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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.
Positive
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.
Positive
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.
