There are several use cases that we are working on with Palantir Foundry. The first thing is for data model creation for all our data engineering pipelines. That is one use case. Palantir Foundry also has an ontology, more of a semantic layer, so that we can directly hand over the data model to the end users. That is another use case that we have, creating the semantic layer ontology. Recently, we have started working on some AI use cases as well. Palantir Foundry has very good wrappers such as AIP Agent Studio and AIP Logic, where you can choose any model and build your own chatbot or any AI function or generative AI function. These are a few use cases we are working on.

I work with different types of data in Palantir Foundry, including structured and unstructured data. We process PDFs and Word documents, but I have not worked on any use case with video and audio, although there are a few teams in our company that actually process video and audio as well. When it comes to textual information, I have worked on several use cases, and Palantir Foundry has made it very simple. There are some built-in functions, and you can also use Python libraries if you want. Additionally, there are no-code tools to parse unstructured information.

Based on my huge experience with Palantir Foundry, I find that starting from the data connection to the end user application, there is a tool for everyone. For example, I do a little bit of coding, and even for me, if I wanted to do some enhancements, I can do it and build specialized applications on it. If there is a person who is from a non-coding background, such as a business analyst, they will have some application to do that as well. For example, I have to build some pipelines on Spark, which needs to use several rows of huge data. There are also no-code tools available which are as good as Spark, not in terms of data management, but at least in terms of learning and ease of use. A business analyst can use that and prepare the same type of pipeline. For everyone, there is a tool or application in the platform.

We use collaborative functionality in Palantir Foundry. Initially, when I started, the collaboration function was not as matured as working on a general open-source application or something else outside the platform. Now it has matured a lot. Starting from the initial dataset, you can create branches up to the application level as well. This is a very good enhancement that Palantir Foundry has made in the last year or so.

The main benefits that Palantir Foundry provides for me as an end user are that everything is in one place. You can use multiple tools, and initially, there may be a little learning curve, but after you get started with it, you will find many advantages. I feel the advantages are on an organizational level. The main advantage is you can decentralize the analytics, and you will have everything in one place, so that you do not need to rely on multiple departments working on different tools. If your organization adapts to Palantir Foundry, then it is a totally different thing, because I see that advantage. I work in an internal audit team as a data engineer and data analyst, and sometimes we need data from different departments to do analysis. It became much easier for our organization because all the data is in Palantir Foundry. That is the main advantage that I see.

Regarding points for improvement for Palantir Foundry, I see that they are improving day by day. In the last one to two years, I have seen many improvements compared to the two years that I have worked on Palantir Foundry. There are many things that come up, but a few things are not intuitive enough. Now that we are in this AI phase, Palantir Foundry has created some wrappers around the models, allowing us to create using a no-code application, chatbots, and LLM functions. The problem is that interaction with outside applications can be difficult with the current setup that Palantir Foundry has. There are ways to do that, but it is not that intuitive, which is what I feel.

I have been working with Palantir Foundry for four years now.

For stability, I would rate it nine out of ten.

For scalability, I would rate it ten out of ten because you have a lot of flexibility. You do not need to worry about spinning up machines as you would with Azure. Here, you can select what memory or configuration you want. It is very easy, a ten out of ten.

I am not sure in general about the technical support, but at least for our company, it is very fluid. Whenever Palantir Foundry introduces a new product, the Palantir people come and train us on new applications, so I would rate that at least a nine or ten.

The setup process for Palantir Foundry is not complex at all. It is very simple. For me, the organization has already adopted Palantir Foundry, so I just need to deal with security policies that are already set up. I can directly start with sourcing the data and get started with the transformation. It is very easy for me, but at the organization level, I am not sure how difficult it is. Since we are in an insurance company, we have a lot of regulations, and I think there are many security policies in place, so all that setup is managed centrally for everyone in the organization.

Regarding pricing for Palantir Foundry, I am not entirely sure about the exact pricing because it is centrally managed by the organization.

When comparing Palantir Foundry with its main competitors on the market, I would say Azure and AWS are competitors because they offer several functionalities. However, Palantir primarily supports data management. I feel they are at the same level as those other cloud providers. I have also seen Microsoft Fabric, which has good pipeline building capabilities, but I am not sure about its AI capabilities since it is connected to Azure.

The visualization part in Palantir Foundry works for me at least if I want to see how the data is structured and for an initial analysis, but I would say it is not as matured as Power BI or Tableau in the market. Compared to Power BI and Tableau, it is not that mature. You can do a lot of things, but UI-wise, I feel Power BI and Tableau take precedence over what Palantir Foundry has. Palantir Foundry has it, but it is not as mature as those applications.

I am using some data integration features, including the in-built data integration feature in Palantir Foundry. We have several external sources apart from Palantir Foundry, so our application data is stored there. We use the data connection application and bring in all the data there.

I am not entirely sure about my level of satisfaction with the functionality of Palantir Foundry, but I think it is good. My overall review rating for Palantir Foundry is eight out of ten.

One of the leading European manufacturing plants uses Palantir Foundry for manufacturing interior parts of various car brands such as Honda, Hyundai, Ford, Mercedes-Benz, and BMW. This involves highly secured information that is not supposed to be shared with any competitors.

My experience with Palantir Foundry and Azure has been good. Palantir Foundry is costly, but Azure is open, which allows for easier experimentation. Being a closed product, Palantir Foundry is difficult to practice offline unless we have an enterprise edition. However, it is very secure compared to other platforms.

Palantir Foundry's best features include security, built-in features, low-code, no-code platform, and ease of use.

The collaborative workspaces within Palantir Foundry contribute to team efficiency and project outcomes through seamless operation. The ease of customization is particularly notable.

I have worked with the data lineage feature in Palantir Foundry, which comes by default. We simply need to tick the checkbox and make necessary configuration changes within the system itself. We do not need to procure another lineage platform as Palantir Foundry has its own built-in features for data lineage, data governance, and data security.

The lineage feature helps enhance our data management practices by allowing us to understand the origin of data, track all activities happening on the data, identify users and consumers, and monitor how it flows across the system. This makes it easier to generate reports based on the lineage database.

The predictive analytics capability within Palantir Foundry impacts financial forecasting strategies through its AIP functionality, which includes numerous pre-built models, LLMs, and data science application libraries. Using the AIP library within Palantir Foundry helps us develop quick resolutions for predictive models and analytics.

Apart from the pricing and offline availability issues, improvements are needed in Palantir Foundry's costing factor. Cost-wise, it is not open for everybody, and they are not exposing anything outside of the box.

The major hindrance with Palantir Foundry is that being a very closed product, the cost optimization and costing are not exposed to the end users. Apart from that, it is a very good tool and product.

In terms of stability and scalability, I have not faced any challenges. The scalability and scheduling capabilities are very good. Regarding performance, I have not experienced any stability, performance, or security issues.

I haven't had the opportunity to discuss with Palantir Foundry technical support, but we were able to manage on our own. The documentation and technical support are very good.

Palantir Foundry is an excellent product for data engineering. On a scale of one to 10, I would rate Palantir Foundry a 9.

I am getting into the ontology space using Palantir Foundry. The primary use case is for developing a common business model that includes data, people, and processes, essentially describing how businesses operate. We are applying this model in the utilities sector.

Palantir Foundry offers the core capabilities of a digital twin enterprise. The digital twins concept allows bi-directional data and process integration between virtual and physical worlds. This integration enables what-if analysis, such as simulating tariff changes to plan responses. The low-code app development platform empowers any user to build applications using preferred visualization tools, while the AI platform allows for diverse model integration. Furthermore, Palantir Foundry is not limited to one LLM model, offering flexibility to build personalized models.

Palantir Foundry is missing marketing, which could help it grow. Additionally, the startup pricing is high, causing concern despite being cost-effective in terms of total cost of ownership. Palantir Foundry also needs to change the traditional data management approach from one-directional to bi-directional, near real-time data flow everywhere, which they address through data virtualization.

Palantir Foundry's customer service is excellent. I would rate it ten out of ten. They are knowledgeable, and their boot camps demonstrate solutions in just three days, which typically takes months or years. This speed of delivery was a key driver for choosing Palantir Foundry.

Palantir Foundry's initial setup is affected by high startup pricing, which might scare some people off. However, it is cost-effective in the long run, as it reduces the need for developers.

Palantir Foundry is expensive to start, but not costly in terms of total cost of ownership. Its high initial pricing can be intimidating, but it becomes cost-effective as it reduces the need for a development team. Traditional data management is being transformed to bi-directional, near real-time data flow using data virtualization.

I have heard about a partnership between Databricks and Palantir Foundry. Databricks has similar concepts focused on data.

I rate Palantir Foundry a ten out of ten. Overall, it's an excellent solution, and I recommend it. While I am building a business case to adopt Palantir Foundry, it's worth noting its partnership with Databricks. Our organization already has Databricks, and I hope we will get Palantir Foundry as well.

The AI engine that comes with Palantir Foundry is quite interesting. We have a lot of data from various trials and analyses. We need a machine learning and analytical feature that can push huge amounts of data into the application based on pre-set rules.

Palantir Foundry is emerging as an appealing healthcare platform. Instead of having multiple tools, we can leverage this platform for our data ingestion. The solution has a couple of modules, and we haven't evaluated the entire spectrum. We are taking it one bit at a time. I don't yet have a complete vision or impression, but the tool has served our purposes.

The AI engine that comes with Palantir Foundry is quite interesting. The solution provides good flexibility and scalability.

Palantir Foundry is very good for someone technical. The tool still needs to work on the non-technical part, where people can use its flexibility. The business user should not end up writing huge queries to get small snippets of data. The solution's visualization and analysis could be improved.

I have been using Palantir Foundry for over a month.

I rate the solution a seven out of ten for stability.

Palantir Foundry is a scalable solution. Medium-level businesses can be a good starting point for Palantir Foundry, but it is definitely for enterprise businesses.

The solution’s initial setup is very simple since it is cloud based.

Palantir Foundry has been very forthcoming, but we don't have a full picture of their roadmap and what it would be built upon. It's more of a partnership where we talk about our use cases, and their team comes and tells us about the features we can use. So, it is a work in progress.

You can connect Palantir Foundry to various LLNs like Google Bard, Llama, or OpenAI.

Overall, I rate the solution a six out of ten.

Palantir Foundry is being used for multiple hybrid cloud integrations in one of the services we provide for an existing US-based customer. It's all about getting together data from Azure and Amazon and then providing a hybrid platform through Palantir Foundry. We then provide the analytics or insights enablement for the customer.

Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration. The solution is more scalable and robust compared to other hyperscalers.

The solution's pricing is high. Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive. There could possibly be a little bit of overhead concerning the maintainability of the platform.

We started using Palantir Foundry since last year.

Palantir Foundry is a stable solution.

Palantir Foundry is a scalable solution. We are supporting the implementation of Palantir Foundry for at least three or four customers who have chosen to go ahead with it.

The solution’s pricing is high.

Palantir Foundry is a cloud-based solution. I would recommend Palantir Foundry to other users based on their use cases, the complexity, and the scale of the platform they're looking for. Palantir Foundry has all the right ingredients in terms of the overall data platform, but it depends on the kind of use cases that customers are looking at.

Overall, I rate Palantir Foundry a seven to eight out of ten.

Our company uses the solution as a big data lake for storage and cherry-picking data sets using multiple languages. We create ETL pipelines, run them on a schedule, and export the data to visualize it.

We perform functional tests on the data sets using Excel in a Fusion Sheet. A schema is created that shows all data in columns and can be manipulated to extract meaningful information.

The Code Workbook is used to import data and write code using R, Python, Spark SQL, or PySpark. From there, you can perform calculations and create data sets.

Contour is the graphical user interface that gives us the available basic or automatic operations. You do not need a technical grasp because it is easy to use with knowledge of the basics and filters.

Across our company, there are 3,000 users who access our data lake.

The Code Workbook gives you the option to switch across built-in languages such as Spark SQL, PySpark, R, or Python.

Live video sessions enhance the available documentation and allow you to ask questions directly. There are a multitude of sessions within each framework that occur weekly. At the end of a session, you have the option to read other user's questions or ask questions yourself.

The GUI is easy to use and does not require advanced technical knowledge.

There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers. It is easy to find answers for code issues because Spark SQL and Python have wide user bases. There is a certain probability you won't find a solution-specific answer if you search for it. For example, there are certain errors that are specific to the solution. The more you use the solution, the more you understand it. The learning curve could be reduced with online documentation that includes the meaning of and troubleshooting for error codes.

Predefined code templates or informational prompts would help with writing syntaxes.

I have been using the solution for fourteen months.

The solution is very stable. On occasion, we receive an error but it is rectified within a few hours.

We create use cases that do not have processing limits. The solution is a big data tool so should handle any scalability.

I have not needed technical support.

I previously used Microsoft SQL which is a traditional database. The solution is an advancement because it is a direct jump to a big data source.

Comparing the solution to traditional databases is liking comparing an apple to a banana.

A different team handles the solution and our data lake so I don't have knowledge of the setup. Our team accesses the solution via a web link and creates use cases.

The solution has many features but I am only using the Code Workbook and Contour. Another feature called Slate allows you to create websites or record user data.

Based on my current usage, I rate the solution a seven out of ten.