Deepgram Reviews

For the last two years, our primary use case for Deepgram has been to power sophisticated, AI-driven voice bots for major US clients.

The technical workflow is as follows:

1. A client initiates a call to a Twilio number.
2. Our system captures the audio and streams it in real-time to Deepgram's Speech-to-Text service.
3. Deepgram transcribes the speech into text with high accuracy.
4. This text is then passed to a Large Language Model (LLM) to analyze and determine the user's intent.
5. Based on the identified intent, we trigger the appropriate backend functions to generate a relevant response.
6. Finally, we use a Text-to-Speech (TTS) engine, such as ElevenLabs, to convert the response back into audio and play it for the user.

The entire process is built upon the speed and reliability of Deepgram's transcription. Our environment is deployed on the Public Cloud, specifically using Amazon Web Services (AWS).

Of course. Based on my review, here are the features I've found most valuable:

• Continuous Innovation and Responsiveness: I find it incredibly valuable that Deepgram is not a static product. They are constantly evolving and genuinely listen to user feedback. The evolution from their Nova models to the new Flux model, which was specifically designed to solve end-of-speech detection for conversational AI, is a perfect example. It shows they are committed to solving real-world problems for their users.
  
• High Accuracy and Reliability: For my voice bot solutions, accuracy is non-negotiable. The models are remarkably accurate, performing at 90-92% efficiency even with challenging conditions like background noise and a wide range of international accents. Furthermore, the service has been incredibly stable; in my four years of using it, we've never experienced downtime.
  
• Excellent Configurability and Ease of Integration: Deepgram offers a level of granular control that allows me to fine-tune the STT engine's behavior, which is a significant advantage over competitors. This flexibility, combined with straightforward integration, extensive documentation, and robust code examples, allows my team to be highly efficient.
  
• Cost-Effectiveness and Scalability: The pay-as-you-go pricing model is both affordable and transparent. It provides a significant return on investment because it satisfies all our primary requirements—technical accuracy, ease of integration, and low implementation cost—within a scalable and predictable financial model.
  
• Outstanding Customer Support: The support team is brilliant and always ready to assist. Having access to official support channels, active community forums, and frequent webinars ensures that we are never without resources, which is crucial for a business-critical application.

Honestly, Deepgram has been exceptionally proactive in addressing the primary area that needed improvement. My main challenge was with the real-time detection of when a user has finished speaking in a live conversation, which is critical for a responsive voice bot. They directly solved this by releasing their Flux model.

Because Flux is a recent release, I haven't yet had enough time to thoroughly test it and identify new limitations. At this stage, any "improvement" would be more of a "nice-to-have" feature rather than a fix for an existing problem. The core service is already very robust and meets all of our current needs.

What additional features should be included in the next release?

Looking toward the future, here are a few features that could add even more value to an already excellent platform:

1. Advanced Built-in Analytics: While I can get the raw transcript and build my own analytics pipeline, it would be powerful to have features like sentiment analysis, emotion detection, or automatic summarization offered directly through the API. This would save significant development time.
   
2. More Granular Speaker Diarization: For calls with multiple participants, enhancing the real-time speaker diarization (labeling who is speaking) to be even more precise would be a fantastic addition for creating detailed call analyses.
   
3. Tighter Integration with TTS: Since Deepgram is also expanding into Text-to-Speech (TTS), offering a more seamlessly integrated STT-to-TTS pipeline could simplify the development stack for creating voice agents from start to finish.
   
4. Specialized, Pre-Trained Industry Models: While the general models are highly accurate, offering even more specialized, pre-trained models for specific industries like finance, healthcare, or legal-which are heavy on specific jargon-could push the accuracy even higher for those niche use cases.

I have been using the solution for four years.

Based on my experience, my impression is that the solution is exceptionally stable.

We have never experienced any downtime. Their service is very transparent, and they even provide a status page where you can check the availability of their systems. It's a reliable and robust platform that we can depend on for our business-critical voice bot applications.

We have never faced any issues with downtime or performance, even as our usage has grown. The architecture is clearly built to handle high volumes of real-time transcription. Furthermore, its pay-as-you-go, usage-based pricing model directly supports this scalability, making it financially viable to grow our services without being locked into a rigid plan. It's a system that scales seamlessly both technically and financially.

Based on my experience, the customer service and support from Deepgram have been outstanding.

The support team is brilliant, highly reachable, and always ready to assist whenever we have a question or need help. It's a comprehensive support system that goes beyond just a direct contact channel; we have access to official support, very active community forums, and they frequently schedule webinars to share announcements and updates.

I've always felt that there are plenty of resources available, and we've never been left without a solution. It's a very real and accessible support system - a simple email or call gets you the assistance you need.

Positive

Yes, I did. Initially, I used AssemblyAI in parallel with Deepgram while evaluating the best solution for our needs.

I made the switch to using Deepgram exclusively because of its superior configurability. While AssemblyAI is a solid product, I found that Deepgram provides a much deeper, more granular level of control. It allows me to fine-tune the behavior of the STT engine down to a micro-level, which is critical for optimizing the performance and accuracy of our voice bots. That ability to precisely tailor the service to our specific use case is why Deepgram ultimately stood out as the better choice for us.

The initial setup was very straightforward.

It was a simple "Do-It-Yourself" (DIY) process that our in-house team handled entirely on our own, without needing to involve any external vendors. The primary reasons it was so easy were the extensive resources Deepgram provides:

• Excellent Documentation: The documentation is clear, comprehensive, and easy to follow.
  
• Rich Code Samples: They have robust GitHub repositories filled with plenty of examples and code samples in multiple languages, including Python, Java, and JavaScript. This made integration into our existing systems much faster.
  
• Strong Community and Support: The availability of an active support community meant that if we had any questions, resources were readily available.

These factors combined made the implementation and integration process smooth and efficient.

We implemented the solution entirely with our in-house team. It was a straightforward process, and we did not involve any vendors.

Our return on investment (ROI) with Deepgram has been excellent, although I don't track it as a specific percentage. The value comes from several key areas:

1. Low Implementation Cost: The solution is very developer-friendly with great documentation, which allowed our in-house team to integrate it quickly without needing to hire external vendors. This significantly reduced our initial investment.
   
2. Cost-Effective Operational Model: The pay-as-you-go pricing is transparent and affordable. It scales directly with our usage, which means our costs are always aligned with our business volume, preventing large, unnecessary expenses.
   
3. High-Value Enabler: The primary ROI comes from the fact that Deepgram's high accuracy and reliability are the foundation of our voice bot service. It enables us to deliver a high-quality product to our clients, which in turn generates our revenue. The investment in Deepgram directly translates to our ability to operate and grow our business.

In short, the ROI is demonstrated by low initial costs, predictable operational expenses, and the high quality of the core technology that powers our entire service offering.

Yes, before committing to Deepgram as our primary solution, I evaluated other options. The main competitor I looked at was AssemblyAI.

I used both AssemblyAI and Deepgram in parallel for a period to directly compare their performance in our real-world use cases. While AssemblyAI is also a good service, I ultimately chose Deepgram because it offered significantly more configurability. This allowed me to fine-tune the Speech-to-Text engine at a much more granular level, which was crucial for achieving the highest possible accuracy and performance for our specific voice bot applications.

Yes, I absolutely have some advice for anyone considering or currently using Deepgram.

1. Don't Settle for the Defaults: The single biggest advantage of Deepgram over its competitors is its deep configurability. My advice is to really spend time with their documentation and API parameters. You can fine-tune the models to your specific audio environment, the accents you typically encounter, and the vocabulary relevant to your industry. This is where you can move from 90% accuracy to 95% or higher for your specific use case.
2. Stay Engaged with Their Updates: Deepgram innovates at a rapid pace. The release of the Flux model is a perfect example of how they solve real-world problems their users are facing. I highly recommend subscribing to their newsletters and attending their webinars. You might find that they've released a new feature or model that directly addresses a challenge you're working on, saving you significant development effort.
3. Leverage the Full Ecosystem: Think of Deepgram as the first crucial step in a larger data pipeline. The real power is unlocked when you connect its highly accurate transcripts to other services. As in my use case, feeding the text into an LLM for intent recognition, sentiment analysis, or summarization opens up a world of possibilities. You can analyze sales calls, automate customer support, or create detailed meeting summaries.
4. Use the Community and Support: Don't hesitate to engage with their support channels or community forums if you run into issues. My experience has been that they are incredibly responsive and helpful. The community is also active, and it's likely someone else has faced and solved a similar problem to yours.

In summary, my advice is to be an active user. The more you explore the platform's capabilities and stay current with its evolution, the greater the return on your investment will be. It's a top-tier solution that rewards a hands-on approach.

I'm a reseller and consultant, and I mainly use Deepgram as an AI voice integration service.

When I use Vapi AI to implement voice AI call solutions, the understanding of German in Nova 2 feels more reliable than the last version. This observation was from approximately two months ago when I used it.

I use Deepgram for voice integration when I have a customer getting voice calls, implementing solutions with AI. I use it for calling agents, cold calling agents, and mostly for speech to text. I always use it to transcribe the voice to LLM text streams. I also used it for a platform with web integration, allowing customers to schedule calls with AI coaches on different topics, where I did the transcription with Deepgram API integration into LiveKit so the system could understand what the customer is saying.

I started with Vapi AI, which introduced me to Deepgram. Now I have a private account there for other solutions than Vapi. Initially, Deepgram was the only reliable service for my needs, which is why I have always used it and never switched. I stick with Deepgram because it consistently works; I have no reason to test something else that might not be as effective.

The most important feature of Deepgram for me is the transcription service, along with the reliability of the service. If you integrate a service and it does not respond or has delays, if it's not reliable, it doesn't matter if it's high quality; it has to work every day so that my customers are always happy and not offended because nothing is working at times.

Deepgram's low latency transcription has greatly impacted my ability to deliver reliable voice agents and provided very good transcription. I showcased this on my website, allowing someone to call and have real-time transcription displayed, which was very beneficial. I didn't find a good service a year ago that could perform this, and without Deepgram, I probably wouldn't have sold any AI automation services.

Deepgram is very reliable for normal conversation, but when it comes to special names or information, I'm not entirely sure how to implement it perfectly. I did use a feature to highlight special words based on the topic, but I've seen overweighting of this topic, where normal words weren't recognized properly.

The handling of different topics needs to be optimized to be more accurate. I could upload lists with special words for specific topics. In my experience, it was not perfect, as there were only a few words that were understood properly. When I had an AI interview for coding, Deepgram didn't capture the names of programming languages or well-known LLMs accurately all the time. For example, when a person said "I'm experienced in Python development," Deepgram didn't get the word Python correctly.

When Deepgram Nova 3 was released, I experienced problems with the API, but it could be on the Vapi side; I'm not sure, which is why I stick with version two, but I think currently there is no problem anymore.

The setup could be easier, as everything is moving towards no-code solutions. You can configure everything properly on the dashboard and get a JSON file with the structured output to insert into other programs. If there were a way to configure special words for the API more readily, that would be helpful.

The initial setup needs to be optimized to be more intuitive.

I have been working with Deepgram for a bit over a year.

Deepgram always matched the workload, so I didn't have any performance issues.

Deepgram always matched the workload, so I didn't have any performance issues. In Germany, we face GDPR issues, which is annoying. We have to ensure services remain within EU borders. I'm not sure if Deepgram offers options to choose the server location, such as having a server in Frankfurt like AWS.

I haven't communicated with Deepgram's technical support at all. Until now, I've always found the documentation sufficient and managed without needing support.

The documentation is fine. Most of the time, I use AI to read it and implement directly. If my programming assistant can't find the information in the documentation, I go in and read it myself, pointing out specific topics for the AI to understand how to implement features. For me, the most important aspect of the documentation is that it is structured so that AI can read it effectively.

Neutral

I started with Vapi AI, which introduced me to Deepgram. Now I have a private account there for other solutions than Vapi. Initially, Deepgram was the only reliable service for my needs, which is why I have always used it and never switched. I stick with Deepgram because it consistently works; I have no reason to test something else that might not be as effective.

The setup could be easier, as everything is moving towards no-code solutions. You can configure everything properly on the dashboard and get a JSON file with the structured output to insert into other programs. If there were a way to configure special words for the API more readily, that would be helpful.

The initial setup needs to be optimized to be more intuitive.

I've seen a return on investment with Deepgram. Last week, I sold an agent for 2,500 and included Deepgram in the configuration. The customer had tested different solutions and found them inadequate. I hosted a demo service for my agent using Deepgram, and the customer tested it through a link on my landing page. He stated that the performance was significantly higher than elsewhere, and he found it suitable for his needs. Deepgram is part of it, contributing to my revenue.

If the handling of the German language improves continuously, it would be beneficial for me.

I always use Krisp or something similar as a second service to filter out background noise, but I haven't checked if different languages or speakers are detected properly.

On a scale of 1-10, I would rate Deepgram a 9.

We are currently using Deepgram for text-to-speech, specifically the Nova model. Our usual use cases of Deepgram involve text-to-speech, primarily focusing on building speech-to-speech interactive AI chatbots.

The most valuable capabilities of Deepgram that I've found so far include low latency, as it offers less than 200 milliseconds, which is not provided by any other text-to-speech models. The variety of voices is also good compared to others.

The positive impact and benefits I've seen from working with Deepgram include the very human-like and realistic quality of the voices. Deepgram's low latency has significantly improved our organization's customer service, as, to the best of my knowledge, it offers a very low latency compared to all text-to-speech providers, which I appreciate.

One issue we've faced relates to the pricing structure; with the pay-as-you-go model, we only get eight concurrent connections for web sockets for text-to-speech, which makes it difficult to scale. For enterprise, the annual fee is around $25,000 to $30,000 USD, regardless of usage, which allows for 100 concurrent connections, but still doesn't provide enough scalability when we're using a lot.

I have noticed that the web socket connection sometimes breaks due to inactivity, and increasing the timeout period would be beneficial. Additionally, if you leave the web socket connection with the TTS model for a certain period, it loses the connection. Even when used continuously for long periods, it occasionally gives an error. During a call with the support team, they acknowledged that there is an issue on their side. We have the intention to improve or optimize stability for future releases, but since Deepgram is not open-source, we don't have much control over that.

I have been working with Deepgram for close to two years.

We didn't have any issues integrating Deepgram's API into our existing workflows because the documentation is straightforward.

Deepgram has been stable and reliable; I haven't seen any issues except for some occasional connection losses.

We haven't seen a return on investment with Deepgram so far; we have been building POCs for the last two years but recently switched to AWS in the last two months due to scalability issues with the pay-as-you-go model. We are no longer using Deepgram, as AWS provides higher scalability with 10,000 connections at a single go, despite higher latency than Deepgram.

Based on my experience with them, I would rate the technical support a nine.

Positive

We didn't try Deepgram's domain-specific vocabularies feature. We started with Deepgram and then switched to AWS.

I didn't personally participate in the initial setup of Deepgram.

We took Deepgram directly from the source and did not purchase it via any marketplace.

We haven't seen a return on investment with Deepgram so far; we have been building POCs for the last two years but recently switched to AWS in the last two months due to scalability issues with the pay-as-you-go model.

We did not evaluate other options or vendors before choosing Deepgram. The factors we considered before choosing Deepgram included the lower error rate compared to other available providers and the very low latency of around 200 milliseconds, which is significantly less than other models in the industry at that time.

We are an end-user of Deepgram, using it as a service. Regarding transcription, we haven't used speech-to-text models; we focus on text-to-speech, which has been good, and I haven't noticed any errors there. I have about five years of experience in my current field. Overall, based on my experience, I would rate Deepgram an eight.

My main use case for Deepgram is creating voice agents to automate the customer support part and reply to FAQs and customer queries. Deepgram has multiple models, speech to text and text to speech models, which we create workflows on. We also use it for personalized voice agents.

We have seen a significant change in our organization as most of the customer queries have now been automated using Deepgram voice agent. Human interaction has been reduced and customer query resolution has been increased rapidly.

We have tracked a reduction of around 70% in the support cost and direct human interaction for support. Since we have saved resources, less human support is needed.

Deepgram offers a complete all-in-one platform to create voice-based AI agents for production-ready use cases. What I appreciate most about having an all-in-one platform for our use case is that it offers everything, so latency is very low and it is very seamless to use. The all-in-one platform comes with a studio, a playground to test, and proper documentation and tutorials.

Deepgram has a vast UI and a vast range of models, but there could be a simpler version for creating AI agents rather than providing a full-fledged platform for minimal use cases.

It could be multilingual, as in India, there are multiple several languages. If it had support for many more languages, especially regional languages, it would be valuable.

I have been using Deepgram for more than one year.

Deepgram is stable. We have not faced any downtime.

Deepgram is highly scalable at the production level because they have their own servers.

I feel the customer support is quite good. We have not had an opportunity to interact with the support team.

Positive

We used ElevenLabs before Deepgram, but ElevenLabs is more expensive than Deepgram, which is why we switched. Additionally, the latency is higher for ElevenLabs, and Deepgram offers lower latency with the same quality.

My experience with pricing, setup cost, and licensing is that pricing is seamless and customizable as needed. Currently, we use the growth plan. For enterprise, they offer a higher tier, so it is clear and everything is mentioned in their documentation.

Before choosing Deepgram, we evaluated Google AI Studio and ElevenLabs. We have used ElevenLabs but chose to stick with Deepgram. We also considered Vapi.

My advice for others looking into using Deepgram is that if they want a lower latency platform than competitors with the same quality and no compromise in quality, Deepgram is a seamless solution to have and use.

Obviously, the 70% reduction in human interaction in customer support has been achieved and time has been saved as humans are not required. Resource cost has been reduced since human cost is more expensive than the voice agent. I would rate this product an 8 out of 10.

I use Deepgram for a company that requested me to implement an AI voice agent for a security application that warns other neighbors of near alerts of some incidents that may occur in their neighborhoods.

I implemented this in January 2025, using Deepgram as a transcriber for those conversations for three months, and I love the technology because it transcribes very well all the conversations, making the implementation relatively easy.

My main use case for Deepgram is just for transcribing, and since this company is a Spanish company, I got deep into some use cases and settings configurations to adjust those transcriptions that include both Spanish and English words.

Deepgram handled one of these bilingual conversations by adjusting some settings, such as the name of the company being in English while the conversation was in Spanish, so we needed to configure it to transcribe accurately because Vapi utilized that transcription for the LLM agent to speech those words through an agent voice. Regarding my experience with those bilingual transcriptions, I think the transcriptions were quite precise, and while there is room for improvement, the results met expectations, making Deepgram a good fit for that work.

The best features Deepgram offers for me include mainly the transcription option, which I think is the robust solution among other providers since Deepgram does the job quite well.

Deepgram's transcription stands out compared to other solutions primarily due to its speed and accuracy; those are important points for me because not all providers or tools handled Spanish well, but Deepgram adjusted perfectly for that use case, and we also chose 11Labs voice, a South American voice, which worked very well with Deepgram.

Deepgram has positively impacted my organization by achieving our desired results, which is very good from the overall technology perspective, saving a lot of time for the support team since the voice agent replaced the human agents managing the calls, thus improving response time and reducing the time dedicated by those human agents.

Regarding improvements for Deepgram, I think the quality of the transcriptions could be enhanced, as the Spanish accent poses challenges, making it harder to transcribe some words, and considering additional accents from Chilean or Argentine speakers could improve the model's performance with local words.

I don't have any additional improvements for Deepgram besides those I mentioned earlier about Spanish accents and transcription quality.

I have been working in my current field for 14 years, and since I am 40, I have quite a bit of experience.

In my experience, Deepgram is very stable, as I haven't encountered any downtime or issues.

Deepgram's scalability has been fine; there were some limit issues with Vapi, but those issues stemmed from the Vapi platform and not Deepgram itself.

I did not interact with customer support for Deepgram, so I cannot comment on my experience with them.

Neutral

Before Deepgram, we used other transcribers, but I don't remember the specific ones because they didn't work so well, prompting us to switch.

I have seen a return on investment in terms of time saved and fewer employees needed for both.

My experience with pricing, setup cost, and licensing was good, as I found it to be cheaper without any problems.

Before choosing Deepgram, I did evaluate other options, but it was mainly a decision based on the integration with Vapi.

My advice for others looking into using Deepgram is to read the documentation because the API is very flexible, and I encourage them to just test it out as it's a wonderful technology.

I was offered a gift card in AWS for this review.

On a scale of 1-10, I rate Deepgram a 9.

My main purpose for Deepgram was to convert meeting voices to text very easily, and the other purpose was for content creation. I mostly use Deepgram for those two purposes.

The best features of Deepgram for me are the level of transcription accuracy it provides and the amount of time it saves. While editing audios for content creation, I need specific audios to connect with clients, customers, or prospective customers. Deepgram helps tremendously in making customization of those audios.

I normally use Deepgram with masculine and feminine voices according to my needs, and by changing the accents and characteristics. This helps a lot with my work.

Deepgram assists my team significantly by improving efficiency and productivity.

Even though Deepgram has many customization options, I wish that Deepgram had voice cloning customization to a much larger extent. I also wish that the price were a bit lower if possible.

I have been using Deepgram for about a month now.

For those with basic needs of converting voice to text or transcription tools, Deepgram is a viable option. If you are looking for very specialized needs, then I think Deepgram needs to improve in that area.

I give Deepgram an overall rating of 9 out of 10.

We primarily use the solution for transcribing speech to text. We use it to record phone calls and meetings and then transcribe them.

The quality of the product is the most valuable aspect. The recognition of industry-specific terminology phrases and abbreviations is really important for us. We were able to get a good level of industry specificity with Deepgram.

Two things come to mind for improvement. Maybe they have fixed these, or maybe there is something new, and we haven't implemented it yet. 

One improvement could be dual-channel audio. We've had issues in the past where it generates the transcript, and a lot of the text is duplicated. I understand why it would happen. It's an audio file with more than one channel of the same speaker, which is what may cause the duplicated text. That said, it would be great either to have a way for Deepgram to realize that it's basically the same audio on two channels and only transcribe one of them or at least give us a warning that it's happening. We've found workarounds, however, a better solution from Deepgram's side would be great. 

The other issue comes up when some changes are made on their end, and we want to test them. We've had one to two instances where they tell us that we have access, and we try to test something out, and it turns out we don't. When that happens, then they have to fix something on their end. It's not a big deal. We have a Slack channel with them where we can quickly touch base. We let them know, and they will get back to us and fix the access. It's not something we're doing very often. 

We've been using the solution for about a year and a half.

The solution is stable. We've had no real issues. The only issues that come up more in the testing space are when we're looking at a new model that we want to test out. The issue in that case is more about access where we think we have it and then we don't. That's not a stability issue, however. 

The scalability is significant. We have grown significantly with it and I don't see any reason why we can't keep growing with it. It doesn't require many people to have it up and running. The output that we get from Deepgram is used by a large number of people. Day to day, it's really very hands-off. 

Either before we set up the contract with Deepgram, or right after, they set up a Slack channel with us that had a bunch of people from their end - including customer service and their operations team. We had a call or two with them to get started, and we have been able to quickly correspond with them to get answers or send them details via Slack. That's been very helpful. Even now, we can jump onto the channel and send them questions. They send out updates via email as well.

I can't say if having a Slack channel with them would be considered an extra cost or if it is available to any customer or not. Maybe if you have a premium program or custom model, it's definitely included. 

Positive

Previously, we were using human transcription, which was a lot more expensive.

The initial setup isn't any different from any other speech-to-text solution out there. My understanding is that it wouldn't be extremely difficult. 

It was implemented by one of our developers in-house and I was involved as well. The Deepgram team was very supportive. It depends on the use case, however, in our case, it doesn't require a big team to set up or manage. 

We had our in-house development team involved to get it up and running.

The ROI has been excellent. The cost is night and day compared to the cost of human transcription. We're spending maybe a tenth of the cost we would if we were still doing manual transcriptions.

The pricing was very good. Although the competitors also would have saved us a lot of money, we were mainly looking for the right level of quality of the transcript. 

We compared several other automated transcription services similar to Deepgram a little over two years ago. We're constantly testing out others. We find Deepgram to be the highest quality. That was really important to us.

Amazon AWS has some sort of solution that we tested out. I don't remember the names of the others. We might have tested something from Google and some other lesser-known options.

Being able to access it and send recordings was the same level of work for our development team. So it came to the quality and the ability to build a custom model. We gave them some data and some audio file samples that they used in order to come up with a custom model that would perform even better for us. 

We're a Deepgram customer. 

We use Deepgram for speech-to-text. There are different models you can use in order to do that. We have a custom model and we have also tested out some of their other models as well. 

We're really happy with it. Technology in this space is constantly changing, and we see what's happening with ChatGPT, for example. Everyone uses different kinds of platforms for transcription. Deepgram also has a lot of other solutions we're not using, and it supports other languages, which is not how we're using it. However, it will likely continue to improve itself and align with all of the advances in the space. Deepgram has been really great for our use case. 

I interact with Deepgram's team and the team internally that has implemented it. I don't have hands-on experience with it myself. I continue to review the quality of the transcripts, which is what we're using it for. That said, my understanding is that the ease of use would depend on your experience and your skill level. It is in line with everything else out there. 

I'd recommend the solution to others. I'd rate it nine out of ten. We evaluated and compared different solutions based on the quality of speech-to-text and implementation. For quality, Deepgram definitely came out above everything else. The implementation also went well. Support has been good - not that we've needed it too often. Compared to what we had before, which wasn't a technological solution, the difference is night and day. The other automated transcription services we've looked at in the past few years just weren't good enough. Deepgram really offers great quality. 

I am the software development team lead in my company, and we are creating a speech recognition product based on a few different engines. The company works in the legal process, so my team generates a machine-based transcript and then converts it to a readable format. It saves time for the person using the machine-based transcript as a starting point.

The solution's most valuable feature is its speed of transcription. It is one of the fastest tools, especially if you compare it to the second fastest solution that you can get, which is 20 times faster. Thus, it is not just a marginally faster product.

In comparison to Deepgram, I would say that the transcript accuracy offered by other products is much higher. In our company, we had five jobs, and each job was generated by two different engines. One of the aforementioned engines was Deepgram, and the transcribers had two versions, and the users didn't know which one was which version. After the aforementioned process was carried out, the users had to choose which version they thought was less work to convert to the final or the perfect legally binding document since the tool was getting paid per page. Deepgram had a clear incentive to do some little work as fast as possible and to get a particular amount of money. In our company, we didn't have a huge sample size because we are a small company. We did five jobs with five different transcribers, and each job had two versions. A blind test was done, and we found that the other tools were marginally more accurate than Deepgram.

I would like it to be more accurate. I can get maintainability and faster transcripts with the perfect features with an improved tool.

I have been using Deepgram for a year. I use the solution's latest version. I am an end-user of the tool.

I don't use all of the features in the tool because Deepgram adds new features every few months. The features that I have been using in the tool have been very stable. I have never had any issues with the tool and it has never crashed. In our company, we just update to the latest version, and see if there are no issues until the next update.

I use the tool as a software developer and transcriber. Though I don't know the number of transcribers that work in our company , I can say that it runs into hundreds.

I contacted the solution's technical support for help, and I got a nice, decent service and had no complaints at all. At the moment, whenever you want to update Deepgram by yourself, it is a very easy process. You just get the version of the tool and pull it from Docker. If you want to update the model, you have to contact the support anyway. I haven't contacted the support team very often. I might have contacted the product's support team three times just to update the new versions of the model. I got decent support from the tool's support team.

My company still uses multiple products in parallel to Deepgram. For example, in transcript-related business, some clients want to get the transcript super fast or in a few hours. You need to produce the machine transcript in a few minutes and give it to the transcriber, who should start working immediately on it. In such cases, there is a huge improvement when one uses Deepgram, which is a major advantage.

If I consider and compare the other engines I have used with Deepgram, I would say that the ease of installation is one of the strong points of the product. Compared to all other engines, the installation of Deepgram has been simpler and far more stable. It just gets updated, and it runs properly. The good thing is that the tool is modular. In the tool, the modules and the engine itself are separate objects. If you want to update only the module, then there is no need to redeploy anything since most other engines that we have in our company are on an on-premises model.

The solution is deployed on on-premises and cloud environments. It is deployed in the private section of our company's cloud. We aren't using the API and prefer to use our own deployment.

When using Deepgram, one needs to pay for the hours or minutes for which the transcription is needed. The more hours you commit to in advance, the cheaper the price. It is slightly cheaper than the other engines we used. You should take into account that you usually pay for cloud resources or even if you are doing just an on-premises deployment, and since it is a fast process, theoretically, you can save twice, like, once on the money you pay for usage and the second time you pay for cloud resources because if you can, like, finish transcribing in a minute instead of an hour, you can know, if your pipeline scales down, you will only pay for that minute.

Whether you should use the tool or not depends on your use cases and the main use cases where it is used. Based on the engines and factors like maintainability or how easy it is to maintain, and if you consider them to be your priorities, you definitely go with Deepgram.

Speaking about how Deepgram handles large volumes of audio data without compromising inaccuracy, it is always a trade-off. As I said, Deepgram is not as accurate as the other engines we're using, but the difference is marginal, and if speed is more important to you, you should go with Deepgram. I think if the accuracy of the transcript is far more important, other engines give better results.

For an end user, the tool offers on-premises deployment, and it also has an API. Using API is super easy since you just log in to the site and create an account, and you can start using it. If you want to deploy it on an on-premises model, you need to have a basic understanding of the cloud and how it works. The tool has a step-by-step guide on its website, which is very nice, and I still use it even because it is super simple and can be easily understood.

We haven't actually used our AI features at the moment because we're not quite sure how we can use them in our company because we are working on the legal transcript, and those have to be developed word by word even if the person who speaks, says certain things incorrectly. We have to maintain 100 percent accuracy. I am not sure how we can apply AI features in the tool, but we are always looking at the AI aspect in our company.

Deepgram offers some clear advantages over other applications. If you want a tool that produces transcripts in a very fast manner, there is nothing that comes even close to being disputed with Deepgram.

I rate the tool an eight or nine out of ten.

I use Deepgram for audio transcriptions and speech recognition. I am working on a feedback survey app where users provide verbal feedback that Deepgram transcribes into text. 

We receive the results and implement features like punctuation and Smart Format.

Deepgram has significantly improved our transcription process in terms of speed and accuracy. It has allowed us to efficiently convert verbal feedback into text, enabling quicker analysis and implementation of new features. 

Integrating Deepgram has streamlined our workflow, enhancing productivity and delivering more accurate transcription results.

We previously used IBM Watson, which was slow and had limitations in accurately transcribing words. After evaluating OpenAI's Whisper model, we discovered Deepgram, which incorporates Whisper and adds the powerful Nova model.

Deepgram's latency is impressively low, around 0.5 to 1 second, making it a superior choice.

Live transcription could be improved. Sometimes, Deepgram's WebSocket is disposed of due to redundancy issues. Enhanced stability in live transcription would be beneficial.

I have been using Deepgram for one and a half years.

Initially, we encountered some stability issues, but Deepgram has since improved its architecture. With the addition of hooks for status updates, the accuracy has improved to approximately 90 to 95%, which is better than other models we've tested.

It's scalable. Our platform handles 50 to 60 users simultaneously without compromising accuracy. For instance, a 20-minute audio file was transcribed within a second, demonstrating its ability to handle large volumes of audio data effectively.

My experience with customer service and support has been positive. They are responsive and helpful, and they provide timely resolutions to any issues.

Positive

We previously used IBM Watson, but it didn't deliver appropriate results. We searched for alternatives and found OpenAI's Whisper model, which was initially slow. After thorough analysis, we discovered Deepgram. It proved to be superior, leading to our decision to migrate. We used a detailed spreadsheet to compare various models before making the switch.

Thanks to clear documentation, the initial setup was very easy. If you have prerequisite knowledge of the programming language you're using, it’s straightforward to follow the documentation and implement it into your system. When I started, I closely followed the documentation, which made the process very manageable.

Deployment model: We last deployed it on the Google Cloud Platform (GCP).

The implementation was done in-house.

Our ROI has increased due to enhanced transcription accuracy and speed, leading to more efficient workflows and better user satisfaction.

The pricing is moderate. While live transcription may incur some charges when the connection is open, they become minimal over time. So, it's a balanced option—neither cheap nor overly expensive.

Yes, besides IBM Watson, we evaluated OpenAI's Whisper model.

Deepgram is highly recommended. Users don’t need to do anything special before using it, as the documentation is comprehensive. I am a Node.js developer and have used Deepgram packages for Node.js. Understanding your programming language is key, whether it's Node.js, Python, or others.

AI Features:

I have integrated various AI models into our application. Deepgram's sentiment analysis feature allows us to create graphs and analyses to determine if words are positive, negative, or neutral. This helps us summarize feedback and derive actionable insights.

My ratings:

I would rate it an eight out of ten. The live transcription feature needs improvement as the WebSocket sometimes gives errors or breaks down during live streams.

We use the solution for TTS (Text-to-Speech) and STT (Speech-to-Text) purposes.

The solution's Speech-to-Text conversion feature is really awesome.

Deepgram is currently restricted to only the English variants, but it should include other languages, such as German or French.

I have been using Deepgram for five to six months.

Deepgram is a stable solution.

The Deepgram cloud can handle large volumes of audio data. Around three to four people use the solution in our organization.

The solution’s initial setup is easy.

Deepgram is a cheap solution. We can create an account for $200, which we can initially use for the Deepgram services.

I have used Deepgram with Twilio for the calling system. I would recommend Deepgram to users who want to use it for speech-to-text purposes.

Overall, I rate the solution an eight out of ten.