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Machine Learning · head to head

Keras vs LatchBio

Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Free plan
Yes
Full reviewWebsite
LatchBio logo

LatchBio

AI

AI agents that run bioinformatics analyses, from a company that pivoted from workflow hosting

From
On request
Free plan
No
Full reviewWebsite

The short version

  • Only Keras has a free tier, so it costs nothing to try first.
  • Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; LatchBio the company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.
  • They diverge on capability: Keras covers Sequential and Functional API, LatchBio covers Agent driven analysis.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Keras and LatchBio actually diverge.

Attributes where Keras and LatchBio differ
AttributeKerasLatchBio
Starting priceFreeOn request
Pricing modelopen-sourcequote
Free tierYesNo
PlatformsPython, Google Colab, JupyterWeb
CategoryMachine LearningAI
Founded2015Unknown

Identical on both: 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 LatchBio

  • Agent driven analysis
  • Multi assay support
  • Published benchmarks
  • White labelled portals
  • Common file format handling
  • Managed compute

What people use each for

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

Keras

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

LatchBio

  • A small biotech with sequencing data and no bioinformatician who needs a first pass differential expression or cell type annotation quicklynot Keras
  • An assay or kit vendor that wants to give customers an analysis portal under its own branding rather than shipping raw filesnot Keras
  • A wet lab scientist who wants to explore a dataset interactively before commissioning a formal analysis from a core facilitynot Keras
  • A research group benchmarking agent produced analyses against its own established pipeline to decide how much routine work can be delegatednot 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

LatchBio

  • The company has pivoted from hosted bioinformatics workflows to AI agents, so organisations that adopted the earlier SDK and workflow product are now on a line that is no longer the strategic focus.
  • Agent generated analysis code has to be validated by someone who could have written it, which inverts the value proposition for the small labs that most need it and cannot check the output.
  • No pricing is published, so the cost of routine agent analysis at team scale cannot be estimated without contacting sales, and compute billed through the platform makes the bill usage dependent.
  • Regulated use is a poor fit: an agent that writes non deterministic analysis code is difficult to validate under GxP, so this is a research tool rather than something that will sit in a regulated diagnostic or submission workflow.
  • The vendor publishes its own benchmarks, which is more than most competitors do, but self reported benchmarks on selected datasets are not a substitute for testing the agent on your own data before committing.

Pricing, plan by plan

Keras

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

LatchBio

On request
  • LatchBio Platform$undefined/year
    • Quoted through a sales process
    • Interactive sandbox available for trying the agent before contracting
    • Enterprise and white labelled portal deployments priced separately

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 LatchBio if

  • You need agent driven analysis.
  • You also want multi assay support.
LatchBio review

Questions people ask

Is Keras or LatchBio better?
Neither clearly leads. Keras starts at Free and LatchBio at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Keras or LatchBio?
Keras has a free tier; the other does not. Paid plans start at Free for Keras and On request for LatchBio.
Does Keras or LatchBio run on more platforms?
Keras runs on Python, Google Colab, Jupyter. LatchBio runs on Web.
Can I use Keras for free?
Yes. Keras has a free tier, so you can try it without paying. LatchBio starts at On request.
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 LatchBio is typically brought in for.
What can Keras do that LatchBio cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. LatchBio covers Agent driven analysis, Multi assay support, Published benchmarks, White labelled portals.

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
LatchBio: Is LatchBio still selling the original workflow platform?

The company is now led by agent.bio and its agent products. Buyers evaluating the older Python SDK workflow platform should confirm current support directly.

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
LatchBio: Can I try it before buying?

Yes. There is an interactive sandbox where you can upload data and run the agent before entering a sales conversation.

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
LatchBio: Is it suitable for regulated work?

Not really. Non deterministic agent generated code is hard to validate under GxP, so treat it as a research and exploration tool.

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
LatchBio: What data types does it handle?

Single cell RNA sequencing, spatial transcriptomics, epigenomics, proteomics and pharmacology data, ingesting FastQ, H5AD and spreadsheet formats.

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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