Gavagai vs. TensorFlow

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Gavagai
Score 10.0 out of 10
Enterprise companies (1,001+ employees)
Gavagai Explorer is a text analysis tool for companies that want to keep track of what their customers think – regardless of which language they speak. Explorer analyzes texts in 47 languages. The texts get automatically analyzed and the results are presented in interactive and share-able Dashboards. Gavagai understands meaning The majority of the text data it analyzes comes from sources such as surveys, reviews, emails, chat conversations, and social…
$3,000
Time used to Set Up
TensorFlow
Score 9.0 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
Pricing
GavagaiTensorFlow
Editions & Modules
Small - 3 project slots -1200 credits
€ 120 per month - More or extra credits can be purchased
Number of Texts Analyzing, number of seats, number of projects
Medium - 10 project slots - 1200 credits
€ 400 per month - More or extra credits can be purchased
Number of Texts Analyzing, number of seats, number of projects
Large - 50 project slots - 1200 credits
€ 2,000 per month - More or extra credits can be purchased
Number of Texts Analyzing, number of seats, number of projects
The Entire Web Application
$3000.00
Time used to Set Up
Enterprise
quote: https://www.gavagai.io/request-quote/
Number of Texts Analyzing, number of seats, number of projects
No answers on this topic
Offerings
Pricing Offerings
GavagaiTensorFlow
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeOptionalNo setup fee
Additional DetailsBuy extra credits at any time Bought credits never expire
More Pricing Information
Best Alternatives
GavagaiTensorFlow
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Medium-sized Companies
Posit
Posit
Score 9.1 out of 10
Posit
Posit
Score 9.1 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
GavagaiTensorFlow
Likelihood to Recommend
10.0
(1 ratings)
8.5
(14 ratings)
Usability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
-
(0 ratings)
9.1
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
GavagaiTensorFlow
Likelihood to Recommend
Gavagai AB
Gavagai is well suited for a B2C business that receives a lot of customer feedback in a form of open-ended text. It makes life easier for the customer experience team to efficiently identify the strengths and areas of improvement for the business. It saves a lot of time and also the hassle of analysing text data manually. It is not just a word cloud tool that shows you the words with the most number of mentions. Gavagai directs you towards actionability.
Read full review
Open Source
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Read full review
Pros
Gavagai AB
  • Simplifies text data.
  • Lets you customise your dashboard.
  • Sentiment analysis
  • Supports 47+ languages
Read full review
Open Source
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
Read full review
Cons
Gavagai AB
  • Auto generated filters based on the data uploaded.
  • Adding more Indian regional languages.
  • Competitive pricing
Read full review
Open Source
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
Read full review
Usability
Gavagai AB
No answers on this topic
Open Source
Support of multiple components and ease of development.
Read full review
Support Rating
Gavagai AB
No answers on this topic
Open Source
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
Read full review
Implementation Rating
Gavagai AB
No answers on this topic
Open Source
Use of cloud for better execution power is recommended.
Read full review
Alternatives Considered
Gavagai AB
I didn't evaluate many options while choosing Gavagai, I had explored a few local vendors whose capabilities were either incomplete or were not up to the mark. Their customer support was also quite poor. Also, the tool was debugged enough which led to frequent crashing. Alchmer although is not a direct competitor to Gavagai, since it's more of a customer feedback tool with additional capabilities of text analytics. I found Alchemer to be extremely expensive. Zonka on the other hand was quite welcoming to feedback from me and promised to develop additional capabilities for my specific requirements although the plan didn't go through due to internal reasons.
Read full review
Open Source
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
Read full review
Return on Investment
Gavagai AB
  • Helps save man hours.
  • Reduces the effort required to analyse big data.
  • Helps to act upon areas of improvement faster.
  • Indicates the strengths or things that are particularly liked by your customers.
Read full review
Open Source
  • Learning is s bit difficult takes lot of time.
  • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
  • Once you have learned this, it make your job very easy of getting the good result.
Read full review
ScreenShots

Gavagai Screenshots

Screenshot of Using the Gavagai Explorer produces Topics from the text data. The Topics give you a granular understanding of the text data through understanding the Prevalence, Sentiment, and Associations.Screenshot of The language technology the Gavagai Explorer is built on.Screenshot of Customer Experience unstructured Text data analytics