Software Alternatives & Startups

Keras VS Embeddable

Compare Keras VS Embeddable and see what are their differences

Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Rating
0 reviews
Pricing
Open source
Embeddable

The toolkit for building fast, interactive, fully-custom analytics experiences into your app.

Rating
0 reviews
Pricing
Paid
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Keras seems to be a lot more popular than Embeddable. While we know about 35 links to Keras, we've tracked only 2 mentions of Embeddable.

social mentions
35 vs 2
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
240+ vs 165

Base details

Website, pricing, platforms and company facts side by side.

Keras
Embeddable
Website keras.io embeddable.com
Pricing
Open source
Paid
Company Startup from the United Kingdom · 20 - 49 employees · 2023
Listed in

About Keras and Embeddable

In their own words, as submitted to SaaSHub.

Keras
Embeddable

No description of Keras yet.

Build Remarkable Analytics Experiences. No more 'Build vs. Buy'. Embeddable is the embedded analytics tool where you own the front-end code and we handle everything else. Now you can build fully-bespoke, fast-loading charts and dashboards in your app without the engineering costs. Delight your...

Read more about Embeddable

Features and specs

What each product offers, as listed by its team.

Keras 6 features
Embeddable 14 features
  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.
  • Cloud-Hosted Option
  • Self-Hosted Option
  • Frontend SDK
  • No-code Dashboard Builder
  • Performant Embedding
  • Row-Level Security
  • Configurable Cache
  • Compatible with Major Databases
  • Compatible with Charting Libraries
  • Template Charting Components Provided
    Included
  • Dedicated Account Management
  • Version Control
  • Audit Logs
  • Documentation

Analysis

An editorial look at what each product does well and who it suits.

Keras
Embeddable

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

No analysis of Embeddable yet.

Videos

Walkthroughs and reviews on video.

Keras 3 videos + Add
Embeddable 0 videos + Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos

  • - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

No Embeddable videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Keras
Embeddable
0% 0%
100% 100%
100% 100%
OCR
0% 0%
0% 0%
100% 100%

Questions & Answers

As answered by people managing Keras and Embeddable.

How would you describe the primary audience of your product?

Embeddable's answer:

Software companies who care about the UX and loading speed of their customer-facing analytics.

What makes your product unique?

Embeddable's answer:

Get the best of 'Build vs. Buy' in one stack-agnostic solution. Embeddable gives you full control over the frontend of your analytics experience, and handles the backend for you. No longer do you have to choose between a limited out-of-the-box solution, or building everything from scratch.

What's the story behind your product?

Embeddable's answer:

Embeddable is from the team behind Trevor.io -- a popular internal BI tool which also allows you to embed dashboards into your app. We realised embedding dashboards from a BI tool into your app wasn't the 'dream solution', and building analytics from scratch was super expensive... so we built Embeddable from the ground up to enable teams to deliver fully-bespoke, highly-performant analytics in their apps for their customers in 10% of the time.

Who are some of the biggest customers of your product?

Embeddable's answer:

  • Scalapay
  • Adthena
  • Irwin
  • EtonX
  • Resident Advisor
  • Facilities Solutions Group (FSG)
  • Multibrain
  • Raydiant
  • ThinkCERCA
  • Tixly
  • Softools
  • Faheem App
  • Just Move In
  • Any Creek

Why should a person choose your product over its competitors?

Embeddable's answer:

If you want full control over the UX of your customer-facing analytics experience, but don't want to invest months of developer time on building and maintaining a fully-custom build -- OR -- if you're using an embedded analytics too already that loads slowly and doesn't look and feel like the rest of your platform.

User comments

Share your experience with using Keras and Embeddable. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Keras no reviews yet
Embeddable no reviews yet
  • 6 Best Looker alternatives
    trevor.io · Jun 2024

    After a successful, oversubscribed Private Beta, Embeddable is now publicly available. More information on how to work with Embeddable can be found on their homepage at embeddable.com. Get in touch with the Embeddable...

  • Power BI Embedded vs Looker Embedded: Everything you need to know
    embeddable.com · Nov 2023

    The main differences between Power BI Embedded and Embeddable are performance, price, and customizability. Embeddable gives you full control over your charting components and data models. It’s also built from the...

  • Embedded analytics in B2B SaaS: A comparison
    medium.com · Nov 2023

    I’m happy to say that we’ve enrolled in the beta program of Embeddable. After learning all the above it seems like this is the option we’d want to invest in. We’ll keep you posted on how this pans out, but we’re...

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Keras 35 mentions
Embeddable 2 mentions

View more

  • AI in BI tools: why we're not there yet
    Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like “top customer” or “Q3 revenue” actually mean. Without this, the AI won't know... - Source: dev.to / over 1 year ago
  • Apache Superset
    It’s still pretty new but build by an experienced team. It’s commercial software though. https://embeddable.com/. - Source: Hacker News / over 2 years ago

Alternatives to Keras and Embeddable

When comparing Keras and Embeddable, you can also consider the following products.