Vertex AI on Google Cloud is an MLOps solution, used to build, deploy, and scale machine learning (ML) models with fully managed ML tools for any use case.
$0
Starting at
Saturn Cloud
Score 7.7 out of 10
N/A
Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment. Saturn Cloud is presented as flexible and straightforward for new data scientists while giving senior and experienced staff the
capabilities and configurability they need.…
$10
hourly $5 credit purchase to start
Pricing
Vertex AI
Saturn Cloud
Editions & Modules
Imagen model for image generation
$0.0001
Starting at
Text, chat, and code generation
$0.0001
per 1,000 characters
Text data upload, training, deployment, prediction
$0.05
per hour
Video data training and prediction
$0.462
per node hour
Image data training, deployment, and prediction
$1.375
per node hour
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Offerings
Pricing Offerings
Vertex AI
Saturn Cloud
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
Pricing is based on the Vertex AI tools and services, storage, compute, and Google Cloud resources used.
Vertex AI seems to be a lot more accurate with image editing versus other competitors (including free one). We do a lot of image creation, especially of dogs in very certain scenarios. We use Adobe Stock to get us started, but many times we need some very specific edits done to the image. We've found Vertex can produce those with a lot more precision than other AI image generators.
Saturn Cloud is a powerful data science platform that offers numerous benefits to organizations. It simplifies and streamlines the development, deployment, and scaling of data science and machine learning models. The platform addresses common business problems such as scalability, collaboration, efficiency, and cost-effectiveness. With Saturn Cloud, organizations can easily handle large datasets and complex computations, collaborate effectively among data science teams, automate repetitive tasks, optimize workflows, and utilize flexible and cost-efficient cloud resources. By leveraging Saturn Cloud, organizations can accelerate their data science projects, improve productivity, and achieve better outcomes in areas such as predictive modeling, recommendation systems, fraud detection, and more.
Vertex AI comes with support for LOTs of LLMs out of the box
MLOps tools are available that help to standardize operational aspects
Document AI is an out of the box feature that works just perfectly for our use cases of automating lots to tedious data extraction tasks from images as well as papers
While Saturn Cloud offers a range of pre-built templates and workflows, there is currently limited support for customization. For example, users may not be able to modify the pre-configured environments that come with the templates, or may find it difficult to integrate their own custom libraries and tools. Offering more flexibility in this area could help users tailor the platform to their specific needs and workflows.
While Saturn Cloud offers a variety of pre-built environments for data science and machine learning workloads, some users may prefer to use custom Docker images instead. However, the platform currently has limited support for Docker, which can be a limitation for users who need to work with specific dependencies or custom libraries. Adding more robust support for Docker could help to make the platform more versatile and adaptable to a wider range of use cases.
This is user friendly , better than its counterparts. Anyone familiar working with other cloud solutions for GPU will agree on this. Hence the rating of 10 was given to this. I personally love the fact that I get so much compute time for being a free user which is very efficient in terms of budget
It's not always instant, but understandable when it's under heavy load. It's not impressive nor disappointing, just what is expected. But when calling this platform through API's for it to do the actions requested there is minimal delay and wait time. It feels very responsive and quick when integrating it with a call center chat platform for example.
Vertex AI is much more accessible to non-developers than IBM's product. Moreover, Vertex AI integrates well with other Google products, enhancing its capabilities. A big plus is its integration with cloud storage, that allows for better management and access of data. In all honesty, it wasn't much of a difficult choice to choose Vertex AI.
Saturn Cloud provides an R server, that's super important. Even you can write R on CoLab with different settings, but it is inconvenient and slow. Saturn Cloud can give me a different IDE environment that I'm more used to, even if I'm using Python. Whereas CoLab is more dedicated to Jupyter notebook
Although we are still in the implementation phase with Saturn Cloud, we anticipate significant positive impacts on our business objectives.
The platform is expected to enhance our computational capabilities with its easy access to top-tier NVIDIA GPUs, which should accelerate our AI and machine learning projects. We believe this will lead to reduced development times and faster deployment of our generative AI models.
While Saturn Cloud provides excellent computational resources and reliable uptime, I find that their user interface could be improved. The UI can be unintuitive at times, making it a bit challenging to navigate and configure certain settings. Enhancing the user interface to be more streamlined and user-friendly would significantly improve the overall experience. Having pre-configured stacks readily available would also save time and make the platform even more efficient to use.
