Fiddler AI Reviews

My main use case for Fiddler AI is as a tool to monitor, explain, and govern machine learning models and generative AI applications, which helps organizations build trust by tracking their performance, preventing security threats like hallucination and prompt injection, and ensuring regulatory compliance.

In a recent project and task, I showcase how we use Fiddler AI's key features like AI observability and monitoring, which track hundreds of metrics across traditional machine learning models and large language models to monitor performance, data drift, and accuracy. We use it as security and guardrails that implement real-time safety measures to block harmful outputs and protect against jailbreak and prompt injection attempts. We also utilize it for explainability, helping teams understand why an AI model made a specific prediction or decision, providing deep root cause analysis when problems occur. Lastly, we employ it for governance and compliance, creating centralized audit trails and model cards to help companies meet strict regulatory standards and risk management requirements.

Fiddler AI has significantly impacted my organization positively, particularly in security and guardrails. As we use many tools, we previously had humans monitoring all of them. Human errors created loopholes, but now with Fiddler AI, which is trained using our company policies and playbooks, human intervention has become minimal. We have reduced human intervention by 70% and human mistakes by around 90%, which are the points I wanted to mention.

Reducing human intervention by 70% directly affects our productivity and day-to-day work. A person who worked with one policy or guardrail for a week now hardly takes one day, saving us six days of effort for each cycle. Moreover, the reduction in human mistakes has significantly improved product quality, with great feedback from vendors and other businesses using the product, increasing confidence levels and scores when using Fiddler AI.

I have observed one area where Fiddler AI can be improved. When explaining a product, it is essential to specify company policies and guardrails. Without providing company-specific criteria, the product may hallucinate, as it will compare international policy boards with our company's policies. Therefore, to achieve better results, we need this improvement.

While support for Fiddler AI is quite good, documentation needs improvement. The tool's documentation should specifically mention the policies, security aspects, and all organization-specific documents alongside generic documents to provide more precise information.

I have been using Fiddler AI for the past two years.

Fiddler AI is stable and consistent in its performance for us.

Its scalability is excellent and can handle growing workloads very easily and effectively.

Customer support for Fiddler AI is very good. They support critical issues (P1) within a day or a few hours, while medium or low priority issues (P2) are fixed within two to three days.

Before choosing Fiddler AI, we evaluated several options, including Gemini, ChatGPT, Azure, and other vendor products. Ultimately, we found Fiddler AI to be the best tool for security and governance purposes in the market.

I have seen a return on investment with Fiddler AI. The investment reflects both quality improvements and savings in time and money. Previously, we used a manual solution, requiring us to validate every security aspect manually before deployment. Now, with this AI solution, we have increased efficiency. For instance, what used to take 12 months for validation is now accomplished much faster, directly benefiting our costs.

Regarding the features, I want to mention that top AI products are generally split into two categories: versatile conversational assistants and specialized creation tools. Widely adopted and highly rated AI products include ChatGPT, Claude, Google Gemini, and others. However, Fiddler AI focuses on governance and compliance, and security and guardrails, which is why I respect the tool and see many use cases for it.

Among the features I mentioned, I find governance and compliance the most valuable and impactful in my work because, in our IT company, we use many tools daily. As part of our day-to-day activity, we utilize in-house and vendor-specific tools. If the governance and compliance section is maintained by AI, it highlights those features, which is significant for the Information Resource Management (IRM) team to get details on governance and compliance. We use hundreds of vendor-specific tools, which makes it hard to measure governance or regulatory aspects every time. Fiddler AI checks this holistically and provides a dipstick report on the same governance and compliance.

Fiddler AI is deployed in our organization first in UAT and then in production. We use AWS as the cloud provider for deployment. We purchased Fiddler AI through the AWS Marketplace.

Regarding Fiddler AI's capabilities, I think its governance and security are very high, featuring two to three layers of security that provide excellent protection for secure data. The accuracy and reliability of Fiddler AI's output range between 70% to 80%, and I can trust it. However, I believe that 20% to 30% requires human check or validation, as AI's outputs need checking for correctness.

My advice for others looking into using Fiddler AI is that if a company or individual seeks security, governance, and scalability for their product, they should definitely consider it. I would rate this review an 8 out of 10.

I am currently working with D Innovation Private Limited for a client called Casia in a Senior Data Engineer profile, where I have been working for the last 1.6 years. I have a total of eight years of experience working at Infosys as well.

I use data engineering tools for Power BI and Fiddler. For analysis, I have to create a model to analyze the data. I utilize our data for this purpose.

In day-to-day work, if I need to support or create any language-based model with features, I find it very easy to use. I create modular solutions that convert from one language to another. Fiddler AI is the best way to handle this right now.

I am utilizing Fiddler AI for AI agents with multiple Matillion tools and Kubernetes. I mention that we have AI agents for these purposes. The pricing is good, at approximately 0.002 dollars per developer, which is very cost-effective. Additionally, the latency is less than 100 microseconds, which is excellent for handling Fiddler AI.

I have been using Fiddler AI in multiple ways for approximately two to three years.

Fiddler and Fiddler AI serve different purposes. I utilize Fiddler for creating Power BI reports, and it is very good visually. There are multiple options for color choices and data design, allowing me to explain data effectively. Right now, I utilize Fiddler AI for machine learning purposes.

In Power BI projects, I use it for data enhancement and data quality checking for schema validation. I use it for data analysis as well. I connect it directly to my reports through Fiddler API and write Python code for analysis. I integrate this into my Power BI projects.

All features in Fiddler AI are good, offering excellent connectivity options. The REST API is good, supporting multiple languages with connected speech and responsive answers for any AI tools. Data performance is very good, and it is very easy to use. From a learning perspective, it provides responsive answers for data manipulation and other utilization needs.

To improve things, I need to connect it for handling LLM prompts and data management. This is mainly for understanding queries better. Snowflake Cortex is similar, but it does not give very good responses and sometimes provides poor experiences. Fiddler AI provides a better way.

I should not give too much data to any AI agent because when an agent reads and analyzes data itself, this is the wrong approach. Giving full data access to any organization is not a good practice. I need to analyze any tool first before proceeding. After that analysis, I can move forward.

I have been using this solution for approximately six to seven years.

Fiddler AI is stable and performs well.

Scalability is very good. The solution handles 7 to 15x more data, which is a very good opportunity.

Customer support is very good. We handle data and data breaks well, and Google Cloud support is available anywhere, which is excellent. Fiddler AI supports healthcare, AI defense, intelligence, healthcare data, and insurance data.

I did not use a previous solution and have just now tried Fiddler AI.

The initial setup is fine with no issues.

The implementation team was fine.

A significant amount of time is saved, which is very good for us. Both time and money are saved. If I needed to utilize a task with 10 people, I can now handle data analysis with only five people, which is very good for our organization.

Licensing cost is very low, which is very good. The usage cost is reduced by 70 to 80x compared to other solutions, and the latency is less than 100 microseconds, which also provides a very good value.

I have definitely considered other solutions.

I will need to check on further recommendations after some time. I rate this product at 8.5 out of 10.

For work purposes, I have used Fiddler AI. During those four months, my main use case for Fiddler AI was trying to get the logs, control the results from the testing, and get the feeds from the testing. When I was doing mobile testing and web testing, I tried to manipulate the testing results. This was my use case of Fiddler AI.

There are very useful features Fiddler AI offers, but for me, the most useful ones were Traffic Inspection, Request Response Modification, Load and Performance Diagnostics, Traffic Filters, Session Organization, Security Testing Capabilities, and breaking points and debugging. Mobile and remote device testing were helpful, and I found these features very useful for me.

The most useful feature for me was response and request modification because, in most cases when doing testing on mobile, I needed to change the responses and requests through Fiddler AI. When I needed to work in a more organized way, I used filters, traffic filters, and session organization. One of the core features that helped me as well was HTTP and HTTPS traffic inspection. These have been helpful for me.

Using Fiddler AI improved my technical expertise about understanding logs, bugs, sessions, and traffic, and how to control the request and response. This knowledge is transferable, and I am still using it with other tools and in my daily life.

There can be improvements in getting error logs more clearly and in a more organized way with AI analysis of what is actually wrong and what can be the solution or how to report that error if it is a bug. That AI analysis can be helpful.

Fiddler AI is great to work with, but there can be improvements on the UI. Sometimes Fiddler AI's UI is a bit more complicated because the options sometimes feel confusing when you open the tabs and sub-tabs. This can be an improvement on the UI, and errors can be understood more clearly after an AI analysis of the logs. These are my suggestions for why I did not provide a 10.

I have been using Fiddler AI for the initial four months of my junior QA engineer role.

I sometimes felt instability when I was using breaking point and filtering modes. I do not actually remember some of the crashes, but sometimes my PC got hanged. I do not know if it is my hardware issue or a software issue, but sometimes I felt a bit stuck doing the work.

I did not work collaboratively, but Fiddler AI is really stable, I believe.

I did not work collaboratively, but Fiddler AI is really stable and I believe it is scalable.

I did not need to reach out to the customer experience team.

Before my current position, I was at A1QA, where I was introduced to Fiddler AI.

Fiddler AI was deployed in my organization on my desktop. I tried to get it from the source, just installed it on my desktop, and started using it.

Initially, it was really tough for me to work with Fiddler AI because the UI was a bit more technical, and it was tough to understand. I felt the UI was a bit more congested. That is why it was tough for me initially, but when I got used to it, it was pretty easy for me.

I did not actually need the documentation because I had a mentor in my workplace. In most cases, when I was in any problem, I would reach out to her, and she helped me a lot.

Using Fiddler AI saved me approximately one hour. If I needed three hours to complete a task initially, I could do it in two hours with Fiddler AI's help.

Since I started using Fiddler AI, it improved my technical expertise about the understanding of logs, bugs, sessions, and traffic, and how to control the request and response. These have been very helpful. The knowledge has been transferable, and I am still using that knowledge in other tools and my daily life. Fiddler AI really helped me to test faster. I might need to switch to any other tools that can work for only one feature. I did not need to because Fiddler AI had multiple features that I needed at the time.

I would love to provide advice on using the request response editor and doing HTTP and HTTPS file responses about filtering the sessions. To use those features, you should use Fiddler AI. That is my recommendation. It would be great if you could finish early. The learning curve was smooth, but initially, it was an exponential curve. Initially, it was tough, but when I got used to it, it was really fast. I provided a review rating of 7 for Fiddler AI.

We primarily use Fiddler AI to monitor our AI models in production and catch issues like drift or unexpected prediction changes early. For example, if a client-facing model starts behaving differently after new data comes in, we use Fiddler AI to trace the problem, understand why it happened, and decide whether the model needs adjustment or retraining.

As a web agency, we have been using Fiddler AI for about eight months now, and it has proven to be excellent. Fiddler AI fits into our workflow as an early warning and debugging layer for production models. It helps us spend very little time manually investigating issues and allows us to focus more time on fixing the actual root cause, which makes our model operations much smoother. Over time, it has become part of our regular workflow whenever we need to check the model health or investigate unexpected behavior. Instead of starting from scratch, we can quickly narrow down to the issue and move faster on resolution with the help of Fiddler AI. For us, it is not just a monitoring tool but rather part of how we keep our AI systems reliable every day.

The two features we rely on most are drift detection and explainability. Drift detection helps us spot issues early, and explainability helps us understand the root cause quickly so that we can take action faster. Together, those features make it easier to monitor and debug models in production.

The alerting feature is also helpful with Fiddler AI because it lets us respond quickly when something changes. The interface makes it easy to move from an alert to a specific model or data slice that needs attention. This saves a lot of time and keeps the workflow smooth.

What helps most with Fiddler AI is the combination of drift detection and explainability. We can first see when the performance or prediction patterns have changed, and then drill into the specific features or slices of data causing the issue. That makes it much easier to figure out whether the problem is data drift, a bad input pattern, or something in the model logic. The most valuable aspect is that Fiddler AI shows both the change and the reason behind it, so we can trace the issues from an alert into the exact data slice or feature that caused the problem.

The main thing I would improve with Fiddler AI is onboarding and ease of use for different stakeholders. The platform itself is very powerful, but making some workflows more guided and adding more ready-made dashboards would make it even better for a team in our situation. It is strong overall, but simpler onboarding, better dashboards, and more automation would make it even easier to use.

I have been working in ADP for about two years and six months to be precise.

Fiddler AI has been stable for us overall, and we have not run into any downtime or reliability issues. It has been dependable for day-to-day monitoring and investigations, and I commend the team managing it.

Fiddler AI has scaled very well for us so far. It has been able to handle our workload even better as we add more models because it gives us a centralized way to monitor and debug everything. From what we have seen, it scales very effectively, and as our usage grows, it still feels manageable and does not become a bottleneck for the team.

We have interacted with Fiddler AI's support team very rarely, but the experience has been very good. They are very responsive, professional, and helpful when we needed clarification or guidance. The support overall was excellent.

We were using a more manual approach before Fiddler AI, along with some basic monitoring tools. We moved to Fiddler AI because it gives us much greater visibility into model behavior and makes debugging much faster.

The documentation, integrations, and everything else look perfect. The only suggestion would be if they could add simpler onboarding, better dashboards, and more automation, as it would be even better.

I am satisfied with Fiddler AI, and I have discussed everything with the relevant parties.

We have clearly seen the return on investment mainly through the time saved and more efficient use case of our eight-member team. We spend much less time manually investigating model issues, which has become a very significant advantage. This means fewer hours lost on debugging and faster incident response. We have not had to add extra headcount just to manage model monitoring, so the platform has helped us stay efficient as we scale.

Pricing for Fiddler AI feels appropriate for an enterprise-level product, so it is not the cheapest option, but the value is there for a production AI use case. The setup cost was manageable since it was a hosted platform, and licensing was straightforward for a team-based deployment.

We did look into a few other options before choosing Fiddler AI, mostly general ML monitoring and observability tools, as well as our internal manual approach. Fiddler AI stood out because it was a strong option on explainability, drift detection, and making production debugging easier.

My advice to others looking into using Fiddler AI would be straightforward. If you need serious production monitoring and faster model debugging, go with Fiddler AI. It is especially valuable if your team wants better visibility into drift, explainability, and incident response. I recommend starting with a clear workflow and making sure your team is aligned on what to monitor so that you get the value from it very quickly. I have given this review an overall rating of 8.

My main use case and interest in Fiddler AI is detecting and analyzing AI models. If we can analyze the AI model, it's a significant achievement for us, and we can easily implement AI features.

A specific example of how I tried to analyze and detect an AI model using Fiddler AI involves my work in the QSR domain. Imagine there is a food delivery app. Fiddler AI can be used to recommend restaurants, recipes, and nearby or favorite restaurants.

I found Fiddler AI to be the best solution in the market to analyze the QSR domain. As a QSR domain expert, we can use Fiddler AI to give personalized recommendations. If we have an application for restaurants or QSR, we can personalize the menu, homepage, and cart to a specific user. If I am an end user using an application and receiving menu choices based on my previous recommendations, that would be an eye-catching moment that we can easily achieve using Fiddler AI.

The best features that Fiddler AI offers include monitoring and managing the model, which is the best feature.

When I say monitoring and managing the model, the aspects I find most valuable are tracking performance, detecting drift, and alerting when something goes wrong. Fiddler AI helps me analyze behavior and a bunch of data, since AI is nothing but a set of data. I can track user journey and analyze user behavior.

Fiddler AI has positively impacted my organization through my exploration, even though I haven't implemented it in my company. I am an AI enthusiast, so I try to explore solutions that I can recommend to my company. Based on my exploration, I think Fiddler AI could have a positive impact if my company decided to use it. It has the potential to help my application personalize recommendations according to the user, which is the main trending feature of any QSR app available in the market.

If we can monitor the models, we are monitoring the behavior of the customer. If we can offer what the customer wants, it will directly and indirectly increase sales and automatically generate more revenue for us.

I would not say Fiddler AI needs improvements. Whatever Fiddler AI offers is great to have.

I have been exploring Fiddler AI. I haven't used it particularly, but I explored it out of curiosity to learn AI. I found Fiddler AI to monitor and manage machine learning and LLM models.

Fiddler AI is stable from what I explored.

Based on my exploration, I would rate Fiddler AI's scalability as eight on a scale of one to ten. It depends if my user base increases, which part I haven't tested, so I don't want to comment on that.

The customer support for Fiddler AI, based on my interactions, is looking good. I'm interacting with you, and I would say I had a great experience connecting with you.

Positive

I haven't previously used an AI solution for model monitoring or AI explainability. I implemented an in-house personalization solution providing recommendations. There was a platform we used for personalization, but I can't give the name of the company due to confidentiality.

I haven't reviewed the pricing, setup cost, and licensing while exploring Fiddler AI because it's not my area of focus. I recommended it to my organization, and they have a specific team or finance team who will review everything, and if the budget allows, they will definitely pursue it.

I definitely anticipate specific benefits and money growth if my organization adopts Fiddler AI. It will help us quickly adopt and implement solutions.

I did not evaluate any other AI platforms before considering Fiddler AI. In the market, there are already multiple personalized platforms. We explored Sitecore as well.

If I get the opportunity to use Fiddler AI in my organization, I would keep it in my organization first, then go with the public and hybrid part by using the cloud. If I were to use a hybrid cloud, I would prefer to use either AWS or Azure, depending on the budget my company provides.

My advice to others looking into using Fiddler AI is simply to monitor and detect the models and user behavior. Fiddler AI is the best solution in the market, and I would give it my strongest recommendation. My overall review rating for Fiddler AI is nine out of ten.