Domino Data Science Platform and MathWorks Matlab are competitors in the data analysis and scientific computing category. Domino seems to have an upper hand in flexibility and collaboration, while Matlab is preferred for complex computations due to its strong algorithms and comprehensive toolset.
Features: Domino emphasizes collaboration, reproducibility, and scalability with its cloud-based ecosystem, supports multiple languages, and provides robust model deployment. MathWorks Matlab offers strong algorithm development, extensive simulation capabilities, and superior data visualization, with toolboxes for specialized scientific and engineering applications.
Ease of Deployment and Customer Service: Domino offers a modern cloud-based deployment model that simplifies scalability and collaboration and provides effective customer support for data science teams. MathWorks Matlab features strong in-house deployment support and extensive training resources, though may require more effort in scaling operations.
Pricing and ROI: Domino presents flexible pricing aligned with various organizational needs, demonstrating high ROI through enhanced productivity and reduced overhead. MathWorks Matlab has a traditionally higher setup cost, yet offers significant ROI in precision engineering and complex simulations, where its capabilities can justify the expenditure despite the higher initial investment.
| Product | Market Share (%) |
|---|---|
| MathWorks Matlab | 1.6% |
| Domino Data Science Platform | 2.3% |
| Other | 96.1% |
| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 4 |
Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.
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