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Machine Learning & Data Science · head to head

Keras vs Together AI

Keras logo

Keras

Machine Learning & Data Science

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite
Together AI logo

Together AI

AI Tools

Open-source AI at scale

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; Together AI fine tuning carries a minimum charge of $4.00 per job regardless of dataset size
  • They diverge on capability: Keras covers Sequential and Functional API, Together AI covers Open-source models.

Where they differ

Only the attributes on which Keras and Together AI actually diverge.

Attributes where Keras and Together AI differ
AttributeKerasTogether AI
Pricing modelopen-sourceusage-based
PlatformsPython, Google Colab, JupyterApi, Cloud
CategoryMachine Learning & Data ScienceAI Tools
Founded20152022

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 Keras

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

Only in Together AI

  • Open-source models
  • Fine-tuning
  • Fast inference
  • Embeddings
  • REST API
  • Python SDK
  • OpenAI compatible
  • Api support

What people use each for

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

Keras

  • Machine learningnot Together AI
  • Data analysisnot Together AI
  • Model trainingnot Together AI
  • Predictive analyticsnot Together AI

Together AI

  • Serverless inference against open source chat, vision, embedding, image and video modelsnot Keras
  • Renting dedicated single tenant H100, H200 or B200 GPU clusters by the hournot Keras
  • Fine tuning open weight models on a per token basisnot Keras

Where each one falls short

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

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

Together AI

  • Fine tuning carries a minimum charge of $4.00 per job regardless of dataset size
  • Reserved GPU commitments beyond 180 days are priced by contacting sales with no published rate
  • Volume and enterprise discounts are quote only with no published threshold
  • Reserved dedicated inference pricing is contact sales while only on demand rates of $5.49 to $8.99 per GPU hour are published

Pricing, plan by plan

Keras

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

Together AI

Free
  • FreeFree
    • $5 credits
    • API access
  • Pay-per-use$0.2/per-million-tokens
    • All models
    • Fine-tuning

Which should you pick?

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

Choose Together AI if

  • You need open-source models.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want fine-tuning.
Together AI review

Questions people ask

Is Keras or Together AI better?
Neither clearly leads. Keras starts at Free and Together AI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Keras or Together AI?
Keras starts at Free and Together AI at Free.
Does Keras or Together AI run on more platforms?
Keras runs on Python, Google Colab, Jupyter. Together AI runs on Api, Cloud.
Can I use Keras for free?
Both have a free tier, so you can try either at no cost before committing.
What is Keras best used for?
Keras is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Together AI is typically brought in for.
What can Keras do that Together AI cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.

Answered from the vendors’ own pages

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
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
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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