The main use cases for ElevenLabs include building voice agents and chat agents mainly within different industries, and the main structure that I'm using is basically defining a knowledge base and from there onwards building an agentic workflow.
Building multilingual voice agents has expanded my global reach and supports diverse audiences
What is our primary use case?
What is most valuable?
The user interface is pretty useful. It is amazing for technical people but also for non-technical users. I am a technical person, so I have the possibility to program using my JavaScript and Python knowledge. Furthermore, the setup in terms of agent by defining the knowledge base, by defining and uploading the different documents in terms of PDF files and Markdown files, is pretty useful. The overall workflow architecture is easy to use as well, which is why I love working with it.
In terms of content creation, ElevenLabs is pretty useful because it works with 42 languages, if I am not mistaken. I have tried some niche languages such as Bulgarian, Romanian, and Serbian, and the text-to-speech works amazingly because there are different accents. As a fluent Bulgarian, I can judge it quite well.
ElevenLabs' linguistics analysis is pretty useful when it comes to reaching a diverse audience. Different architectures and analysis features are available for use, and whenever I have the possibility to analyze the language, everything is there in terms of accuracy and when the code dropped. I would say it is pretty useful.
The multiple language and accent features of ElevenLabs have improved my global audience a lot. I am now able to work with different countries, and every time when they come with a specific language or accent, ElevenLabs provides it. In comparison to Cognigy, ElevenLabs' text-to-speech features are way better and way more reliable toward the client. In case there are some hallucinations, I can restrict the LLM so it speaks confidently and accurately to the client, ensuring their use cases can be solved successfully.
What needs improvement?
The workflow section, where I am building the bots, could use enhancements. Although it is made pretty easy, as a developer, I often prefer to see more freedom. I prefer not to rely solely on predefined architectures but rather to build the workflow from scratch.
For how long have I used the solution?
I have been working with ElevenLabs for seven months so far.
What do I think about the stability of the solution?
I have not had any crashes, downtimes, or performance issues with ElevenLabs yet.
What do I think about the scalability of the solution?
I find ElevenLabs to be scalable. With the plethora of languages implemented, its scalability can grow significantly as it allows me to approach different countries and clients. ElevenLabs provides amazing features for that.
How are customer service and support?
I have escalated questions to the customer service team of ElevenLabs, and I can attest to their tech support. In the beginning, I reached out because I was supposed to receive reference agents when I logged into the system but was not able to see them. Their technical support was pretty useful, replying within ten minutes on a workday. A call was scheduled the day after, and everything was set up super quickly. In contrast, with Cognigy, I have usually had to wait one to two days for the first reply.
What was our ROI?
I find ElevenLabs to be cost-effective so far as everything is positive. I have been working with it for a while, and I would say I need more time to figure out the ROI.
Which other solutions did I evaluate?
In comparison to Cognigy, which I also have experience with, the key differences lie in both pros and cons. Cognigy is a good start, while ElevenLabs requires more experience to pick things up quickly. For someone starting with building agents, I would say Cognigy serves as a solid base, with everything explained nicely and plenty of videos and courses to follow. In comparison, ElevenLabs mainly has documentation, which can be tough to read, and is not set up particularly well yet. Another benefit for Cognigy is that it allows building everything node by node and tool by tool, while ElevenLabs is set up in an agentic way from the beginning and then can be changed to a deterministic flow later on. One benefit for ElevenLabs is the analysis section, where it excels compared to Cognigy. ElevenLabs immediately provides the number of conversations, amount of calls, conversational topics, and main visualizations for understanding bot performance, which is much better than Cognigy, where connecting to a third party for analyzing results in an Excel sheet is required. There are pros and cons for both platforms, but so far, ElevenLabs is outperforming Cognigy.
What other advice do I have?
Everything is hosted within ElevenLabs platform itself, and I use different ones for each bot. For the last one, I have used GPT-5.4.
I would rate the tech support of ElevenLabs a perfect ten out of ten.
For organizations considering ElevenLabs, I would say to start as soon as possible because it is a pretty useful platform. A lot can be learned from it, even if you are not someone who has been building bots for a while. It offers plenty of possibilities to grow and learn from the platform itself, so you feel confident when someone asks you to build a use case. I would rate this review an eight out of ten.
