Whisper vs Deepgram

Most would agree that the defining moment of the modern AI era came when OpenAI released ChatGPT in November 2022. However, what many missed is that OpenAI had already quietly revolutionized speech-to-text (STT) technology two months earlier with the release of Whisper in September 2022. Suddenly, developers could access multilingual transcription that rivaled commercial services, in a way that was completely free and customizable.

This breakthrough sparked a classic tension that had been brewing for years. Companies like Deepgram—founded by physicists in 2015 looking to pioneer end-to-end speech models—had already proven that high-accuracy, real-time transcription was possible through managed APIs. But Whisper democratized the technology, giving any developer access to enterprise-grade capabilities for free.

Suddenly, teams faced the archetypal build-versus-buy decision: embrace Whisper’s open-source flexibility, or pay for battle-tested platforms like Deepgram that required no setup?

Today, we’re comparing Whisper and Deepgram to help you navigate the differences.

What is Whisper?

Trained on 680,000 hours of multilingual internet audio, Whisper was one of OpenAI’s most immediately impactful releases, democratizing high-quality speech-to-text for developers worldwide. Whisper’s launch in September 2022 was a watershed moment for the field of speech recognition. Unlike previous open-source models that struggled with real-world audio, Whisper delivered accuracy that rivaled commercial APIs. And, it was completely free.

Traditional speech recognition systems typically required separate models for different languages and separate pipeline stages for transcription and translation. Whisper handled it all with a single, unified model with good enough accuracy for production applications.

What are some advantages to Whisper?

Because Whisper is open-source, the model is free. The open-source license has also inspired an ecosystem of specialized variants, keeping Whisper competitive even as newer models emerged. Some of these popular variants include:

• Whisper.cpp reimplemented the model in C++ for dramatically faster CPU inference and smaller memory footprints, enabling practical deployment on edge devices including laptops and phones
• faster-whisper delivered up to 4 times faster performance for virtually the same accuracy while using less memory
• WhisperX enabled precise speaker diarization (identifying who said what in a transcript) and word-level timestamps, while securing performance enhancements by using faster-whisper under the hood

Because Whisper is open-source, developers who self-host it get full code-level control of customizing the model. This includes anything from fine-tuning the model to pairing it with other components (e.g. a proprietary turn detection model) in a custom STT pipeline.

Whisper is also relatively cheap to self-host for high-throughput use cases that can saturate the model. On Modal, you can process an hour of audio for a cent with L40S GPUs!

What are some disadvantages to Whisper?

Self-hosting Whisper requires more engineering effort than using a proprietary API like Deepgram. While transcribing a single file takes just minutes to set up, building a scalable STT service involves other considerations. Developers need to engineer for things like GPU resource management, concurrent request handling, and optimal batching strategies.

For real-time use cases, developers also need to write their own audio chunking logic.

In addition, unlike LLM inputs and outputs which are just lightweight strings of text, STT requires audio or video files that are heftier in size. It can be a pain to manage storage and data transfer of these files in a way that enables efficient processing.

Note that many of these challenges can be addressed by using a serverless GPU platform like Modal, which handles the orchestration of AI infrastructure for you.

Who uses Whisper?

Because Whisper is an open-source offering and not a managed service, OpenAI does not advertise customers directly. However, some companies known to be implementing Whisper include Snap (developers of Snapchat), Patreon, BlandAI, and Quizlet.

What is Deepgram?

Founded by physicists in 2015, Deepgram pioneered end-to-end speech models seven years before Whisper. Given its pioneer-like placement in modern STT’s history, Deepgram had a headstart at building a production-ready transcription service.

What are some advantages to Deepgram?

Deepgram claims that its newest model, Nova-3, has top-of-the-line accuracy, though anyone evaluating accuracy should do so on their own datasets. Regardless, Deepgram’s true advantage lies with ingestion: Novo-3 can support real-time streaming natively. Whisper, on the other hand, processes audio in 30-second chunks, and this chunking creates downstream delays (even in optimized implementations like WhisperX). Nova-3, meanwhile, streams audio continuously with words appearing as they’re spoken. This makes Novo-3 a good candidate for products that require real-time transcription like video call sidekick services or phone call answering systems.

For example, imagine implementing a live captioning system for a conference. Nova-3’s realtime processing would display words as the speaker says them, while Whisper’s chunking approach would deliver text every few seconds, creating an awkward delay for viewers.

While Whisper’s multi-lingual support was considered novel when it debuted, Novo-3’s multi-lingual support is stronger. For some language pairs, Deepgram claims to perform up to 8x better than Whisper.

Beyond speed, Deepgram’s platform includes production-ready features that solve essential pain points when self-deploying Whisper:

• Speaker diarization works out of the box (this is available with WhisperX but not Whisper)
• Automatic scaling (deploying Whisper requires managing your own infra, though serverless platforms like Modal simplify this)
• Enterprise SLAs and support

What are some downsides of Deepgram?

The downsides of using Deepgram are similar to those when using other proprietary model APIs.

First, you make yourself dependent on a third party. That means that you don’t have control over API changes or underlying model changes—the latter of which can result in unexpected changes to model outputs. At scale, you can also run up against rate limits.

Second, you are working with a black box. You have limited ability to customize the model. Let’s say you want to increase accuracy for domain specific vocabulary—Deepgram has a couple features (Keywords and Keyterm Prompting) to better identify up to 100 unique terms, but you can’t fine-tune the underlying model on your own datasets to generalize to a domain. On the data privacy front, you may also work in a sector where sending data to a third party black box is not permitted.

Finally, Deepgram is expensive. For batch use cases, it’s anywhere from 10x to 100x more expensive than hosting an ASR model yourself.

Who uses Deepgram?

Deepgram counts many customers in its base, including Twilio, Citi, NASA, Vonage, and Khoros. Deepgram’s customer base includes both enterprises and startups.

Comparing Whisper and Deepgram

To summarize, let’s break down a head-to-head comparison of Whisper and Deepgram with a table of the most pertinent metrics for scoring STT models.

* This estimate assumes the use of L40S GPUs on Modal, for a batch use case that maximizes throughput, and doesn’t take GPU cold start costs into account.

Note that benchmarking WER is not an exact science and is highly dependent on the shape of your dataset. Some models may be more accurate than others for your use case, so always benchmark on your own datasets!

In summary: which should you choose?

Start by evaluating your primary use case. If you need real-time transcription with minimal setup, then Deepgram’s managed API gets you up and running in minutes. If you need batch transcription at a >10x cheaper cost, have stringent data privacy requirements, or need full code-level control for fine-tuning, self-hosting an open-source model like Whisper is the better choice. You should also evaluate if any of the newer open-source ASR models might be even better for your use case.

For teams deploying Whisper, consider serverless GPU platforms that eliminate infrastructure management time and allow you to burst to hundreds of GPUs with no commitments. Modal, for example, handles automatic scaling, GPU provisioning, and monitoring, letting you focus on shipping transcription features for your AI product. Check out our Whisper inference example to get started.