Fireworks AI vs Google Vertex AI comparison

Fireworks AI and Google are both solutions in the AI Development Platforms category. Fireworks AI is ranked #11 with an average rating of 10.0, while Google is ranked #2 with an average rating of 8.4. Fireworks AI holds a 6.4% mindshare in AIDP, compared to Google’s 11.1% mindshare. Additionally, 100% of Fireworks AI users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Fireworks AI

Read 1 Fireworks AI review

1,721 Views
1,721 Comparison Views

100% willing to recommend

Google Vertex AI

Read 12 Google Vertex AI reviews

8,787 Views
7,908 Comparison Views

100% willing to recommend

Fireworks AI

Google Vertex AI

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Executive Summary

We performed a comparison between Fireworks AI and Google Vertex AI based on real PeerSpot user reviews.

Find out what your peers are saying about Google, Microsoft, Hugging Face and others in AI Development Platforms.

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AI Development Platforms

July 2025

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Review summaries and opinions

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

Categories and Ranking

Fireworks AI

Ranking in AI Development Platforms

11th

Average Rating

10.0

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Vertex AI

Ranking in AI Development Platforms

2nd

Average Rating

8.4

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

AI Infrastructure (1st)

Mindshare comparison

As of August 2025, in the AI Development Platforms category, the mindshare of Fireworks AI is 6.4%, up from 0.8% compared to the previous year. The mindshare of Google Vertex AI is 11.1%, down from 20.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

reviewer2588646

Senior Software Development Engineer at a tech services company with 1-10 employees

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…

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Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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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."

"Vertex comes with inbuilt integration with GCP for data storage."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"The integration of AutoML features streamlines our machine-learning workflows."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"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."

"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 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."

"The support is perfect and fantastic."

More Google Vertex AI pros

Cons

"When using the API, it does not return information about the charges for image generation, which would be useful for our solution."

"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."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"The tool's documentation is not good. It is hard."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"I'm not sure if I have suggestions for improvement."

"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."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

More Google Vertex AI cons

Pricing and Cost Advice

Information not available

"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."

"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."

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Top Industries

By visitors reading reviews

University

15%

Computer Software Company

14%

Financial Services Firm

Comms Service Provider

Computer Software Company

13%

Financial Services Firm

11%

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What is your experience regarding pricing and costs for Fireworks AI?

I cannot comment on pricing or setup cost since others handle that aspect. As a developer, I primarily use the API.

See all answers

What needs improvement with Fireworks AI?

Returning the values charged for each event generation would improve Fireworks AI. When using the API, it does not return information about the charges for image generation, which would be useful f...

See all answers

What is your primary use case for Fireworks AI?

See all answers

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...

See all answers

What is your experience regarding pricing and costs for Google Vertex AI?

They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.

See all answers

What needs improvement with Google Vertex AI?

Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...

See all answers

Comparisons

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Compared 13% of the time

Hugging Face vs Fireworks AI

Compared 11% of the time

Google Cloud AI Platform vs Fireworks AI

Compared 1% of the time

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Azure OpenAI vs Google Vertex AI

Compared 33% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 15% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

IBM Watson Machine Learning vs Google Vertex AI

Compared 1% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

AI Development Platforms

July 2025

Download Fireworks AI product report

Buyer's Guide

Google Vertex AI

July 2025

Download Google Vertex AI product report

Overview

Fireworks AI uses advanced technologies to streamline operations and enhance user experience, catering to industry-specific requirements and driving innovation.

Fireworks AI integrates cutting-edge tools for data processing, offering seamless automation in managing complex workflows. It addresses industry needs through scalable solutions adaptable to personalized requirements. Fireworks AI ensures optimized performance, enhancing decision-making efficiency across businesses.

What are the crucial features of Fireworks AI?

Automated Workflow Management: Streamlines complex processes efficiently.
Data Analytics: Provides insightful analytics for informed strategy development.
Scalability: Offers flexible scaling to match business growth trajectories.
Industry Integration: Solutions tailored to the unique demands of different industries.

Which benefits and ROI should users evaluate?

Increased Efficiency: Significant reduction in workload through automation.
Cost Savings: Optimizes resources, leading to reduced operational costs.
Enhanced Decision-Making: Supports data-driven decisions for better outcomes.
Scalability: Facilitates growth without proportional cost increases.

Industries such as healthcare and finance benefit from Fireworks AI by streamlining data management, improving client interaction, and supporting compliance through automated document handling. Each deployment adjusts to specific sector demands, ensuring relevant application across diverse business environments.

Fireworks AI

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Buyer's Guide

AI Development Platforms

July 2025

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