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Palantir builds enterprise software that helps government agencies and commercial organizations connect scattered data to analysis, operational workflows, artificial intelligence and software deployment. Its four principal platforms have different jobs: Gotham supports government and defense missions, Foundry organizes data and operations, AIP connects generative AI to an organization’s processes, and Apollo manages continuous delivery across cloud and other environments.
Palantir in plain English
Palantir Technologies sells software to institutions that have complicated data, many operating systems and high-stakes decisions to make. A utility may have information in separate systems for maintenance, customers, weather and field crews. A manufacturer may keep production, purchasing, quality and logistics data in different applications. A government organization may need to combine reports, sensor feeds, plans and mission information. Palantir’s platforms are designed to connect those sources into a working view of the organization, then link that view to analysis and actions.
The company’s description in its 2025 Form 10-K is: “We build software that empowers organizations to effectively integrate their data, decisions, and operations at scale.” In practical terms, that means the software is intended to do more than display dashboards. It can represent important entities, apply business or operational logic, help people make decisions, and support workflows that carry those decisions into day-to-day work.
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Palantir is therefore primarily an institutional software provider, not a consumer app maker. An individual normally does not download a Palantir application for personal use. A government department or business buys access through an organizational contract, connects approved data sources, configures models and workflows, and gives relevant employees or mission users access.
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What Palantir’s software is designed to do
The platforms can be used separately or bundled. Their shared idea is to connect three things that are often separated inside large organizations:
- Data: information from databases, applications, files, sensors and other systems.
- Decisions: analysis, rules, forecasts, priorities and recommendations that help people choose what to do.
- Operations: the tasks, approvals, changes and actions that follow a decision.
A simple way to picture the process is:
- Bring relevant information together from existing systems and sources.
- Give that information business or operational meaning, rather than leaving it as disconnected tables.
- Analyze the resulting picture and identify a decision or priority.
- Turn the decision into a workflow, assignment, update or other controlled action.
- Run the software in the environment required by the organization and continue updating it.
This is an explanatory model, not a claim that Palantir replaces every database, enterprise application or specialist tool. The company describes its platforms as working with an organization’s existing data and operations.
The four principal Palantir platforms
Gotham, Foundry, AIP and Apollo are related, but they are not four versions of the same product. The table shows their distinct roles.
| Platform | Primary job | Main setting or users | Relationship to the others |
|---|---|---|---|
| Gotham | Integrates information for situational awareness, operational decisions and mission coordination. | Government, defense and intelligence organizations. | Can be used on its own or alongside Foundry, AIP and Apollo where an organization needs those capabilities. |
| Foundry | Data management, logic authoring, analytics, Ontology development and workflow building. | Commercial operations and other organizational teams that need a shared data-and-operations foundation. | Often provides the operational foundation to which AI capabilities or deployment services are connected. |
| AIP | Connects generative AI and other AI capabilities to organizational data and processes. | Teams building AI-assisted workflows, agents and automations, with human review and security controls. | Uses the organization’s operational context rather than treating an AI model as a standalone chatbot. |
| Apollo | Coordinates continuous delivery, including software updates, security changes and platform configuration. | Deployment and platform teams operating across cloud, on-premises and other environments. | Helps deliver and maintain Palantir platforms, including the other products, across different environments. |
Palantir says the platforms may be bundled. A customer could therefore use Foundry to organize commercial operations, AIP to add AI-assisted work, and Apollo to deliver updates in the organization’s required environments. Gotham serves a different mission emphasis, although the same general pattern of integrating data with decisions and operations applies.
What is Gotham?
Gotham is the platform Palantir describes for government, defense and intelligence missions. Its stated purpose is to integrate information from different domains and sensors so users can build situational awareness, make operational decisions and coordinate missions.
For a government organization, the challenge may be that relevant information is distributed among agencies, systems or sensor types. Gotham is intended to help authorized users view relationships and circumstances in a common operational context instead of examining each source in isolation. That context can support planning, prioritization and coordination.
These are Palantir’s stated purposes for Gotham. They should not be read as independent verification that every deployment produces a particular mission outcome. Results depend on the data supplied, the organization’s processes, user training, security requirements and the way the system is configured.
How Gotham differs from a general analytics tool
A conventional analytics tool may answer questions about historical data or display a report. Gotham is positioned around mission work: combining information from multiple domains, understanding what is happening, deciding what should happen next and coordinating activity. That operational emphasis is the key distinction, not a claim that Gotham eliminates the underlying source systems.
What is Foundry?
Foundry is described as Palantir’s foundational data operations platform. Its functions include data management, logic authoring, analytics, Ontology development and workflow building across teams and organizational units.
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Foundry’s role is to give an organization a common place to work with data and the logic that makes the data useful. A company can connect information from separate applications, define how important objects relate to one another, create analyses and build workflows for employees or operational teams. The goal is to make data usable in the context where decisions are made, rather than leaving each department to interpret isolated extracts.
Examples of Foundry work
- A supply-chain team can examine orders, suppliers, inventory and distribution constraints in one operational model.
- A manufacturer can connect production-line information with quality or maintenance processes.
- A utility can organize data used by planners, field teams and customer-service operations.
- A public-health organization can combine information needed for planning and response.
Those examples describe the types of sectors and work the company identifies; they do not imply that every organization uses every capability or achieves the same result.
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AIP is Palantir’s generative-AI platform. It connects large language models and other AI capabilities to an organization’s data and processes. The company describes tools for AI agents, automations, evaluation, security controls and human review.
The important distinction is context. A general language model can generate text, but it does not automatically know which inventory record, customer order, maintenance rule or approval process is authoritative. AIP is intended to connect AI behavior to the organization’s operational data and the actions that its processes permit.
What an AIP-enabled workflow can contain
- Context: approved organizational information supplied to the model or agent.
- Actions: defined operations an agent or automation may request.
- Evaluation: methods for checking how an AI capability performs.
- Security controls: restrictions governing data and available actions.
- Human review: a person’s involvement before or during consequential work.
This design treats generative AI as part of a governed workflow rather than as an unsupervised replacement for employees. The exact controls, review points and permissions depend on the customer’s implementation.
What is Apollo?
Apollo is Palantir’s continuous-delivery platform. It coordinates software updates, security changes and platform configuration across cloud, on-premises and other environments. Palantir also uses Apollo to support delivery of its own platforms.
Organizations rarely run all software in one identical place. Some workloads may be in a public cloud, others in a private data center, and still others in specialized or restricted environments. Apollo’s role is to help manage the software lifecycle across those locations so that updates and configuration changes can be delivered in a coordinated way.
Why deployment location matters
Where software runs affects connectivity, security controls, maintenance procedures and the speed at which updates can be introduced. Apollo addresses the delivery side of that problem. It does not turn every environment into the same infrastructure; instead, it is designed to coordinate the platform across the environments an organization must operate.
What Palantir means by the “Ontology”
In Palantir’s terminology, an Ontology is a structured representation of an organization’s data, logic and actions. It is not simply another name for a database. A database stores records; an Ontology is intended to represent meaningful entities, their relationships, the logic applied to them and the actions that users or workflows can take.
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For example, an organization might model a plant, a production line, a supplier, a customer order and a shipment as operational objects. The model can connect those objects to relevant data and permitted actions. Palantir’s architecture material uses supply-chain examples such as updating a purchase order or changing a distribution strategy.
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- Shared meaning: Different teams can work with the same definition of an order, asset or shipment.
- Relationships: Users can see how records connect instead of reviewing unrelated tables.
- Operational logic: Rules and calculations can reflect how the organization actually works.
- Action links: Analysis can lead to a workflow or approved change, not just a chart.
- AI context: AI tools can work with defined objects, relationships and actions rather than unstructured information alone.
Building a useful Ontology requires the organization to decide which concepts matter, how they relate and which actions are allowed. That modeling work is one reason Palantir targets complex organizations rather than casual individual users.
Who uses Palantir?
Palantir reports customers in both government and commercial institutions. The company gives examples spanning utilities, automotive manufacturing, oil and gas, pharmaceuticals, supply chains, public health and military organizations.
These customers have different missions, but they share characteristics that make an operational data platform relevant: information is distributed across systems, decisions involve multiple teams, and the cost of acting on incomplete or delayed information can be significant.
Government and defense users
Government customers may use the platforms for defense, intelligence, public-sector operations or other missions involving information from multiple domains. Gotham is the platform most directly associated with this setting, while AIP and Apollo can provide AI or deployment capabilities where appropriate.
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Commercial users
Commercial customers may operate factories, supply chains, utilities, pharmaceutical programs or energy assets. Foundry is positioned as the general data-and-operations foundation for these environments, with AIP available for AI-enabled workflows and Apollo for delivery across required infrastructure.
How many customers does Palantir have?
Palantir’s 2025 Form 10-K reported 954 customers as of December 31, 2025. The filing counts separately contracted and invoiced divisions or agencies as distinct customers. Therefore, 954 does not necessarily mean 954 separate parent companies; one parent organization can account for more than one reported customer relationship.
| Reported measure | Figure | Qualification |
|---|---|---|
| Customers | 954 | As of December 31, 2025; separately contracted and invoiced divisions or agencies are counted separately. |
| 2025 revenue | $4.5 billion | Palantir Technologies Inc. figure for the 2025 fiscal year, reported in its 2025 Form 10-K. |
| Government share of 2025 revenue | 54% | Company-reported portion of 2025 revenue. |
| Commercial share of 2025 revenue | 46% | Company-reported portion of 2025 revenue. |
| U.S. share of 2025 revenue | 74% | Company-reported portion of 2025 revenue from U.S. customers. |
| Outside-U.S. share of 2025 revenue | 26% | Company-reported portion of 2025 revenue from customers outside the United States. |
These figures describe 2025 and should not be treated as a forecast. A later annual filing or quarterly report may change the reported totals and mix.
How a Palantir deployment typically fits together
Palantir’s products are sold to organizations with complex environments, so an implementation is more involved than installing a desktop application. The following sequence explains the main pieces without assuming that every contract follows identical steps.
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1. Define the operational problem
The organization identifies a decision or process that crosses systems or teams: for example, production planning, supply-chain response, mission coordination or asset maintenance. Starting with an operational question keeps the project from becoming a generic data warehouse exercise.
2. Connect relevant sources
Data from existing databases, applications, files, sensors or other approved sources is made available to the platform. The useful sources depend on the problem being solved, and the organization remains responsible for data quality, permissions and legal requirements.
3. Build the organizational model
Teams define the important objects, relationships, calculations and rules. In Palantir’s vocabulary, this is where the Ontology gives data operational meaning and links it to permitted actions.
4. Create analyses and workflows
Users build views, analyses and workflows that help people decide and act. A workflow might assign work, request approval, update a record or change a plan, subject to the organization’s controls.
5. Add AI where it is appropriate
If a process benefits from generative AI, AIP can connect models or agents to approved context and actions. Evaluation, security controls and human review are part of the design rather than optional afterthoughts.
6. Deliver and maintain the software
Apollo coordinates updates, security changes and configuration across the environments where the software runs. This is particularly relevant when an organization operates a mixture of cloud, on-premises or other environments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why Palantir targets large, complex organizations
Palantir describes its target opportunities as large and complex, with challenging data environments, high installation costs and long sales cycles. Those conditions explain both the product design and the buying process.
- Many systems: The value depends on connecting information that was not designed as one unified application.
- Multiple stakeholders: Data owners, operators, security teams and executives may all need different views and permissions.
- Operational consequences: A workflow can change a plan, assign work or affect a mission, so governance matters.
- Specialized environments: Software may need to run in cloud, on-premises or other controlled locations.
- Implementation effort: Modeling data and processes takes organizational work in addition to licensing software.
Palantir says, “Our customers pay us to use the software platforms we have built,” in its 2025 Form 10-K. The statement describes the company’s business model; it does not mean that every customer contract has identical pricing, scope or terms.
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Not a consumer productivity app
Palantir’s primary customers are institutions. The platforms are configured around organizational data, permissions and workflows, rather than personal notes, messaging or entertainment.
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Not just a dashboard product
Dashboards may be part of an implementation, but the stated emphasis is integrating data with decisions and operations. The Ontology and workflow capabilities are intended to connect analysis to action.
Not a single artificial-intelligence chatbot
AIP is one platform in a broader system. Its purpose is to connect AI capabilities to organizational data, processes, controls and review, not simply to provide an open-ended chat window.
Not a replacement for every source system
The platforms are designed to work with an organization’s data and operations. Existing databases, applications and specialist systems may remain in place even when Palantir provides a shared operational layer.
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Important limitations and questions to ask
Palantir’s product descriptions are company statements about intended capabilities. They do not independently establish the outcome of every deployment. Before adopting any enterprise platform, an organization should ask:
- Which data sources will be connected, and who is accountable for their accuracy?
- Which users can see sensitive information or trigger actions?
- Which decisions remain with people, and where is human approval required?
- How will AI outputs be evaluated, monitored and corrected?
- Where must the software run, and how will updates be delivered there?
- What implementation work, training and ongoing governance will be required?
- How will success be measured for the specific operational problem?
These questions matter because the software’s usefulness depends on more than features. Data quality, process design, permissions, adoption and deployment constraints all affect the result.
How the platforms fit in two example settings
Commercial supply chain
A manufacturer could use Foundry to connect orders, suppliers, inventory, production and distribution data. Its Ontology could represent those objects and the relationships between them. Analysts and planners could build workflows around a constrained shipment or purchase order. AIP could assist with approved analysis or recommendations, while Apollo could coordinate delivery of the platform across the company’s cloud and on-premises environments.
Government mission
A defense or intelligence organization could use Gotham to integrate information from different domains and sensors for situational awareness and mission coordination. If the organization also needs AI-assisted workflows, AIP may provide that layer with security controls and human review. Apollo can coordinate platform updates in the environments the mission requires.
These examples illustrate how the products can complement one another; they are not claims about a specific customer deployment.
The simplest way to remember Palantir
Think of Palantir as software for turning an organization’s scattered information into an operational system: data is connected, important objects and relationships are modeled, analysis informs decisions, workflows carry decisions into action, AI can be added under controls, and Apollo helps deliver the software across the environments where it must run.
The Bottom Line
Bottom line: Palantir sells institutional software that connects data, decisions and operations. Gotham focuses on government and defense missions, Foundry on data operations and workflows, AIP on governed generative AI, and Apollo on continuous delivery. Its 2025 filing reported 954 customers and $4.5 billion in 2025 revenue, split between government and commercial institutions.
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