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Gophish vs Lightning AI comparison

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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

Arctera Insight Platform
Sponsored
Average Rating
0
Number of Reviews
0
Ranking in other categories
Data Governance (61st), Compliance Management (31st)
Gophish
Average Rating
8.6
Number of Reviews
22
Ranking in other categories
AWS Marketplace (2nd)
Lightning AI
Average Rating
8.6
Number of Reviews
2
Ranking in other categories
AWS Marketplace (71st)
 

Featured Reviews

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JC
Administrador de sistemas informaticos en red at a consultancy with 201-500 employees
Phishing awareness campaigns have improved training impact but still need smarter automation
I think Gophish could be improved with more automation, for instance. It is great that you can create templates, schedule them, and do everything you want in Gophish, but it would be nice to have a small integrated AI model with which you could create email templates and phishing templates. It would be nice if Gophish implemented artificial intelligence. I wish Gophish could provide more support and be more advanced and that they continue developing it because it seems that Gophish does not get many updates, and I think they need to implement more features. It is great because it is free, but it needs more features. It would be cool if there was an artificial intelligence model that could create phishing campaigns or templates or email templates for you integrated within Gophish. I would rate it a seven. It is quite good and free software, but it needs more substance. It also depends on the number of clients a company has; it may be necessary to launch Gophish in a staggered way, which I also think is a drawback Gophish has. The issue is that if there are many people being targeted, for example, 5,000 employees in a company and you send 5,000, it may be that sending so many messages gets blocked by the security systems companies have. I think that Gophish could also improve the message sending flows. I gave it a seven because there are things to improve and it is not perfect. As I said before, it would be nice if templates could be created with AI, integrated into Gophish using an API with Gemini or ChatGPT or whichever, but the point is that it would be nice if the sending flows at large scale were better managed. When you send a phishing campaign to 5,000 people, you have to send it in sections in a staggered way, for example: to these 5,000 people it is going to be sent between eight in the morning and four in the afternoon. If you send them all at once, the phishing may get blocked and then the campaign has no effect. I would like it if Gophish implemented more improvements because they are needed, as it is kind of a bit stagnant. I hope that in the future they add more improvements including creating personalized templates with artificial intelligence and improving the message sending flows.
Shravan Revanna - PeerSpot reviewer
Product Engineer at a non-profit with 51-200 employees
Rapid experimentation has transformed our AI prototyping and collaboration workflows
There are definitely a few areas where Lightning AI can improve. Overall, we have had a positive impact, but there are definitely a few areas it could enhance. One area is cost visibility and resource management. There are multiple teams running experiments, GPUs, and long-running sessions. It is not always obvious how much compute is being consumed and what the projected costs might be. More granular visibility and alerts would help the team manage usage proactively. Another area is workspace and project organization. As the number of experiments grows, it can become difficult to keep projects, notebooks, data sets, and test environments organized. Better lifecycle management could help achieve this and discoverability would be useful for larger teams. We have also encountered situations where long-running sessions or development environments needed more resilience. While this is not unique to Lightning AI, interruptions during model training and experimentation can be frustrating, especially when working with larger data sets. From an enterprise perspective, I think there is room to strengthen governance and operational control. Features around permissions, auditability, environment standardization, and usage policies become increasingly important as adoption expands across teams. I would particularly appreciate better support for moving successful experiments into production workflows. There could be better cost and resource visibility, stronger project and experiment organization, improved reliability for long-running sessions, stronger governance capabilities, and a smoother journey from experimentation to production. None of these are major blockers for us, but these are areas where the platform could become more valuable as the team and workload scale. A minor annoyance would be stronger project and experiment organization. When more data sets and more projects come into place, it becomes difficult to organize, and keeping them in a standardized way becomes slightly difficult. That is an area I wanted to highlight. There is not much of a pain point. There are a few minor suggestions I would mention, such as observability and experiment tracking at scale. When teams start running many experiments across different models, it becomes increasingly important to have a clear view of what changed and why performance improved or declined. That could be one area. Another area is cross-team discoverability. As AI adoption grows within an organization, valuable experiments and reusable components can be scattered. Better mechanisms for surfacing reusable workflows and templates would be beneficial. I would also appreciate continued investment in LLM and agent development workflows. The AI landscape is evolving rapidly. These suggestions come from the perspective of a team that is using the platform heavily. Most of the core capabilities work well today, which is why the feedback is more about helping the platform scale with a growing AI organization rather than fixing major shortcomings.
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Top Industries

By visitors reading reviews
No data available
Construction Company
26%
Manufacturing Company
11%
University
11%
Comms Service Provider
8%
Construction Company
38%
University
15%
Manufacturing Company
9%
Outsourcing Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business8
Midsize Enterprise6
Large Enterprise9
No data available
 

Questions from the Community

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What is your experience regarding pricing and costs for Gophish?
Regarding the price, setup cost, and licensing of Gophish, I do not remember having to pay to use it. It is a complet...
What needs improvement with Gophish?
In my opinion, Gophish could be improved to better meet my needs or those of other users, but I did not really encoun...
What is your primary use case for Gophish?
I used Gophish for a project last August, a phishing attack simulation, and I reused it recently because a student fo...
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Overview

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