TrustRadius

Obviously AI vs. Pytorch

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Obviously AI
Score 0.0 out of 10
N/A
Obviously AI offers their solution to allow users to create AI models without code, such as classification models (e.g. fraud detection, lead conversion, churn), regression models (predicting sales, dynamic pricing, etc.), or time series to predict the occurrence of future events of interest. Obviously AI offers self-service plans, and higher tier plans include support from a dedicated data scientist who joins the customer's team to produce models, with code files available on higher tier plans.
$300
per month 1 user & 12,000 predictions
Pytorch
Score 9.3 out of 10
N/A
Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A
Pricing
Obviously AIPytorch
Editions & Modules
Limited Access
$300
per month 1 user & 12,000 predictions
Full Access
$999
per month 5 user seats & 120,000 predictions
Data Scientist
$999
per month unlimited requests and revisions
Data Scientist with Code Files
$1999
per month unlimited requests and revisions
Data Scientist Team with Express Support
Custom Plan
No answers on this topic
Offerings
Pricing Offerings
Obviously AIPytorch
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Obviously AIPytorch
Considered Both Products
Obviously AI

No answer on this topic

Pytorch
Pierre Tassel | TrustRadius Reviewer
Pierre Tassel
Data Scientist Engineer/Researcher
Chose Pytorch
Tensorflow without Keras is not a pleasant experience; when using Keras, it is pretty nice, but it feels more opinionated than PyTorch; one is less free, which is not an issue in industrial settings with classic workflow but can be an issue in research settings. JAX is great …
Verified User
Anonymous
Chose Pytorch
Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly …
Verified User
Anonymous
Chose Pytorch
Pytorch is very, very simple compared to Tensorflow. Simple to install, less dependency issues, and very small learning curve. Tensorflow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a …
Verified User
Anonymous
Chose Pytorch
As I described in previous statements, Pytorch is much better suited than Tensorflow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks.

You can get to better performance models by better understanding the deep learning …
Verified User
Anonymous
Chose Pytorch
The syntax of PyTorch is much better in my opinion, and the programming style is more pythonic and easier to use. I also think PyTorch is a lot easier to debug than the competitors I've listed (caffe2 and tensorflow). I do like some of the examples given on tensorflows website, …
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User Ratings
Obviously AIPytorch
Likelihood to Recommend
-
(0 ratings)
9.0
(0 ratings)
Usability
-
(0 ratings)
10.0
(0 ratings)
User Testimonials
Obviously AIPytorch
Likelihood to Recommend
No answers on this topic
Everything deep learning related if not on TPU (in such case, JAX would be better suited). For LLM deployment, libraries such as vLLM would be better suited, too; otherwise, wrapping the PyTorch model with Ray is a good option.
Pierre Tassel | TrustRadius Reviewer
Pierre Tassel
Data Scientist Engineer/Researcher
Read full review
Pros
No answers on this topic
  • Provides Benchmark datasets to test your custom algorithm
  • Provides with a lot of pre-coded neural net components to use for your flow
  • Gives a framework to write really abstract code.
Taapas Agrawal | TrustRadius Reviewer
Taapas Agrawal
Research Intern
Read full review
Cons
No answers on this topic
  • It should have support for Java also as Java is one of the most popular language.
  • They should make things more easy if we want to use GPUs for computation.
  • They should keep adding the latest models so that we can easily load them for use for further fine-tuning.
Verified User
Anonymous
Read full review
Usability
No answers on this topic
The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
Pierre Tassel | TrustRadius Reviewer
Pierre Tassel
Data Scientist Engineer/Researcher
Read full review
Alternatives Considered
No answers on this topic
Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly less time to create valuable POCs as most of the things are inbuilt.
Verified User
Anonymous
Read full review
Return on Investment
No answers on this topic
  • Less time wasted on handling the library version issues
  • Small learning curve as very similar to Python
  • Compatibility with other popular Python libraries makes it easy to build a lot of things on it
Verified User
Anonymous
Read full review
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