Anaconda Business and Amazon SageMaker compete in the data science and machine learning platform category. Anaconda Business has the upper hand in cost-effectiveness and user-friendly nature, appealing to those looking for pricing and functionality, while SageMaker leads with its advanced AWS integration and scalable model deployment features.
Features: Anaconda Business offers a comprehensive data science platform with integration of numerous libraries and tools like Jupyter Notebook, providing easy access for both beginners and experts. It offers strong community support and seamless integration with Python and R, which are highly appreciated by users. Amazon SageMaker, on the other hand, provides seamless integration with AWS services, offering advanced machine learning capabilities and a wide range of options from data preparation to model deployment. Its auto-scaling and simplified model deployment are key advantages.
Room for Improvement: Anaconda Business could improve by enhancing its open-source OS compatibility and providing clearer documentation. Users seek better interface design and improved stability for handling heavy workloads. Amazon SageMaker requires more comprehensive documentation and clearer pricing models. Users desire better scaling capabilities, increased security measures, and more streamlined cost management options.
Ease of Deployment and Customer Service: Anaconda Business is well-suited for diverse environments, including on-premises and hybrid clouds. It benefits from strong community support but faces occasional issues with documentation clarity. SageMaker is optimized for public cloud deployment, integrating efficiently with AWS services. Both products face mixed reviews regarding technical support, with users often resolving issues independently or via community resources.
Pricing and ROI: Anaconda Business's open-source model is cost-saving, providing valuable returns through time and resource efficiency. In contrast, Amazon SageMaker's pricing is based on usage, which may lead to higher costs. Users appreciate its robust feature set but find its complex pricing structure challenging, necessitating careful management to maximize ROI. Anaconda Business's straightforward costs appeal to organizations aiming for cost efficiency.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
Everyone being able to work smoothly without unnecessary delays.
I have seen a return on investment; specifically, when we talk about efficiency, it's both time-saving and money-saving.
I have seen a return on investment with time saved by 50% and less downtime, allowing the team to deliver projects faster with fewer errors.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
The response time is generally swift, usually within seven to eight hours.
Anaconda Business customer support is very active with a quick response time.
Overall, support was reliable when we needed it, just not super-fast every single time.
The customer support for Anaconda Business provides a better approach.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
As more environments or users get added, it still runs smoothly without major slowdowns.
Anaconda Business scales very well because it is built around centralized environment and package management.
Anaconda does not have scalability restrictions as it depends on the type of machine running it.
There are issues, but they are easily detectable and fixable, with smooth error handling.
The product has been stable and scalable.
I rate the stability of Amazon SageMaker between seven and eight.
Earlier, setting up or troubleshooting conflicts could take anywhere from thirty minutes to an hour, but now most setups just work.
Anaconda Business is stable to an extent, but it sometimes crashes on systems with insufficient RAM.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
It would also be nice to have clearer error messages when something fails, so it is easier to understand what went wrong without digging too much.
They should enhance the security point of view; it's good, but it needs some more advanced features.
The pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult.
The cost for small to medium instances is not very high.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
Anaconda is an open-source tool, so I do not pay anything for it.
My experience with pricing, setup cost, and licensing is that it is a little costly, but it is useful.
My experience with pricing, setup cost, and licensing indicates that it is a bit costly, but it is useful.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
They offer insights into everyone making calls in my organization.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
Anaconda Business has positively impacted my organization because, when discussing the security point of view, it's exceptional; when comparing it to other solutions, Anaconda Business is superior.
We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful.
Anaconda Business positively impacts our organization by protecting us from compliance and security risks while keeping the environment consistent, allowing our team to focus on insight and innovation instead of worrying about setups, security, and software issues.
| Product | Market Share (%) |
|---|---|
| Amazon SageMaker | 4.6% |
| Anaconda Business | 2.6% |
| Other | 92.8% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 17 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 2 |
| Large Enterprise | 19 |
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.
Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.
What are the key features of Anaconda Business?Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.
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