

Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Cohere's Embed English model took less time to embed than OpenAI's embedding ada-002 model.
Cohere helped us with all three aspects: money is saved, time is saved, and we needed fewer resources to meet our end goals.
On average, we're saving about 10 to 15 hours per project.
They answer all my questions and share guidance on using DataRobot scripts if certain functionalities are not available in the UI.
Being cloud-hosted enables automatic resource scaling, which supports collaboration across teams.
We don't observe many scaling problems because it's an enterprise application.
We haven't had any issues to escalate to Cohere's support because reranking is an optional feature in our product, and we haven't seen any significant issues so far.
We want such features because when chatting with clients, we can demonstrate that employing Cohere's reranking model significantly improves results compared to not using it.
Because it does not have extensive understanding of Oracle functionalities in ERP, it sometimes gives wrong results or the confidence score is lower than desired.
During the embedding process, measurable metrics are not visible.
DataRobot is a UI-based tool, which means it cannot provide all the features I might manually implement through notebooks or Python.
There is a lack of transparency in the models; sometimes it feels like a black box.
My experience with pricing, setup cost, and licensing is that it is expensive to use all Oracle services.
Cohere's pricing, setup cost, and licensing are better.
The prices are competitive compared to competitors.
The setup cost was minimal because it's cloud-hosted, eliminating the need for heavy on-premises infrastructure, allowing us to start using it immediately after purchase.
This makes it very easy to find and use the catalog to determine whether existing functionality is already implemented, preventing redundant implementations.
Cohere has positively impacted my organization by helping our customers work more efficiently when creating requests, and the embedding results are of very high quality.
I noticed a 10% improvement in my log system after using Cohere.
By automating highly technical aspects like model comparison, DataRobot enhances productivity and reduces project timelines from three months to less than one month.
DataRobot has positively impacted our organization in many ways. First, it has improved efficiency; tasks such as model testing, feature engineering, and predictions that used to take us days or weeks can now be accomplished in hours.
| Product | Market Share (%) |
|---|---|
| Cohere | 1.3% |
| DataRobot | 1.8% |
| Other | 96.9% |

| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
Cohere provides a robust language AI platform designed for efficient implementation in various domains, offering advanced features for automation and data analysis.
Cohere delivers a scalable AI language model that facilitates automation in data-driven environments. Highly adaptable to industry-specific requirements, it supports tasks such as text generation, summarization, and anomaly detection. This flexibility, along with its integration capabilities, makes it valuable for tech-savvy users seeking seamless AI solutions.
What are the notable features of Cohere?
What benefits should users consider in reviews?
Cohere sees significant use in finance, healthcare, and marketing, enabling precise data analysis and strategic insights. In finance, it assists with detecting market trends, while in healthcare, it supports clinical documentation and research analysis. Marketing uses include content creation and consumer sentiment analysis.
DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users to build and deploy highly accurate machine learning models in a fraction of the time.
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