Palantir Foundry Reviews

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.

We use Palantir Foundry for data engineering and self-service tools. Palantir is a great service tool for business users who don't have the necessary IT skills. It helps them to easily draw up their own models and use cases with data by simply using Palantir's drag and drop tool. 

It's a great tool for us to say, "Here's your data. You can play around it, build models with it, aggregate tables, and check everything on your own." It's a self-service tool.

It's deployed on cloud. The cloud provider is AWS.

Over 300 people are using this solution in my organization. It's used on a daily basis.

The ease of use is my favorite feature. We're able to build different models and projects or combine different projects to build one use case. We can put health checks into place, which is advantageous to my company.

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.

I have used Palantir Foundry for almost two years.

It's a very stable tool. If you're a business person instead of an IT person and you just want to develop use cases, it's a good tool for you.

It's scalable because of computation, which is quite costly.

I work with SAP and AWS. The main difference is that Palantir is an easy platform to use. You don't need a lot of skills or training compared to SAP. Palantir has tutorials for how to use the platform. Anybody can use it.

Setup is straightforward.

I would rate the setup as 3.5 out of 5.

Deployment was done in-house.

It's expensive.

I would rate this solution as eight out of ten. 

My advice is to look at your use case and data. First, who is using the data? Is it a person in finance who doesn't know anything about IT? Is it a person with an IT background? You have to establish those things first. Is this to enable someone with a technical background or is this to enable someone with a non-technical background? Is the data structured or unstructured? What is it going to be used for? Palantir works best with structured data.

We used it for a utility company for preventative maintenance of physical utility assets such as poles, electrical wires and transformers.

The virtualization tool is useful.

Palantir Foundry is marketed as an auto-magical tool that can take over your existing system via artificial intelligence and build the building blocks for it. But it requires a lot of manual work and is very time-consuming to get to a functional point. Therefore, the setup of the system needs to be improved.

We have been using this solution for about eight months, and it is deployed on AWS Cloud.

The stability is promising, but it's oversold. I think their marketing is better than the actual product, and other solutions probably work better with more architecture. The solution is a bit underwhelming.

Scalability is limited because it's based on how you set it up. If you don't set it up properly, it's a house of cards that can crumble. You need to put in a lot of effort to create the ontology layer, so if that's not well done, then it's not scalable. If it's well done, then it's scalable.

An issue is that it's very costly to scale because if you house the data in the cloud, there are huge costs. So, the scalability is limited by certain factors. For example, we have approximately 400 to 500 users using this solution. So, we require at least 50 people for deployment, and then the end users can build their custom solutions based on it.

The customer support is not that great.

We've used other visualization tools, and some of our competitors are solutions like Tableau and Power BI. However, we chose Palantir Foundry because other visualization tools are costly and more engineer-driven. Palantir Foundry enables them to disseminate the information to more people and have more people use the viewer. I believe the reason was mainly cost reduction.

A whole team was involved in the implementation, and the ontology team was in charge of setting up the building blocks. So with this tool, you have to create an ontology layer. A different team created the ontology layer, and they hadn't finished by the time we needed the tool. So the ontology team brings the data from existing systems into the new system and then creates all the naming nomenclature. Then, via the naming nomenclature, other people can use it and build upon it. So Palantir Foundry can become a single source of information for all the users.

I believe the implementation was around a two-and-a-half-year project, but that's in the context of a large enterprise. This is because it is not just one person using the tool. It's a massive enterprise, so it takes two to three years to implement the tool for a large enterprise with a lot of data. Deployment takes a few months. We utilized our employees and some Palantir consultants for the deployment.

The return on investment is the efficiency improvement at the end user level, not at the business or IT level. It is costly in the long run at the IT level and difficult to calculate the return on investment at the IT level. But at the business level, I believe there is a return on investment, but it's hard to quantify. I was involved in a risk-related project that was not driven by ROI, so I don't have exact figures on the ROI.

I rate this solution a six out of ten. Regarding advice, I would recommend being vigilant and careful to understand the work required to get to usability. The solution creates a pretty picture of your data if you know how to build the building blocks, but it requires a lot of effort to get there. Everything is predicated on how to bring the data over to Palantir Foundry, so it is essential to watch for the hidden cost of implementation. There are labour costs and other technical costs. So, in addition to licensing, it requires hiring many people and getting technology to bring the data over. Foundry will not tell you about the hidden costs, and you will have to uncover them yourself.

Features that could be added in the next release are additional integration like SharePoint integration, integration with other systems and better workflows for notifications. But, the main challenge for us was the SharePoint integration. They released a better version, but it wasn't working, and we ended up abandoning it.

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. 

This solution is used more for the analytics available on the platform.

The main use was for a COVID-19 White House initiative that was handled by the Vice President, Michael Pence.

It has been the platform for end to end data processing, manipulations, and reporting, greatly improved org's data reporting effort.

The solution offers very good end-to-end capabilities. 

It works very seamlessly. Behind the scenes, the workflow is pretty decent.

The stability is good.

The product can scale.

Technical support is very good.

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.

I've been using the solution for three years or so.

The solution is pretty stable. There are no bugs or glitches. However, the performance could be a bit better.

The solution can scale well. If a company needs to expand, it can do so pretty easily.

The solution has pretty good technical support. They are helpful and responsive and we have been satisfied with their services so far.

Positive

As implementors, we can deploy the solution for our clients. We don't need the assistance of consultants.

We're implementors.

There are still place the solution can have room to improve, we've been mostly quite happy with it. I would rate the product at a nine out of ten.

I'd advise a company considering the solution gets a technical consultant for the platform. They also have sales training on their website. The modules range from simple to complex. You can do some pretty good self-training with your team if you need to.  

We use this solution for everything, including sales. One of our use cases is performing machine learning to gives us an understanding of customer behavior, and which message should be used to target different customers.

The interface is really user-friendly.

This product allows you to do a better data governance job. For example, it will show you data along with information that explains what it is about and what columns it comes from. It will also allow you to give row-level access to different people, which is important in a corporate setting because restricting access to certain datasets is important.

The built-in automation makes tasks easier.

This solution allows you to do an extensive analysis that cannot be done on a personal computer, or even an on-premises server because of the computational effort that is required. It is enormous. 

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.

I have been using Palantir Foundry for the past two years.

This solution is scalable to an extent. Because we have to rely on different cloud providers, the performance when scaling depends on your negotiations with the providers. If there is enough bandwidth then it works fine but if not, it tends to get quite slow at times.

Technical support is okay.

I would rate this solution an eight out of ten.

This is a data integration tool with multiple components that link to multiple sources to create repositories, transform data and make it available for dashboards or management purposes. We're based in the UAE and I'm a senior manager, customer and user of this solution. 

This product has all the various components for getting data, transforming it and visually creating the dashboards without the need to integrate things and no need to check the compatibility. It's user-friendly and a one-stop shop, where you can do everything. 

The one area where improvement could be made is the cost of the solution which is quite expensive. 

I've been using this solution for two years. 

The solution is stable. 

The solution is easily scalable. 

 The technical support is very good and they respond quickly. 

If you plan to use this solution it's important to be aware of data engineering and data visualization concepts. 

I haven't had sufficient experience with this solution so for now I rate it six out of 10.