Amazon Transcribe vs Deepgram comparison

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Amazon Transcribe

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Executive SummaryUpdated on Apr 6, 2025

Amazon Transcribe and Deepgram are competing products in the automatic speech recognition market. Deepgram seems to take the lead due to its superior real-time processing and nuanced accuracy.

Features: Amazon Transcribe supports multiple languages, integrates seamlessly with AWS services, and offers an easy-to-use interface, making it ideal for AWS users. Deepgram is known for its speed, accuracy, and ability to handle complex audio through end-to-end deep learning models, which are advantageous for tech-focused industries.

Room for Improvement: Amazon Transcribe could enhance real-time transcription capabilities, improve integration flexibility beyond AWS, and refine its AI models. Deepgram might benefit from expanding language options, simplifying its technical setup, and providing detailed user documentation to assist those less familiar with technical deployments.

Ease of Deployment and Customer Service: Amazon Transcribe offers streamlined deployment within the AWS environment and comprehensive support for AWS users. Deepgram features a flexible API and responsive personalized customer service, but may require technical expertise for optimal deployment.

Pricing and ROI: Amazon Transcribe is cost-effective with scalable options, catering to budget-conscious enterprises. Deepgram targets clients desiring advanced functionality, with higher initial costs that potentially offer greater long-term ROI through superior transcription accuracy and efficiency.

To learn more, read our detailed Amazon Transcribe vs. Deepgram Report (Updated: December 2025).

Amazon Transcribe vs. Deepgram

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon Transcribe

Ranking in Speech-To-Text Services

4th

Average Rating

8.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Deepgram

Ranking in Speech-To-Text Services

1st

Average Rating

8.4

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Text-To-Speech Services (4th), AI Customer Support (8th), AI Sales & Marketing (3rd), AI Scheduling & Coordination (1st)

Mindshare comparison

As of December 2025, in the Speech-To-Text Services category, the mindshare of Amazon Transcribe is 10.9%, down from 22.0% compared to the previous year. The mindshare of Deepgram is 20.1%, up from 5.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Speech-To-Text Services Market Share Distribution
Product	Market Share (%)
Deepgram	20.1%
Amazon Transcribe	10.9%
Other	69.0%

Speech-To-Text Services

Featured Reviews

Anubhav Garg

Senior Software Developer at a tech vendor with 10,001+ employees

Efficient voice-to-text conversion enhances communication and advertising efforts

The valuable aspect of Amazon Transcribe is its ability to perform speech recognition and convert it into text. It's highly compatible with a serverless environment, making it easy to trigger the service and get results. Although no specific features handle diverse accents or dialects effectively, the scalability and ease of use are notable. It provides the best results for our needs, is highly scalable, and easy to manage. The service also benefits from cost savings, being a pay-as-you-go model with very reasonable pricing for audio transcription at $0.004 per second.

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Arunkumar HG

Technology Architect & Hands-On Leader | Prototyping, Automation, AI/LLM Integration | 20+ Years in at a consultancy with 1,001-5,000 employees

A Powerful, Adaptable, and Constantly Evolving STT Solution for Voice Automation

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: * 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. * 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. * 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. * 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.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The service also benefits from cost savings, being a pay-as-you-go model with very reasonable pricing for audio transcription at $0.004 per second."

"Amazon Transcribe helps me not to fall behind in a meeting and not know what's going on. Even if I do, I have the transcript at the end to help me figure out what was said during the meeting."

"We don't run into any issues with bugs or glitches."

"The feature I utilized the most was transcription."

"The results I get with Transcribe are near-perfect—over 99% better than what I have experienced before."

"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 features that I have been using in the tool have been very stable."

"The best thing with Deepgram is they are continually evolving and doing a lot of market research, and they take feedback seriously."

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

"Deepgram's low latency transcription has greatly impacted my ability to deliver reliable voice agents and provided very good transcription."

"Deepgram is able to handle large volumes of audio data without compromising accuracy."

"The speed of the solution for transcribing videos is good."

"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."

More Deepgram pros

Cons

"There is a need to improve the processing of background noise. Sometimes, surrounding sounds are recorded and Amazon Transcribe does not process these well, creating clutter."

"I would love to see Amazon Transcribe have its own section or its own page about how to make adjustments if you're using it for accessibility."

"Amazon S3 offers something like uploading parts, where a large file is divided into smaller parts, uploaded faster, and later reassembled. A similar feature in Transcribe would really help, making it easier to upload large file sets without spending extra time."

"The UX and UI could be improved on the AWS console."

"Several AWS products are originally built in English and not in other languages. There is room for improvement in creating more products in Spanish for Spanish-speaking countries."

"We've had issues in the past where it generates the transcript, and a lot of the text is duplicated."

"The area of live transcription could be improved. Sometimes, Deepgram's WebSocket is disposed due to redundancy."

"The solution does not properly identify the number of speakers."

"I would like it to be more accurate."

"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."

"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."

"The traditional Speech-to-Text doesn't understand when the user is done speaking in bot conversations."

"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."

More Deepgram cons

Pricing and Cost Advice

"I think the price on the standard is better for Amazon Transcribe than it is for Amazon Polly."

"The solution’s pricing is cheap."

"The pricing is moderate."

"Deepgram is a cheap solution."

"When using Deepgram, one needs to pay for the hours or minutes for which the transcription is needed."

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Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

12%

University

10%

Manufacturing Company

University

10%

Comms Service Provider

10%

Computer Software Company

10%

Financial Services Firm

10%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	8
Large Enterprise	1

Questions from the Community

What is your experience regarding pricing and costs for Amazon Transcribe?

The pay-as-you-go model is cost-effective, with pricing for audio transcription around $0.004 per second.

What needs improvement with Amazon Transcribe?

There is a need to improve the processing of background noise. Sometimes, surrounding sounds are recorded and Amazon Transcribe does not process these well, creating clutter. Adding functionality t...

What is your primary use case for Amazon Transcribe?

We are using Amazon Transcribe ( /products/amazon-transcribe-reviews ) to convert voice to text. For example, we communicate over the phone, record the call, and then convert the conversation into ...

What is your experience regarding pricing and costs for Deepgram?

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

What needs improvement with Deepgram?

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 ...

What is your primary use case for Deepgram?

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 neighbor...

Microsoft Azure Speech Service vs Amazon Transcribe

Comparisons

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Compared 26% of the time

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Compared 14% of the time

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Compared 10% of the time

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Compared 25% of the time

Microsoft Azure Speech Service vs Deepgram

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AssemblyAI vs Deepgram

Compared 11% of the time

Google Cloud Speech-to-Text vs Deepgram

Compared 9% of the time

Google Cloud Text-to-Speech vs Deepgram

Compared 7% of the time

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Product Reports

Speech-To-Text Services

Download Amazon Transcribe product report

Download Deepgram product report

Overview

Amazon Transcribe makes it easy for developers to add speech-to-text capability to their applications. Audio data is virtually impossible for computers to search and analyze. Therefore, recorded speech needs to be converted to text before it can be used in applications. Historically, customers had to work with transcription providers that required them to sign expensive contracts and were hard to integrate into their technology stacks to accomplish this task. Many of these providers use outdated technology that does not adapt well to different scenarios, like low-fidelity phone audio common in contact centers, which results in poor accuracy.

Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, to automate closed captioning and subtitling, and to generate metadata for media assets to create a fully searchable archive. You can use Amazon Transcribe Medical to add medical speech to text capabilities to clinical documentation applications.

Amazon Web Services (AWS)

Deepgram stands out for its speed in transcribing videos and speech to text, leveraging cutting-edge models like Whisper and Nova for exceptional performance and accuracy. Its latency is remarkably low, enabling swift transcription that users find superior to alternatives.

Deepgram provides an efficient solution for transforming video and audio content into text, benefiting from its advanced ability to recognize industry-specific terminology. Users experience faster results compared to IBM Watson and OpenAI's Whisper model, with low latency contributing to its appeal. However, challenges in speaker recognition and language support remain areas for improvement. Additionally, stronger spelling and grammar accuracy could enhance its performance. Some seek expanded multi-language capabilities and improved manageability during testing phases, noting its slightly less accuracy compared to other tools.

What are Deepgram's most notable features?

Rapid Transcription: Utilizes cutting-edge models for quick speech-to-text conversion.
Industry Terminology Recognition: Excels in comprehending specific jargon and abbreviations.
Low Latency: Offers transcription with minimal delay, approximately 0.5 to 1 second.
Model Integration: Employs Whisper model combined with Nova for high accuracy.

What benefits should users look for when evaluating Deepgram?

High Speed: Significant improvement in processing time over competitors.
Performance Satisfaction: Users appreciate faster and more fluid transcription.
Textual Accuracy: Enhancements can lead to more reliable outputs in transcripts.
Streamlined Processes: Features like punctuation and Smart Format boost efficiency.

Deepgram is widely implemented across industries for transcribing speech to text, often used by organizations for generating machine transcripts of legal proceedings and other vital communications. Teams deploy it on local systems to convert videos and phone calls, integrating speech recognition seamlessly into applications.

Sample Customers

Echo360, VidMob, RingDNA, Isentia

Information Not Available

Amazon Transcribe vs. Deepgram