The Dataiku platform unifies data work from analytics to Generative AI. It supports enterprise analytics with visual, cloud-based tooling for data preparation, visualization, and workflow automation.
N/A
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
Pricing
Dataiku
Gavagai
Editions & Modules
Discover
Contact sales team
Business
Contact sales team
Enterprise
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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
Offerings
Pricing Offerings
Dataiku
Gavagai
Free Trial
Yes
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
Additional Details
—
Buy extra credits at any time
Bought credits never expire
More Pricing Information
Community Pulse
Dataiku
Gavagai
Features
Dataiku
Gavagai
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Dataiku
8.6
5 Ratings
3% above category average
Gavagai
-
Ratings
Connect to Multiple Data Sources
8.05 Ratings
00 Ratings
Extend Existing Data Sources
10.04 Ratings
00 Ratings
Automatic Data Format Detection
10.05 Ratings
00 Ratings
MDM Integration
6.52 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Dataiku
10.0
5 Ratings
18% above category average
Gavagai
-
Ratings
Visualization
10.05 Ratings
00 Ratings
Interactive Data Analysis
10.05 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Dataiku
9.5
5 Ratings
16% above category average
Gavagai
-
Ratings
Interactive Data Cleaning and Enrichment
9.05 Ratings
00 Ratings
Data Transformations
9.05 Ratings
00 Ratings
Data Encryption
10.04 Ratings
00 Ratings
Built-in Processors
10.04 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Dataiku
8.5
5 Ratings
1% above category average
Gavagai
-
Ratings
Multiple Model Development Languages and Tools
8.05 Ratings
00 Ratings
Automated Machine Learning
8.05 Ratings
00 Ratings
Single platform for multiple model development
8.05 Ratings
00 Ratings
Self-Service Model Delivery
10.04 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Dataiku is an awesome tool for data scientists. It really makes our lives easier. It is also really good for non technical users to see and follow along with the process. I do think that people can fall into the trap of using it without any knowledge at all because so much is automated, but I dont think that is the fault of Dataiku.
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.
The integrated windows of frontend and backend in web applications make it cumbersome for the developer.
When dealing with multiple data flows, it becomes really confusing, though they have introduced a feature (Zones) to cater to this issue.
Bundling, exporting, and importing projects sometimes create issues related to code environment. If the code environment is not available, at least the schema of the flow we should be able to import should be.
The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level
The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.
Anaconda is mainly used by professional data scientists who have profound knowledge of Python coding, mainly used for building some new algorithm block or some optimization, then the module will be integrated into the Dataiku pipeline/workflow. While Dataiku can be used by even other kinds of users.
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.