Palantir Technologies sells software to government agencies and commercial organizations that need to combine disconnected data, analyze it in context, and connect decisions to operational work. Its four principal platforms have different jobs: Gotham supports government and defense missions, Foundry manages data and workflows, AIP connects generative AI to organizational processes, and Apollo delivers and updates software across different environments. Palantir is therefore an institutional software provider, not a consumer app.
Palantir’s business in plain English
Palantir builds and licenses software for organizations whose information, decisions, and operations are spread across many systems. A manufacturer might have separate systems for suppliers, production, inventory, maintenance, and orders. A government agency might need to combine reports, sensor feeds, case records, logistics data, and mission plans. Palantir’s platforms are designed to make relevant information usable in a shared operational context, then connect analysis with workflows or actions.
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The company describes its mission in its 2025 Form 10-K this way: “We build software that empowers organizations to effectively integrate their data, decisions, and operations at scale.” The practical meaning is broader than putting charts on a dashboard. Palantir’s software is intended to help people understand what is happening, evaluate options, coordinate work, and in some situations trigger or authorize an operational response.
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Palantir says customers can use the platforms separately or bundle them. A customer might use Foundry as its main data and operations layer, add AIP for controlled generative-AI applications, and rely on Apollo to deliver those capabilities in its own cloud or on-premises environment. A defense organization may use Gotham for mission work while also using other Palantir platforms behind the scenes.
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How the software turns data into operational work
1. Connect information from existing systems
Palantir does not present itself as a replacement for every database an organization already owns. Instead, its platforms are designed to work with information originating in many systems and domains. Those sources can include business applications, operational databases, files, sensors, and other feeds. The goal is to give authorized users a coherent view without requiring every source system to be rebuilt.
Connecting data is only the first step. Different systems may use different names, identifiers, formats, update schedules, or access rules. A useful platform has to preserve those distinctions while making relationships understandable to the people who use the information.
2. Add logic, analysis, and context
Foundry is described by Palantir as supporting data management, logic authoring, analytics, Ontology development, and workflow building. That combination lets a team define how raw records should be interpreted, calculate metrics, investigate patterns, and represent the entities that matter to the organization.
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3. Connect insight to decisions and actions
The intended endpoint is a workflow. A user might review a proposed change, assign a task, update an order, alter a distribution plan, or escalate an exception. Palantir’s platforms can support human review and authorization rather than treating every automated suggestion as an instruction to act. The exact approvals, permissions, and integrations depend on how an organization configures the system.
This is why Palantir often describes its products in terms of data, decisions, and operations together. The software is meant to sit close to the work an organization is trying to perform, not only to provide a separate reporting layer.
The four principal Palantir platforms
Gotham, Foundry, AIP, and Apollo overlap in a broader platform strategy, but they are not four versions of the same product. Their primary jobs and typical settings differ.
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| Platform | Primary job | Main setting or users | Relationship to other platforms |
|---|---|---|---|
| Gotham | Integrate information for situational awareness, operational decisions, and mission coordination | Government, defense, and intelligence organizations | Can be used as a mission-focused platform and may be bundled with other Palantir capabilities |
| Foundry | Data management, logic, analytics, Ontology development, and workflow building | Commercial operations and other organizational teams | Often serves as a data-and-operations foundation; AIP applications can connect to it |
| AIP | Connect generative AI and other AI capabilities to organizational data and processes | Teams building AI-assisted workflows, agents, and automations | Uses organizational context and controls supplied by the surrounding platform environment |
| Apollo | Coordinate software delivery, security changes, and configuration | Deployment and platform teams operating across cloud, on-premises, and other environments | Delivers and maintains Palantir platforms, including its own software |
Gotham: government and defense missions
Palantir says Gotham integrates information from different domains and sensors so users can build situational awareness, make operational decisions, and coordinate missions. Those are the company’s stated purposes; they should not be read as an independent verification that every deployment produces a particular result.
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In practice, Gotham is aimed at environments where information can arrive from many sources and where users need to understand relationships, locations, activities, or resources in a mission context. The intended users are government, defense, and intelligence organizations rather than individual consumers. Access controls, data handling rules, and mission-specific procedures remain the responsibility of the deploying organization.
Foundry: data operations and workflows
Foundry is the platform Palantir describes as foundational for data operations. It provides tools for bringing data together, authoring logic, performing analytics, developing an Ontology, and building workflows across teams and organizational units.
A Foundry implementation can give different departments a shared operational model while allowing each team to work with the views, calculations, and permissions it needs. The value is not simply that more data appears in one place; it is that the organization can define how information relates to decisions and recurring processes.
Foundry can be used on its own, but Palantir also presents it as a foundation for other capabilities. For instance, an AIP application may use the entities, rules, and permissions established in Foundry so that an AI interaction is grounded in the organization’s approved context instead of an unstructured collection of documents.
AIP: generative AI connected to real processes
AIP is Palantir’s generative-AI platform. The company describes it as connecting large language models and other AI capabilities to organizational data and processes. Its tooling includes agents, automations, evaluation, security controls, and human review.
That combination addresses a central enterprise problem: a language model can produce fluent text without knowing which records are authoritative, which actions are allowed, or when a person must approve a result. AIP is designed to place the model inside a governed operational setting. An organization can define what information an agent may access, what tools it may call, how outputs are evaluated, and where human approval is required.
AIP does not mean that Palantir supplies one universal model for every customer or that every task should be automated. The platform can connect to different large language models and AI services. The choice of model, data, permissions, evaluation criteria, and workflow remains a deployment decision.
Apollo: continuous delivery across environments
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 deliver its own platforms.
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This role matters when an organization cannot move all workloads into one public cloud or update every site in the same way. A deployment team may need consistent configuration and controlled updates across a mixture of infrastructure. Apollo is concerned with delivering and maintaining the software; it is not the data model or business workflow layer itself.
What Palantir means by the Ontology
“Ontology” is Palantir’s term for a structured representation of an organization’s data, logic, and actions. It is more specific than using “database” as a synonym. A database stores records; an Ontology is intended to model the meaningful objects an organization works with, the relationships among them, the rules that describe them, and the actions users may take.
Objects and relationships
An Ontology might represent a plant, production line, machine, supplier, customer, product, shipment, or order. It can link those objects so that a user can move from an operational event to the resources, commitments, and people affected by it. The model reflects how the organization thinks about its work, not merely how one source system happens to store fields.
Actions and workflows
Palantir’s architecture material uses supply-chain examples such as updating a purchase order or changing a distribution strategy. In an Ontology-based application, those are modeled as actions with the relevant rules and permissions rather than as disconnected edits in an unrelated screen.
That design can make analytics and operations share the same context. A calculation that identifies a supply risk can be connected to a workflow for reviewing suppliers, reallocating inventory, or changing a plan. Whether an action is automatic, suggested, or subject to multiple approvals is a configuration and governance choice.
Why the distinction matters
Without a shared operational model, teams can disagree about which record represents an order, how a customer is identified, or which inventory figure is current. An Ontology is intended to provide a common layer for those meanings. It does not eliminate the need to maintain source systems, data quality, security, or organizational agreement about definitions.
How the platforms fit together
A simple way to picture the relationship is:
- Foundry organizes data, logic, entities, and workflows for organizational operations.
- Gotham applies a similar integration-and-operations idea to government, defense, and intelligence missions.
- AIP adds governed generative-AI interactions, agents, and automations to data and processes.
- Apollo delivers and updates the software across the environments where it must run.
These are complementary roles, not consumer alternatives that a person chooses like competing mobile apps. A customer may buy one platform, combine several, or use different capabilities in different parts of the organization. Palantir says its platforms can be used separately or bundled.
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Who uses Palantir?
Palantir reports working with government and commercial institutions. Its examples include utilities, automotive manufacturing, oil and gas, pharmaceuticals, supply chains, public health, and military organizations. The common characteristic is not a particular industry label; it is a complicated operating environment where data, decisions, and actions need to be coordinated across teams or systems.
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Government use can involve defense, intelligence, public health, or other missions that require controlled information and specialized operational processes. Commercial use can involve production planning, logistics, maintenance, inventory, customer commitments, or other activities where delays and decisions have financial or service consequences.
Palantir’s target opportunities are generally large and complex. The company describes them as having challenging data environments, high installation costs, and long sales cycles. That profile helps explain why the product is normally obtained through organizational procurement and implementation work rather than downloaded by an individual for personal use.
Palantir’s customer and revenue figures for 2025
The following figures come from Palantir Technologies Inc.’s 2025 Form 10-K. They describe the company’s reported position for the year ended December 31, 2025, not a forecast and not an independent market estimate.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Measure | Reported figure | Important qualification |
|---|---|---|
| Customers | 954 as of December 31, 2025 | Palantir counts separately contracted and invoiced divisions or agencies as distinct customers, so the number does not necessarily equal 954 parent companies. |
| 2025 revenue | $4.5 billion | Palantir-reported revenue for 2025. |
| Revenue from government customers | 54% of 2025 revenue | Company-reported mix for 2025. |
| Revenue from commercial customers | 46% of 2025 revenue | Company-reported mix for 2025. |
| Revenue from U.S. customers | 74% of 2025 revenue | Company-reported geographic mix for 2025. |
| Revenue from customers outside the United States | 26% of 2025 revenue | Company-reported geographic mix for 2025. |
The customer count needs careful reading. A separately contracted or invoiced division can be counted on its own, so “954 customers” is not a claim that Palantir has 954 unrelated corporate parent groups. Likewise, the revenue percentages describe where 2025 revenue came from; they do not indicate that every contract has the same duration, pricing, product mix, or renewal terms.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What buying and deploying Palantir usually involves
Organizational evaluation
A prospective customer first has to identify a problem that crosses data and operational boundaries. Examples include supply disruptions that affect production and shipping, mission information spread across multiple systems, or a need to introduce AI into a process with strict permissions and review.
The organization then needs to identify data owners, security requirements, users, existing systems, and the actions the software should support. A dashboard that only displays information may require less change than a workflow that can update records or initiate an operational task.
Integration and modeling
Implementation involves connecting relevant sources, resolving identities and definitions, setting permissions, and building the logic or Ontology needed for the use case. The work is specific to the organization: a production network, hospital system, utility, and defense mission do not share the same entities or rules.
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Teams decide which users can view data, which users can approve actions, which actions are automated, and how exceptions are handled. For AIP projects, they also need to choose models or AI services, define evaluation tests, constrain tool access, and establish human-review points.
Deployment and ongoing updates
The resulting software has to run where the organization permits it to run. Apollo is designed to coordinate delivery and configuration across cloud, on-premises, and other environments. Ongoing work includes security updates, changes to source systems, new workflows, and revisions to permissions or operating rules.
These activities help explain the company’s reference to high installation costs and long sales cycles. A large deployment is an organizational change project as well as a software purchase. The time and cost depend on the number of systems, locations, users, security constraints, and workflows involved.
What Palantir is—and is not
It is enterprise and government infrastructure
Palantir’s customers pay to use software platforms built for institutional work. The products are aimed at organizations with complex data and operational requirements, and their use normally involves procurement, implementation, access governance, and ongoing administration.
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An individual cannot reasonably evaluate Palantir the way they would a note-taking app, consumer chatbot, or spreadsheet subscription. The relevant questions are organizational: Which data sources must be connected? Which decisions should be supported? Who may take an action? Where must the system run? How will the organization validate results?
It is not simply a database or a dashboard
The platforms can work with databases and present analytics, but Palantir’s stated proposition includes logic, operational models, workflows, AI-assisted actions, and software delivery. Calling the entire offering “a database” misses the Ontology and workflow layers; calling it “a dashboard” misses the operational actions and deployment infrastructure.
It is not an automatic guarantee of better decisions
Palantir describes what its platforms are designed to support. The quality of an outcome still depends on source data, model definitions, permissions, user training, process design, and the judgment of the organization using the system. Company descriptions of mission or business benefits should be understood as stated purposes, not independent proof that every customer achieves the same result.
A concrete example: a supply-chain disruption
Imagine a manufacturer learns that a supplier will deliver a critical component late. In disconnected systems, the purchasing team may see the supplier notice, production may see a potential stoppage, logistics may see an affected shipment, and sales may see only a customer promise date.
- Data connection: Relevant supplier, inventory, production, order, and logistics records are made available in the operational environment.
- Ontology context: The component, supplier, plant, production line, affected orders, and shipment commitments are represented as related objects rather than isolated records.
- Analysis: Logic identifies which production schedules and customer commitments are exposed to the delay and what inventory or alternate routes are available.
- Workflow: The system presents options such as reallocating inventory, changing a distribution strategy, or updating a purchase order. Authorized users review the implications.
- Action and audit: A user approves the chosen action, the relevant source or operational system is updated through the configured workflow, and the organization can track what changed and who approved it.
- AI assistance, if used: AIP could help summarize the situation, answer questions about the affected objects, or propose a workflow step within the permissions and review rules the organization configured.
This example illustrates the intended architecture; it is not a claim that every Palantir customer uses exactly these data sources, actions, or approval steps.
Questions to ask when evaluating a Palantir deployment
- What decision or workflow needs improvement? A clearly defined operational problem is easier to measure than a general goal to “use AI” or “integrate data.”
- Which systems contain the authoritative records? Integration should preserve ownership and update rules rather than create competing versions of the truth.
- Which entities and relationships must be modeled? This determines the scope of the Ontology and the data-cleaning work required.
- Which actions can the software take? Specify whether an output is informational, a recommendation, or an approved operation that changes another system.
- Where must the software run? Cloud, on-premises, and mixed environments can impose different security, connectivity, and update requirements.
- How will AI outputs be evaluated? Define test cases, error handling, audit records, and human-review requirements before an agent or automation is used in production.
- Who owns governance after launch? Permissions, source changes, model updates, and workflow rules need continuing administration.
The bottom line for beginners
Palantir sells software that links an organization’s data with its decisions and operational processes. Foundry is the general data-and-workflow foundation, Gotham focuses on government and defense missions, AIP brings governed generative AI into organizational processes, and Apollo handles delivery and updates across infrastructure environments. The Ontology is the connective model of entities, relationships, logic, and actions that makes those operations understandable in context.
Its reported 954 customers, $4.5 billion in 2025 revenue, and government-commercial mix show a business serving institutions rather than individual users. The most accurate way to think about Palantir is as a platform provider for complex, high-consequence work: useful when an organization must connect many systems and workflows, but dependent on careful implementation, data quality, security controls, and human governance.
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