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AI · head to head

Deepgram vs Keras

Deepgram logo

Deepgram

AI

Voice AI API platform for speech-to-text, text-to-speech, and voice agents

From
Free
Free plan
Yes
Full reviewWebsite
Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Deepgram pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: Deepgram covers Flux speech-to-text, Keras covers Sequential and Functional API.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Deepgram and Keras actually diverge.

Attributes where Deepgram and Keras differ
AttributeDeepgramKeras
Pricing modelusage-basedopen-source
Platformsweb, apiPython, Google Colab, Jupyter
CategoryAIMachine Learning
FoundedUnknown2015

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Deepgram

  • Flux speech-to-text
  • Flux text-to-speech
  • Voice Agent API
  • Real-time and batch processing
  • Self-hosted deployment
  • Audio intelligence

Only in Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

What people use each for

The jobs each tool is most often brought in to do.

Deepgram

  • Building real-time voice agents for customer supportnot Keras
  • Transcribing recorded audio at scale via batch STTnot Keras
  • Adding conversational text-to-speech to voice applicationsnot Keras
  • Self-hosting speech models for data residency requirementsnot Keras

Keras

  • Machine learningnot Deepgram
  • Data analysisnot Deepgram
  • Model trainingnot Deepgram
  • Predictive analyticsnot Deepgram

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Deepgram

  • Pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.
  • The Growth plan requires a minimum $4K/year commitment to unlock discounted rates.
  • Enterprise features and custom SLAs require a direct sales conversation rather than self-serve signup.
  • Some promotional per-minute rates are time-limited, meaning long-term pricing may differ from current promotional rates.

Keras

  • Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • Error messages can be vague and unhelpful, making debugging challenging
  • Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch

Pricing, plan by plan

Deepgram

Free
  • Pay As You Go$undefined/mo
    • $200 free credit to start
    • No minimums or expiration
    • No credit card required to start
  • Growth$undefined/mo
    • Save up to 20% with annual pre-paid credits
    • Minimum $4K/year commitment
    • Credits applied against actual usage
  • Enterprise$undefined/mo
    • Custom pricing for large-scale deployments
    • Dedicated support and contracts

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

Which should you pick?

Choose Deepgram if

  • You need flux speech-to-text.
  • You want to start without paying.
  • You work on web, api.
  • You also want flux text-to-speech.
Deepgram review

Choose Keras if

  • You need sequential and functional api.
  • You want to start without paying.
  • You work on Python, Google Colab, Jupyter.
  • You also want pre-built neural network layers.
Keras review

Questions people ask

Is Deepgram or Keras better?
Neither clearly leads. Deepgram starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Deepgram or Keras?
Deepgram starts at Free and Keras at Free.
Does Deepgram or Keras run on more platforms?
Deepgram runs on web, api. Keras runs on Python, Google Colab, Jupyter.
Can I use Deepgram for free?
Both have a free tier, so you can try either at no cost before committing.
What is Deepgram best used for?
Deepgram is most often used for building real-time voice agents for customer support, transcribing recorded audio at scale via batch stt, adding conversational text-to-speech to voice applications, self-hosting speech models for data residency requirements. Of those, building real-time voice agents for customer support and transcribing recorded audio at scale via batch stt are not what Keras is typically brought in for.
What can Deepgram do that Keras cannot?
Deepgram covers Flux speech-to-text, Flux text-to-speech, Voice Agent API, Real-time and batch processing. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning.

Answered from the vendors’ own pages

Deepgram: What does Deepgram cost?

Deepgram uses usage-based pricing starting with $200 in free credit, pay-as-you-go rates per minute or per character, a Growth plan with annual pre-paid credits requiring a $4K/year minimum, and custom Enterprise pricing.

Source
Keras: What is Keras?

Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.

Source
Deepgram: Is there a free plan, and what are its limits?

New users get $200 of free credit with no credit card required, which can be applied to speech-to-text, text-to-speech, or voice agent usage before any payment is needed.

Source
Keras: What model architectures does Keras support?

Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.

Source
Deepgram: How is usage metered?

Usage is metered per minute of audio for speech-to-text and voice agent calls, and per 1,000 characters for text-to-speech, with add-ons like redaction and entity detection billed separately per minute.

Source
Keras: Can Keras models run on TPUs and GPUs?

Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.

Source
Keras: Does Keras offer pre-trained models?

Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.

Source
Keras: Who should use Keras?

Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.

Source
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