Amazon TensorFlow enables developers to quickly and easily get started with deep learning in the cloud.
N/A
Quickbase
Score 8.8 out of 10
N/A
Quickbase helps users tackle any project, no matter how complex. Quickbase helps customers see, connect and control complex projects. Whether it’s raising a skyscraper or coordinating vaccine rollouts, the no-code software platform allows business users to custom fit solutions to the way they work – using information from across the systems they already have.
$700
per month
Pricing
Amazon Tensor Flow
Quickbase
Editions & Modules
No answers on this topic
Enterprise
Full Customizable
per month/billed annually
Business
Starting at $2,200
per month
Team
Started at $700
per month
Offerings
Pricing Offerings
Amazon Tensor Flow
Quickbase
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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Quickbase offers three key plans, with feature distinction, simple and consistent entitlements, and a flexible licensing model, giving users the option of either user based or usage based licensing across all 3 plans.
A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.
Quickbase has proven that if it's in a spreadsheet, it really should be in Quickbase. One of my more recent achievements with the program was redesigning our current process for notifying management of something irregular. The old process had all the notification infrastructure built behind the scenes with only a handful of people knowing and understanding the process. The new process was able to clean up the behind-the-scenes and also provide an increased level of intuitiveness by allowing complete visibility and customization of notification recipients. This allows the initial notification to reach a select number of management individuals who can then decide whether or not it needs to be escalated, all while allowing the operations personnel to get back to work immediately. The biggest issue I've found with the program is the limitation of the user - I find that the biggest roadblock in my way is myself, as I am confident the program has the ability to accomplish what I need, but am I skilled enough to create it? This has helped me become very proficient in only one year of use and become more active in community engagement surrounding the program.
Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
Tensor Flow allows us to train our models much faster than in our on-premise equipment.
Most of the pre-trained models are easy to adapt to our clients' needs.
SageMaker isn't available in all regions. This is complicated for some clients overseas.
For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.
Pipelines - being able to share them or have permissions based viewing capabilities
Table size limits are small for really large apps. While I agree that there needs to be a cap on the size, it would be nice to adjust it up when needed.
We are growing our user base and getting more out of this system. Quick Base will continue to grow with our company as more of our users become super users and are capable of developing new features and solutions. We're getting more and more interested from departments that are hearing of our successes.
Quick Base has done everything we have asked it to do and then some. Our original goal was to have one system for CRM that encompassed both the sales process and the customer management. We have gone w-a-y beyond that with analytics, project management, system bug logging, and historical effort reporting.
I gave it a 10, but it should be a 9.99. They do a nice job of keeping the application running. The application can drag a bit when you have thousands of users accessing the application concurrently. We experienced this with large scale implementations.
Some of our tables that hold over a million records are starting to perform poorly, with some summaries taking over 20 seconds to load. This may be an indication that it is best to archive old data when reaching large volumes like this.
If you utilize the community, the support is amazing. Unfortunately, I find their actual support system a bit underwhelming. They don't seem to have a great process for interacting directly with an issue and often sweep significant issues under the rug by categorizing them as "Enhancement" ideas or legacy items.
Quick Base already is having a separate portal of providing training to customers and it is very easy to use and updates as per the new features added in to the application
I was not directly involved with the initial account implementation, only a bystander. For the app I directly implemented for my department only, I wish I had know to create an app diagram first. I don't remember if that was suggested. I think that would be a great help tip tool when a new app is created, to have a page with a check list of what is needed or how to get started. If you are a regular app builder, then you can bypass it or have the ability to turn it off in the app settings.
Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.
I picked Quickbase before investigating the low-code no-code market because I was so impressed. I did a pricing exercise a couple years ago because Quickbase was drastically increasing the cost and decreasing our allotments/features. We could have saved a lot of money by going with another comparable solution. However, those companies didn't have the longevity and all of the features and functions that Quickbase had.
While the purpose of this product is to provide flexibility and scalability to and organization, it is not as easy to roll out on the enterprise level. The support from Quick Base is not on a level that it makes the process easy and fluid. Be prepared to spend more time on this product than you might want to.
Quickbase has saved us so much time by reducing the clicks you need to get from one place to another. It takes the average colleague 5 or 6 clicks to get to another tool they need to do their job whereas it takes me 1 or 2 clicks to get to that same tool by using a personal Quickbase dashboard that has all the tools I need all on one page. This is a huge timesaver, especially when desk sharing where you have to get quickly to the info you want to share.
Quickbase has helped us streamline workflows and close gaps by having to ask for info only 1 time. Since users can't save a form until they've provided all the info you need on your end to complete a task, it saves a tremendous amount of time for everyone. No more replying to emails 2 or 3 times until you get the info you need.
