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.
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.
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.
The solution’s data security could be improved. We cannot use many Python packages with the solution. We were able to use only a few compatible Python packages.
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.
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.
Data Engineer at a manufacturing company with 10,001+ employees
Real User
Nov 29, 2022
Computing is very expensive. If you want to create new models on specific data sets, computing that is quite costly. Python's current setup within Palantir is very limiting. I would like to have more freedom to use Python without limitations.
The application development aspect can be improved significantly and would make a difference. We use some third-party tools for reporting and it's a challenge for us to move data into the file system because Palantir is a closed environment and there are difficulties receiving data from external sources. There are some options in place for dealing with that but it's not sufficiently intuitive. I'd like the data exporting functionality to be as intuitive as the importing.
The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points. It was hard to generalize that and put it into a catalog for people who you didn't know to reuse data. Maybe it's different now. That's the nice thing about Informatica: The catalog is reusable. Palantir is successful, and the institution loves them. I don't want to disparage it. I'm just speaking technically from an interoperability perspective.
Manager at a tech services company with 201-500 employees
Real User
May 23, 2021
The workflow could be improved. Although it works rather seamlessly, the workflow too complicated sometimes. Maybe they can reduce the complexity of the workflow. It could be more modularized in the future. The performance of the engine could be better.
Associate - Inhouse Consulting at a pharma/biotech company with 10,001+ employees
Real User
Jul 12, 2020
They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure. There are some issues with scalability because when we are using a really large dataset, the system is rather slow. The performance can be improved. It would make our life a lot easier if it were as fast as Google Cloud. The GCP is unmatchable in terms of the speed at the moment. From a user perspective, it would be nice to have a preview of what the data is looking like. As it is now, you can see the schema but not the actual data. For example, they can see the different columns but they don't know what's there. If they could inspect the first few hundred columns of data then they would have an idea of what they are dealing with.
Palantir Foundry offers intuitive data management and application development, prioritizing accessibility through low-code/no-code tools, enabling users to integrate, analyze, and collaborate efficiently.Palantir Foundry centers on user accessibility, data governance, and real-time capabilities, streamlining processes with low-code/no-code development. It supports comprehensive data analysis and integration, enhanced by digital twin features that align virtual and physical interactions....
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.
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.
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.
The solution’s data security could be improved. We cannot use many Python packages with the solution. We were able to use only a few compatible Python packages.
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.
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.
Computing is very expensive. If you want to create new models on specific data sets, computing that is quite costly. Python's current setup within Palantir is very limiting. I would like to have more freedom to use Python without limitations.
The application development aspect can be improved significantly and would make a difference. We use some third-party tools for reporting and it's a challenge for us to move data into the file system because Palantir is a closed environment and there are difficulties receiving data from external sources. There are some options in place for dealing with that but it's not sufficiently intuitive. I'd like the data exporting functionality to be as intuitive as the importing.
The one area where improvement could be made is the cost of the solution which is quite expensive.
The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points. It was hard to generalize that and put it into a catalog for people who you didn't know to reuse data. Maybe it's different now. That's the nice thing about Informatica: The catalog is reusable. Palantir is successful, and the institution loves them. I don't want to disparage it. I'm just speaking technically from an interoperability perspective.
The workflow could be improved. Although it works rather seamlessly, the workflow too complicated sometimes. Maybe they can reduce the complexity of the workflow. It could be more modularized in the future. The performance of the engine could be better.
They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure. There are some issues with scalability because when we are using a really large dataset, the system is rather slow. The performance can be improved. It would make our life a lot easier if it were as fast as Google Cloud. The GCP is unmatchable in terms of the speed at the moment. From a user perspective, it would be nice to have a preview of what the data is looking like. As it is now, you can see the schema but not the actual data. For example, they can see the different columns but they don't know what's there. If they could inspect the first few hundred columns of data then they would have an idea of what they are dealing with.