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Fireworks AI vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Fireworks AI
Ranking in AI Development Platforms
16th
Average Rating
10.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Google Vertex AI
Ranking in AI Development Platforms
3rd
Average Rating
8.4
Number of Reviews
10
Ranking in other categories
AI Infrastructure (1st)
 

Featured Reviews

reviewer2588646 - PeerSpot reviewer
Nov 6, 2024
Enhanced text-to-image creation with solid API and fine-tuning support
We primarily use Fireworks AI for text-to-image generation. We are developing a platform for artists to sell their art styles, where the system helps them tune a model and then sell images generated from their signature Fireworks AI has helped our organization by enabling us to create a platform…
Serge Dahdouh - PeerSpot reviewer
Aug 16, 2023
A user-friendly platform that automatizes machine learning techniques with minimal effort
I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information We work with clients who request the implementation of a certain document into a chatbot. Because of the…

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Fireworks AI has a solid API and is quite easy to interact with."
"It provides the most valuable external analytics."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"The integration of AutoML features streamlines our machine-learning workflows."
 

Cons

"When using the API, it does not return information about the charges for image generation, which would be useful for our solution."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"I think the technical documentation is not readily available in the tool."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"The tool's documentation is not good. It is hard."
"I'm not sure if I have suggestions for improvement."
 

Pricing and Cost Advice

Information not available
"The solution's pricing is moderate."
"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."
"The price structure is very clear"
"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
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Top Industries

By visitors reading reviews
Computer Software Company
17%
University
17%
Financial Services Firm
6%
Comms Service Provider
6%
Financial Services Firm
13%
Computer Software Company
13%
Manufacturing Company
9%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

Ask a question
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What do you like most about Google Vertex AI?
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...
What is your experience regarding pricing and costs for Google Vertex AI?
Google Vertex AI is relatively expensive, rating it six for pricing. The pricing model could be more flexible.
What needs improvement with Google Vertex AI?
Both major systems, Azure and Google, are not yet stabilized, especially their customer support. It is essential to have a robust customer support system to get quick resolutions as they're frequen...
 

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Overview

Find out what your peers are saying about Microsoft, Google, Amazon Web Services (AWS) and others in AI Development Platforms. Updated: November 2024.
815,854 professionals have used our research since 2012.