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Fireworks AI vs Gemini Enterprise Agent Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 23, 2026

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
9th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
9
Ranking in other categories
AI Software Development (19th), AI Finance & Accounting (5th), AI Research (5th)
Gemini Enterprise Agent Pla...
Ranking in AI Development Platforms
1st
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI Agent Builders (5th)
 

Mindshare comparison

As of July 2026, in the AI Development Platforms category, the mindshare of Fireworks AI is 2.7%, down from 6.5% compared to the previous year. The mindshare of Gemini Enterprise Agent Platform is 8.1%, down from 11.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Gemini Enterprise Agent Platform8.1%
Fireworks AI2.7%
Other89.2%
AI Development Platforms
 

Featured Reviews

M김
Ai스페셜리스트매니저 at a tech vendor with 501-1,000 employees
Automation has accelerated agent workflows and now needs broader connections for enterprise data
In the current function calling, if Fireworks AI could be added as part of our RAG system not only with the function calling we are using now but also with a variety of other connections, then an even better situation would be possible. Fireworks is based on tool calling, so it needs to add more different kinds of connections to enable faster data retention and optimization. Although multiple optimal optimization or measurement methodologies for using LLMs are being discussed, when using them inside enterprises, the main thing is actually measuring work handling capability or work processing speed. Based on that, and also through what might be called interviews with business-side staff, we measured the speed improvements in a somewhat indirect manner.
Pethuru Chelliah - PeerSpot reviewer
Chief Architect at a energy/utilities company with 10,001+ employees
Developed and deployed AI agents through a unified platform that supports integration with enterprise systems
We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and other optimizations to enhance the performance of the AI models. We are not fully leveraging the automation being provided by Google Vertex AI for building AI models. Google Vertex AI has to be enhanced to support agentic AI system development. AI agents have to be developed through Google Vertex AI. Additionally, RAG support, vector database support, knowledge graph support, integration with enterprise systems such as ERP, CRM, PLM, MES, and other enterprise systems need improvement. Automated workflow generation and automation could also be enhanced. At this point, we are completely satisfied with the features and functionalities of Google Vertex AI platform, but as we move towards autonomous systems through agentic AI paradigm, Google Vertex AI platform needs to be improved to facilitate agentic AI system design, development, deployment, monitoring, observability, governance, and security.

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 helped our organization by enabling us to create a platform for artists to sell their art styles."
"After introducing Fireworks AI's high-speed inference engine, I found that communication speed between agents was about twice as fast as before."
"Fireworks AI has positively impacted our organization by increasing our AI response time by twenty to fifty percent, as we now have AI agents and AI features that return answers twenty to fifty percent faster."
"Fireworks AI has impacted us positively as it helps in offering us access to the open-source models by advancing fine-tuning options, a massive library where we can get information from the database that we can use in line with our company policy."
"Fireworks AI has positively impacted our organization by making our AI features feel more production-ready instead of experimental."
"Fireworks AI has a solid API and is quite easy to interact with."
"Fireworks AI has positively impacted my organization by helping my productivity go up."
"Since using Fireworks AI, being part of their startup program has resulted in significant cost savings and has helped accelerate our development timeline."
"The integration of AutoML features streamlines our machine-learning workflows."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."
"The best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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 features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good."
"It provides the most valuable external analytics."
 

Cons

"One of the things that could improve Fireworks AI is the cost, which I think is really expensive."
"When using the API, it does not return information about the charges for image generation, which would be useful for our solution."
"Fireworks AI can be improved by addressing that costs can rise at scale."
"Receiving a $40,000 bill out of nowhere, for usage I never knowingly incurred and which my own dashboard does not reflect, has been an extremely stressful experience."
"Fireworks AI could be improved, as documentation could be clearer in some areas, especially around advanced configs."
"In the current function calling of Fireworks AI, I am using it as one part of my RAG system. If Fireworks AI could be enhanced not only with the function calling I currently use, but also by adding a variety of other connections, then I think it would lead to an even better situation."
"Based on my exploration so far, I find that it is too early to judge any improvements or negative aspects of Fireworks AI, as I am still in the exploration phase."
"The only challenge is that Fireworks AI is not a ready-made business application; you have to customize it to suit your organization's taste, and it lacks a user-friendly dashboard, making it very difficult to grasp."
"Some of the tools should have more advanced settings available. Some are very locked into certain features and settings, and there is no customization."
"I think the technical documentation is not readily available in the tool."
"Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful."
"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 would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"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."
"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."
 

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
University
13%
Computer Software Company
9%
Outsourcing Company
8%
Construction Company
8%
Financial Services Firm
9%
Manufacturing Company
9%
Outsourcing Company
8%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise3
Large Enterprise2
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Fireworks AI?
Be extremely cautious. Although it is marketed as 'Pay-As-You-Go,' there are no spending caps, no threshold alerts, and no notifications. We received a sudden $40,000 charge via AWS Marketplace, wh...
What needs improvement with Fireworks AI?
Billing transparency and safeguards are urgently needed. A product sold as 'pay-as-you-go' should never produce a surprise $40,000 charge with no spending cap, no real-time threshold alert, and no ...
What is your experience regarding pricing and costs for Google Vertex AI?
I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.
What needs improvement with Google Vertex AI?
Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful. To ...
What is your primary use case for Google Vertex AI?
Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.
 

Also Known As

No data available
Vertex, Google Vertex AI
 

Overview

Find out what your peers are saying about Fireworks AI vs. Gemini Enterprise Agent Platform and other solutions. Updated: June 2026.
907,372 professionals have used our research since 2012.