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Domino's Enterprise AI Platform
Scale data science using Domino's centralized, modern MLOps platform to build, monitor, manage, and govern your end-to-end data science lifecycle. Loved by data scientists and trusted by IT, Domino unleashes data science to accelerate time to value, increase collaboration, mitigate risks, and reduce costs.
Reviews (29)
Shaarif G.
Loved the platform
Reviewed on Mar 17, 2025
Review provided by G2
What do you like best about the product?
Loved how it is helpful and flexible in connecting multiple cloud providers like AWS and AZURE.
What do you dislike about the product?
As a Indian customer, pricing is on higher side.
What problems is the product solving and how is that benefiting you?
Helped me to understand my dataset for ML model
Anush G.
Good to be True
Reviewed on Mar 17, 2025
Review provided by G2
What do you like best about the product?
Domino Enterprise AI Platform's interface is user friendly
What do you dislike about the product?
Color combination of user interface is not so attractive
What problems is the product solving and how is that benefiting you?
It solves my analyzing problem, and I used this analysis for my further research and take necessary decisions
Daniel Andres M.
Very pleasant experience using the platform
Reviewed on Mar 17, 2025
Review provided by G2
What do you like best about the product?
The ease of training models and data is exceptional.
What do you dislike about the product?
Perhaps needs a bit more guidance for beginners
What problems is the product solving and how is that benefiting you?
The training of models is so much easier and allows everything to happen within one platform
Shivesh R.
It was a pleasure experience to use Domino as a non-code AI platform for some of my automated jobs.
Reviewed on Mar 15, 2025
Review provided by G2
What do you like best about the product?
It's easy to deploy systems. Compatibility with cloud platforms. Handling data securely and managing data. It's easy to integrate with AWS and other cloud environments.
What do you dislike about the product?
Not having an easy-to-code IDE like Gradio present and not being able to do CV video tasks. As I frequently use this platform, I need to handle all sorts of data.
What problems is the product solving and how is that benefiting you?
It's easy to implement and analyze data on this platform and also easy to deploy its model.
Swapna D.
My thoughts on working with Domino Enterprise AI Platform
Reviewed on Mar 15, 2025
Review provided by G2
What do you like best about the product?
Honestly, what I love the most is how Domino takes the friction out of the AI lifecycle. Before, we spent so much time just getting environments set up, chasing down dependencies, and trying to replicate results. It was a mess. Domino’s unified platform just eliminates so much of that overhead. I can’t stress that enough. When I hand off a project, I know the next person can pick it up and run it exactly as I did. And also we can use our existing cloud or non-prem resources, which saves us money and avoid vendor lock-in. We share code, data, and results are quite easy. The version control for models and experiments is a lifesaver for our team. Genuinely Domino’s helps us operationalise AI. It’s not about building models, it’s about getting them into production and driving real business value.
What do you dislike about the product?
Need some easier setup, faster plugin access.
What problems is the product solving and how is that benefiting you?
Domino is fundamentally solving the fragmentation and inconsistency. Before using it, we are constantly battling siloed data, disparate tools, and inconsistent environments. This led to massive inefficiencies, slowed down our development cycles, and made it incredibly difficult to scale our models. First and foremost, this platform eliminated the need for data scientists to constantly switch between different tools and environments. Secondly, it also solved the 'It Works On My Machine' problem. Because here we can easily replicate experiments and ensure that our models are consistent across different environments. And finally, Domino’s Expertise AI Platform facilitates collaboration between data scientists, engineers, and other stakeholders. So that we can easily share code, data, and results.
AHMEDSAHRAOUI
Accelerated machine learning model development with seamless deployment
Reviewed on Nov 11, 2024
Review provided by PeerSpot
What is our primary use case?
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they can merge their changes with the main project.
How has it helped my organization?
Domino made developments easier, which indicates a good investment as it reduced development time significantly.
What is most valuable?
The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino. Additionally, deploying a model using Domino was quick. Domino's API also speeds up the deployment process.
What needs improvement?
The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that cannot handle big deployments, which is not suitable for LLMs.
For how long have I used the solution?
I have been working with Domino Data Science Platform for four years.
What do I think about the stability of the solution?
Domino is quite stable. I would rate it nine out of ten for stability.
What do I think about the scalability of the solution?
Since we didn't work with big data, stability and scalability were not major concerns. Domino is based on Kubernetes, allowing easy configuration of the number of workers or machines needed, with no issues in scaling.
How are customer service and support?
The technical support team was very helpful. They sometimes visited us in person. However, creating new features could take some time because they had to consult other customers.
What about the implementation team?
A team of six or seven people were needed for maintenance on Domino.
What was our ROI?
Domino delivered return on investment by facilitating easier development work, even though I don't have specific numbers.
What's my experience with pricing, setup cost, and licensing?
There is a licensing fee per user and additional costs for customer support. I don't know the exact pricing, but it's likely above average.
What other advice do I have?
It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying solely on data scientists might not be sufficient.
I'd rate the solution eight out of ten.
Akshay H.
Empowering Collaboration and Efficiency in Data Science Workflows
Reviewed on Jan 03, 2024
Review provided by G2
What do you like best about the product?
Collaborative Environment
Reproducibility
Integration with Tools
Scalability
Automation and Workflow Orchestration
Security and Compliance Features
Model Deployment and Monitoring
User-Friendly Interface
Customer Support
Ease of Implementation
Frequency of use
Reproducibility
Integration with Tools
Scalability
Automation and Workflow Orchestration
Security and Compliance Features
Model Deployment and Monitoring
User-Friendly Interface
Customer Support
Ease of Implementation
Frequency of use
What do you dislike about the product?
Learning Curve
Cost: Some users may express concerns about the cost
Customization Challenges: Depending on specific use cases, users might face challenges in customizing certain aspects of the platform to align with their unique requirements
Cost: Some users may express concerns about the cost
Customization Challenges: Depending on specific use cases, users might face challenges in customizing certain aspects of the platform to align with their unique requirements
What problems is the product solving and how is that benefiting you?
Increased Collaboration: Centralized collaboration features enhance communication and teamwork among data science teams.
Improved Reproducibility: Version control and experiment tracking contribute to the reproducibility of machine learning experiments.
Enhanced Scalability: The ability to scale resources and handle larger datasets supports the growth of machine learning projects.
Efficient Deployment and Monitoring: Streamlined model deployment and effective monitoring contribute to the successful integration of machine learning models into production.
Flexibility and Integration: Integration with diverse tools allows data scientists to work with familiar frameworks and libraries.
Improved Reproducibility: Version control and experiment tracking contribute to the reproducibility of machine learning experiments.
Enhanced Scalability: The ability to scale resources and handle larger datasets supports the growth of machine learning projects.
Efficient Deployment and Monitoring: Streamlined model deployment and effective monitoring contribute to the successful integration of machine learning models into production.
Flexibility and Integration: Integration with diverse tools allows data scientists to work with familiar frameworks and libraries.
Computer Software
The experience was easy and really good
Reviewed on Dec 21, 2023
Review provided by G2
What do you like best about the product?
Simple user interface and great customer support
What do you dislike about the product?
Some of the operations are difficult to manage
What problems is the product solving and how is that benefiting you?
We were creating some gpt's for our product
Computer Software
Perfect platform for AI, ML and data science
Reviewed on Dec 18, 2023
Review provided by G2
What do you like best about the product?
They provide the best AI, ML, data science solutions for various applications. I personally used their tools integrated in MathWorks and Anaconda. The users are now able to access various Python ML packages and R packages in Anaconda. Data science became easier with those R packages. In MATLAB, this platform helps a lot to run simulations easily. Moreover, all the packages are extremely fast and seamless.
What do you dislike about the product?
I feel the prices are high which can be reduced. The integration of Domino with MathWorks or Anaconda can be made easier with less customization effort. The tools can be made handy for a layman.
What problems is the product solving and how is that benefiting you?
I mainly integrated Domino with Anaconda and MATLAB. It provides a lot of ML/Data science/R packages which is very helpful. In the case of MATLAB, it helps a lot with seamless simulation. The main use case is that it comes with a bundle of all previously implemented libraries.
Consulting
Best use for machine learning
Reviewed on Dec 17, 2023
Review provided by G2
What do you like best about the product?
It makes things easy to train the AI with machine learning, which was complicated to train.
What do you dislike about the product?
Minor issues when I started to use this platform earlier.
What problems is the product solving and how is that benefiting you?
I was using another platform to train and deploy the AI, the quality of data was not up to the mark which required rework. The MLOps platform performs so well for deployment.