytics | Offers real-time analytics and allows users to review past recordings, transcripts, and call summaries. Offers custom prompts to tag calls based on internal success criteria and extract data from transcripts. | Offers real-time analytics and call monitoring features. Post-call analysis tools are available to assess performance and gather insights. |
Final thoughts
The verdict is in.
Both ElevenLabs and Bland.ai provide powerful AI-driven voice solutions for various use cases. ElevenLabs offers a vast voice library, integrated STT and TTS services, and extensive language support, making it suitable for multiple applications.
In contrast, Bland.ai focuses on AI phone call automation with customizable prompts and pathways. These features may be appealing to enterprises seeking to automate telemarketing.
Ultimately, your choice between the two will depend on your specific requirements, such as language needs, customization capabilities, and integration preferences.
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