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These are not four consumer apps that you buy separately and use in isolation. Palantir presents them as a connected architecture: Foundry organizes data, logic, and actions; AIP connects approved AI models to that operational context; Apollo manages deployment across the required infrastructure; and Gotham is a distinct platform in the company’s government-focused product family.
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What Palantir does in one sentence
Palantir supplies software for organizations that need to combine information from many systems, control who can use it, analyze changing conditions, and turn a decision into an executable business or mission workflow. The intended users are organizations such as manufacturers, airlines, healthcare providers, financial institutions, utilities, telecommunications companies, pharmaceutical companies, and government agencies, rather than individual consumers. Palantir describes these uses and sectors in its security documentation.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The practical distinction from a conventional reporting system is that Palantir’s platform is designed to connect data with business logic and actions. A team might use it to understand inventory, decide how to allocate it, trigger a replenishment process, and record the result in one connected operating environment. The exact application, data sources, permissions, AI models, and actions depend on the customer’s deployment.
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The four principal Palantir platforms
Palantir’s FY2025 Form 10-K identifies Gotham, Foundry, Apollo, and AIP as its four principal platforms. Palantir’s architecture documentation describes the relationship among Foundry, AIP, and Apollo; Gotham has a separate place in the company’s mission-oriented portfolio.
| Platform | Primary job | What it gives an organization |
|---|---|---|
| Foundry | Data operations, Ontology development, analytics, application building, and workflow development | A shared environment in which data, logic, permissions, lineage, applications, and operational actions can be connected |
| AIP | Generative-AI development and deployment in an enterprise context | Tools for connecting models and creating AI-backed functions, agents, workflows, and applications |
| Apollo | Continuous delivery and deployment management | Infrastructure management and coordinated upgrades for Foundry and AIP services across supported environments |
| Gotham | Mission-oriented operations, especially in government and defense contexts | A principal Palantir platform for organizations working with complex operational and national-security missions |
The table describes each platform by the job Palantir assigns it; it is not a neutral market ranking or a feature-by-feature comparison with other vendors. Palantir’s own overview of the platform family is available at AIP, Foundry, and Apollo, and the four-platform description appears in the company’s FY2025 Form 10-K.
What Palantir Foundry is
Foundry is the data and operational-workflow layer. Palantir describes it as providing data management, logic authoring, Ontology development, analytics, and workflow development. It also supplies application-building and developer tools on a shared foundation of data, compute, security, and lineage. The company’s platform overview explains the broad capabilities, while its app-building documentation covers application development.
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Organizations commonly store operational information in many systems: enterprise resource planning, customer, logistics, maintenance, laboratory, financial, or government systems. Foundry’s role is to make that information usable in a common operating context. The useful output is not simply a larger data lake. It is a governed set of data products and models that applications and workflows can use.
Foundry also emphasizes lineage: users can trace where information came from and how it was transformed. That matters when an analyst, operator, auditor, or manager needs to understand why a dashboard value or recommendation changed. Lineage does not by itself guarantee that source data is correct; it provides visibility into its origin and processing.
The Ontology: data plus meaning and action
Palantir uses the term Ontology for a model of an organization’s important objects, relationships, rules, and actions. In the company’s FY2025 filing, the Ontology is described as going beyond a traditional data representation by integrating the elements of a decision—data, logic, and actions—and allowing users to build interconnected workflows.
In plain language, a traditional database might contain rows for orders, products, locations, and shipments. An Ontology-based application can represent those concepts as operational objects with relationships and permitted actions. A planner can then inspect an order, see the relevant inventory and transport constraints, evaluate logic, and initiate an approved action without manually stitching together unrelated tables.
This is Palantir’s description of its own software model, not a universally accepted industry definition of “ontology.” The value of the approach depends on how accurately the customer models its processes, how well source systems are connected, and whether permissions and business rules are maintained as operations change.
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Analytics, applications, and workflows
Foundry supports analytics for exploring trends and exceptions, but its stated scope extends to building applications and workflows. A team can create an interface for a specific operating group, expose the relevant Ontology objects, apply business logic, and provide an action such as approving, rerouting, scheduling, or escalating a case.
Palantir’s developer documentation identifies package routes for both TypeScript and Python through the Ontology SDK. That gives software teams a way to build custom applications against the operational model rather than treating Foundry as a collection of static reports. The precise APIs and available features depend on the customer environment and the version of the platform.
What Palantir AIP is
AIP is Palantir’s generative-AI platform. Its documented purpose is to connect AI models with an organization’s data and operations and to provide tools for building AI-backed functions, agents, workflows, and applications. The relevant architecture description is in Palantir’s platform documentation.
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How AIP differs from a general chatbot
A general chatbot usually answers from the text or files supplied to it. AIP is intended to place a model inside a governed operational context. Through Foundry’s data and Ontology layers, an AI application can be given access to selected business concepts, rules, and actions. That can support tasks such as summarizing a situation, proposing a plan, calling an approved function, or routing work to a person.
The important word is approved. Connecting a model to enterprise operations does not mean allowing it unrestricted access to every data source or the ability to execute every action. Permissions, application design, review steps, and customer policies determine what an AI function or agent can see and do.
Models, functions, agents, and workflows
Palantir’s materials describe several ways to use AIP:
- Model connections: connect supported generative-AI models to an enterprise environment.
- AI-backed functions: create reusable operations that a model can call within an application.
- Agents: build software that can interpret a goal, use permitted tools, and progress through a task.
- AI workflows and applications: put model output into a larger process with data, human review, and operational actions.
These categories describe product capabilities, not a promise that every customer receives every model, agent feature, or integration. Palantir’s AIP materials state that availability can change or differ by customer, deployment, and access arrangement.
Human oversight and model risk
An AI-generated recommendation can be useful without being authoritative. Organizations still need to decide when a person must review an answer, which actions require approval, how prompts and outputs are logged, and how incorrect or stale information is corrected. AIP can provide the technical connection between a model and an operational workflow; it does not remove the customer’s responsibility for policies, validation, or legal compliance.
What Apollo does
Apollo is Palantir’s continuous-delivery platform. Palantir describes it as managing the infrastructure for Foundry and AIP services and orchestrating upgrades. In practical terms, Apollo is the delivery and deployment layer behind the applications rather than the place where an analyst normally performs day-to-day analysis.
Why a delivery layer matters
Enterprise and government deployments can run across different networks, cloud environments, data centers, or restricted settings. Updating software in those environments is more complicated than publishing a single web release. A delivery platform must coordinate versions, infrastructure, dependencies, and service health while respecting the customer’s security and operating constraints.
Apollo’s documented role is to manage that delivery process for Palantir services. It does not replace Foundry’s data modeling or AIP’s AI-development functions; it helps those services reach and remain operational in the environments where the customer uses them.
Where Gotham fits
Gotham is one of Palantir’s four principal platforms and is associated with government, defense, intelligence, and other mission-oriented operations. Foundry’s documented role is broad data operations and workflow development across commercial and government settings; Gotham belongs to Palantir’s mission-oriented product family and should not be casually described as another name for Foundry.
The public materials used here do not provide a complete, neutral feature matrix that would let a reader determine every difference between Gotham and Foundry. The safest distinction is by operating context: Gotham is a principal platform for complex missions, while Foundry is described as the data operations foundation used to model data, logic, and workflows.
How the platforms work together
Palantir presents Foundry, AIP, and Apollo as an integrated trio. A simplified implementation sequence looks like this:
- Connect and govern source data in Foundry. The organization identifies relevant systems, establishes access controls, and makes data available with lineage and security rules.
- Model operational concepts in the Ontology. Data is organized around objects, relationships, logic, and permitted actions that reflect the customer’s real process.
- Build an application or workflow. Analysts, operators, and developers use Foundry tools or the Ontology SDK to create interfaces, calculations, alerts, and actions.
- Add AI where it is useful. AIP connects an approved model and exposes selected functions, agents, or workflow steps. The application can constrain what the model can access and when a human must approve an action.
- Deploy and update the services. Apollo manages delivery and upgrades for the Foundry and AIP services in the customer’s supported environments.
- Monitor, audit, and improve. Teams review outcomes, permissions, lineage, model behavior, and workflow performance, then revise the Ontology or applications as the operation changes.
This sequence is a reader-friendly explanation of the roles Palantir assigns to its platforms. Actual architecture, integrations, network placement, and operating procedures are customer-specific.
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Who uses Palantir software
Palantir’s security documentation says its software is used by healthcare providers, financial institutions, utility providers, manufacturers, telecommunications companies, airlines, and pharmaceutical companies. Its materials also describe work with government organizations. These are vendor-documented sectors, not a claim that every organization in each sector uses every Palantir platform.
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| Operating setting | Problems the platform family can be configured to address | Data sensitivity Palantir says it supports |
|---|---|---|
| Manufacturing and supply chains | Coordinate production, inventory, suppliers, transport, and exception handling | Operational and commercial data |
| Healthcare and pharmaceuticals | Connect clinical, operational, research, or supply information under controlled access | Personal information and protected health information |
| Financial services and utilities | Analyze transactions, assets, demand, incidents, and regulatory processes | Financial data and other sensitive records |
| Airlines and telecommunications | Manage fleets, networks, schedules, maintenance, and disruptions | Operational, customer, and infrastructure data |
| Government and mission operations | Coordinate intelligence, logistics, planning, and other mission workflows | Controlled unclassified information and classified government data, where the deployment is approved for it |
The sensitive-data categories in the final column come from Palantir’s security overview. They describe intended deployment contexts and vendor-documented capabilities, not an independent security certification or a guarantee that a particular customer environment meets every regulatory requirement.
Security, permissions, and oversight
Palantir’s materials describe access controls, lineage, auditability, and security features for sensitive work. In a real deployment, those controls should be evaluated against the organization’s own identity system, data classification rules, retention requirements, network design, incident procedures, and regulatory obligations.
Access control
Permissions determine which users, applications, agents, and functions can access particular data or perform actions. A well-designed workflow should grant the minimum access needed for the task and separate proposal from execution when an action has material consequences.
Lineage and auditability
Lineage helps trace how a data value was sourced and transformed. Audit records can help show who viewed information, changed a workflow, approved an action, or interacted with an AI-assisted process. The existence of a feature does not guarantee that an organization has configured it correctly or retained the records it needs.
AI-specific controls
Teams adding AIP should define which models are allowed, how sensitive data is handled in prompts and outputs, what constitutes a valid answer, how hallucinations are detected, and which steps require human approval. Those controls are governance decisions around the platform, not automatic properties of a language model.
Customer examples: useful evidence with limits
Palantir’s corporate homepage publishes statements from named customer representatives. They illustrate reported deployments and outcomes, but they are company-published testimonials rather than independent audits. The figures below must not be combined into a general return-on-investment claim.
| Customer statement published by Palantir | What was reported | How to interpret it |
|---|---|---|
| Walgreens VP of Product Pharmacy Jeff Hoffman | The organization initially planned to pilot 10 stores in about six months; Hoffman said that, using Foundry and AIP, it reached about 4,000 stores within eight months. | A reported deployment-scaling timeline for that customer, not a forecast for other retailers. |
| General Mills | About $40,000 in savings per day was reported. | A customer-reported savings figure with no basis here for comparing it with other organizations. |
| Sompo | A $60 million improvement in profit over three years was reported. | A company-published customer outcome; the surrounding business conditions and calculation method are not independently established here. |
| Fujitsu | An annual cost reduction of $9 million within three months was reported. | A reported annualized reduction and timeline for that deployment, not a universal result. |
All four examples appear in statements on Palantir’s corporate homepage. They are best used to understand the kinds of outcomes customers say they pursued, not as independently verified benchmarks.
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Foundry applications and developer tools use shared services for data, compute, security, and lineage. That common foundation is intended to let an application use the same governed operational objects and data relationships as analytics and workflows.
Best Value
Palantir’s app-building documentation describes both no-code or low-code-style application construction and custom development options. The Ontology SDK documentation identifies TypeScript and Python package routes, which are relevant for teams that need software-engineered interfaces or integrations. The specific package versions, APIs, permissions, and deployment constraints are determined by the customer’s environment.
A development team evaluating the platform should therefore ask practical questions rather than only asking whether it has dashboards or an AI model:
- Which source systems can be connected, and how often will data update?
- Can the organization represent its important objects, relationships, rules, and actions clearly in the Ontology?
- Which users and services can read or change each object?
- Can existing TypeScript or Python teams use the available SDK routes?
- How are application changes tested, approved, deployed, rolled back, and audited?
- Which AI models and functions are available in the intended customer environment?
Cost, availability, and buying reality
Palantir’s public product documentation does not provide a general price list in the material covered here. These are enterprise platforms sold and deployed for specific organizations, data environments, security requirements, and operating scopes. A meaningful cost estimate would require details such as the number of users, data volume, integrations, required environments, support terms, security controls, and the applications to be built.
Feature availability can also vary by customer and deployment. A page describing an AIP capability should not be read as proof that every customer can immediately use every model, agent, connector, or workflow feature. Prospective buyers need a deployment-specific statement of scope, supported environments, service levels, data handling, and upgrade responsibilities.
What Palantir does not establish by itself
- It is not a consumer productivity suite. The platforms are designed for organizational operations and mission work, not ordinary personal file storage or household automation.
- It is not simply a database. Foundry’s stated scope includes data management, logic, Ontology objects, applications, and actions.
- It is not an automatic AI decision-maker. AIP supplies tools for connecting models to approved data and workflows; customer controls determine what an AI system can do.
- It is not a guaranteed business result. Reported customer savings, profit changes, and deployment speeds depend on each organization’s starting systems, process redesign, adoption, and measurement.
- It is not independently certified by the descriptions alone. Vendor-documented security features must still be assessed against the customer’s own legal, regulatory, and technical requirements.
- It is not possible to infer a neutral competitive ranking from these product pages. The available documentation explains Palantir’s architecture but does not provide a comprehensive independent comparison with competing platforms.
Palantir’s size versus platform effectiveness
Palantir reported $4.5 billion in revenue for the year ended December 31, 2025, in its FY2025 Form 10-K. That is a company financial result, not a measure of how effective Foundry, AIP, Apollo, or Gotham will be for a particular customer. Financial scale can indicate that the company operates at substantial commercial scale, but it cannot substitute for a technical evaluation, pilot, security review, or outcome measurement.
How to evaluate a Palantir deployment
An organization considering the platform family should start with one operational problem that has a clear owner and measurable baseline. The following checklist keeps the evaluation focused:
- Define the decision. Identify the decision or workflow to improve, the people responsible for it, and the current time, error, cost, or service-level baseline.
- Inventory the data. List source systems, owners, refresh rates, quality issues, classifications, and retention requirements.
- Map the Ontology. Specify the objects, relationships, rules, and actions the workflow needs. Include exceptions, not only the normal path.
- Set permissions and approvals. Decide which users and services can view, propose, approve, or execute each action.
- Choose the smallest useful application. Build a workflow that can be tested end to end instead of starting with an unfocused enterprise-wide rollout.
- Test AI separately from process value. Measure answer accuracy, refusal behavior, latency, and human-review burden before allowing an agent to trigger consequential actions.
- Plan delivery and rollback. Confirm how Apollo-managed updates will be tested, scheduled, monitored, and reversed in every target environment.
- Measure the result independently. Compare the deployment with the baseline using agreed definitions, and distinguish observed results from projections or testimonials.
This approach applies whether the project uses Foundry alone, adds AIP, depends on Gotham’s mission-oriented capabilities, or requires Apollo to manage a complex deployment. It also makes clear which benefits come from better data and process design and which, if any, come from AI assistance.
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