30-Second Explanation
Palantir is often described as a data analytics company.
That is incomplete.
Its current software architecture is designed to move through several layers:
DATA
databases · ERP · files · sensors · operational systems
↓
FOUNDRY
integration · transformation · governance · analytics
↓
ONTOLOGY
objects · relationships · business logic · permissions · actions
↓
AIP / APPLICATIONS
AI agents · workflows · operational software
↓
HUMAN / MACHINE DECISION
↓
REAL-WORLD ACTIONApollo sits underneath this stack and continuously deploys Palantir software across:
- public cloud;
- private cloud;
- on-premises infrastructure;
- edge environments;
- disconnected or air-gapped systems. [S4][S13]
Gotham applies the same broad architecture to defense and intelligence missions.
In 2025 Palantir generated approximately $4.48 billion in revenue from 954 customers. Government customers represented 54% of revenue and commercial customers 46%. [S1]
By Q2 2026, quarterly revenue reached approximately $1.94 billion, while both U.S. commercial and U.S. government revenue grew rapidly. [S2][S3]
The simplest commercial model is:
institution has fragmented data + operational problem
↓
Palantir deploys platform
↓
customer maps data and operations into software
↓
users run workflows / decisions through the platform
↓
customer pays Palantir under multi-period software contractsHow Palantir Actually Works
Palantir does not create the customer's underlying operational data.
It connects to systems the customer already uses.
A simplified implementation looks like:
The software is therefore not only a place to inspect information.
Its intended role is to connect information to decisions and actions.
That is why Palantir increasingly describes Foundry + AIP + Apollo as an Enterprise Operating System. [S4]
What Palantir Does
Integrates fragmented enterprise data
Large institutions rarely have one clean database.
They have:
- ERP;
- CRM;
- data warehouses;
- operational databases;
- spreadsheets;
- documents;
- sensors;
- APIs;
- classified or regulated systems.
Foundry connects these systems without requiring the customer to replace them all first.
Creates an operational model of the organization
Palantir Ontology maps raw data into business objects and actions.
Instead of only:
table: orders
table: factories
table: suppliersthe software can represent:
Factory
Order
Supplier
Machine
Shipmentwith relationships and permitted actions.
This converts technical data structures into a model closer to how operators understand the organization.
Builds operational applications
Palantir Foundry supports:
- analytics;
- workflows;
- application development;
- data engineering;
- decision support.
The goal is not only to explain what happened.
It is to help users decide what to do next.
Connects AI to operational context
Palantir AIP connects large language models and other AI capabilities to the customer's governed data and Ontology.
This gives an AI system context about:
- what objects exist;
- which data a user can see;
- which actions are allowed;
- which business rules apply.
AIP is therefore not only a chatbot layer.
It is designed to put AI inside operational workflows.
Deploys software across difficult environments
Palantir Apollo manages software delivery and updates across different infrastructure.
That matters because Palantir customers may operate:
- normal public cloud;
- private cloud;
- regulated environments;
- edge infrastructure;
- disconnected networks.
Software that only works in one cloud environment would not fit all of Palantir's target customers.
Supports defense and intelligence operations
Palantir Gotham is designed for defense, intelligence and national-security environments.
It integrates sensitive data while preserving access controls and supports operational workflows.
Palantir says its roots were in helping intelligence organizations make better use of fragmented data securely. [S15]
Products, Platforms & Services
Gotham
Palantir Gotham is Palantir's defense and intelligence platform.
Why it exists
Government missions often require users to combine fragmented, sensitive and differently classified information.
Where it sits
Mission software built on Palantir's wider architecture.
Who uses it
Defense and intelligence personnel and related mission teams.
Foundry
Palantir Foundry is the foundational data-operations platform.
Why it exists
Enterprises need a common environment where data can become governed operational software.
Where it sits
Core data, analytics, Ontology and workflow layer.
AIP
Palantir AIP is the generative-AI platform.
Why it exists
A large language model has limited enterprise value if it cannot safely access the right context or take governed actions.
Where it sits
AI layer above Foundry and Ontology.
Apollo
Palantir Apollo is the software deployment and Day 2 operations platform.
Why it exists
Mission-critical software must be updated continuously across many infrastructure environments.
Where it sits
Deployment layer underneath Palantir's wider software stack.
Ontology
Palantir Ontology is the operational representation that connects enterprise data, logic, security and actions.
Why it exists
Users and AI agents need to operate on business concepts rather than raw tables alone.
Where it sits
The central semantic and operational layer inside Foundry / AIP.
The Palantir Stack
Gotham uses the same broad platform architecture with specialized mission applications.
This stack explains why Palantir is difficult to compare with one narrow software category.
It touches:
- data integration;
- analytics;
- enterprise application development;
- AI;
- workflow;
- deployment;
- operations.
Who Uses Palantir?
The buyer and the user are often different people.
Government organizations
Government customers can include:
- defense organizations;
- intelligence organizations;
- public-sector agencies.
The institution signs the contract.
Actual users can include analysts, planners and frontline operators.
Commercial enterprises
Commercial customers span many industries.
Palantir's current documentation cites use cases in:
- manufacturing;
- healthcare;
- energy;
- aviation;
- utilities;
- insurance;
- pharmaceuticals;
- supply chain.
The customer is the enterprise.
The user may be:
- engineer;
- operations manager;
- data scientist;
- technician;
- executive;
- frontline worker.
Developers and builders
Palantir increasingly exposes:
- developer tools;
- SDKs;
- application-building environments;
- AI workflow tools.
The platform therefore also targets internal software builders, not only end users.
Who Pays Palantir?
Palantir is paid by institutions.
The payer is usually:
government agency
or
commercial enterprisenot the individual software user.
Palantir says customers generally enter contracts ranging from one to five years, although shorter arrangements also occur. Revenue is generally recognized over the contract term. [S1]
Government customers
Government revenue represented 54% of 2025 revenue. [S1]
Government procurement can involve:
- awarded contracts;
- contract options;
- budget approvals;
- renewal;
- security requirements.
Government contracts may also include termination-for-convenience provisions.
How Palantir Makes Money
Palantir's accounting model is software revenue, not consulting billable hours.
The company says customers pay to use its software platforms.
Revenue can come from:
Palantir Cloud subscription
+
On-Premises Software subscription / term license
+
ongoing operations & maintenance
+
professional servicesPalantir Cloud
The customer gets continuing access to software in a Palantir-controlled hosted environment.
Revenue is generally recognized ratably over the contract period. [S1]
On-premises / customer environment
The customer receives a term license to run software on internal infrastructure or its own cloud environment, together with ongoing maintenance and updates. [S1]
Professional services
Palantir also provides professional services.
But the strategic objective is to create reusable software platforms rather than sell only custom consulting.
The Forward-Deployed Model
Palantir's software is intentionally close to the customer's operating problem.
Forward Deployed Engineers work with customers in real environments and feed lessons back into the core platform. [S6]
The mechanism is:
Palantir engineer works with customer
↓
real operational problem becomes visible
↓
solution is built using common platform
↓
product feedback returns to core engineering
↓
platform improves
↓
next customer can reuse more capabilityThis model sits between conventional packaged software and pure custom consulting.
The long-term economic question is:
How much customer-specific work becomes reusable platform capability?
That is central to Palantir's scalability.
AIP Bootcamps and Customer Acquisition
AIP changed Palantir's go-to-market process.
Instead of beginning every engagement with a long abstract software evaluation, AIP Bootcamps put customer users and Palantir engineers together to build real use cases quickly. [S10][S11]
The flow becomes:
prospective customer
↓
hands-on Bootcamp
↓
working operational use case
↓
internal customer proof
↓
contract / expansionPalantir says it may conduct pilots and Bootcamps at its own expense without guaranteed future returns. [S2]
That means the acquisition model intentionally spends resources upfront to shorten the distance between product demonstration and operational value.
What Palantir Depends On
Customer data access
Palantir's software is only useful when it can access the customer's relevant systems.
The company does not need to own customer data.
It needs authorized integration.
Customer organizational adoption
A technically successful deployment can still fail if users do not change workflows.
Palantir therefore depends on:
- executive sponsorship;
- operator adoption;
- data access;
- process change.
This is one reason Forward Deployed Engineering matters.
Skilled engineers
Complex implementations require people who understand:
- software;
- data;
- customer operations;
- security;
- deployment.
Palantir's filings identify personnel deployment as important to customer success. [S1]
Cloud and infrastructure providers
Palantir can run across several environments, but hosted services still depend on third-party infrastructure.
The 2025 10-K specifically identifies AWS, Microsoft Azure and other providers as dependencies. [S1]
Apollo reduces dependence on any one deployment environment.
It does not eliminate the need for infrastructure.
AI model providers
AIP can connect to multiple commercial and open-source LLMs.
This reduces dependence on a single foundation-model vendor.
But AI workflows still depend on:
- model availability;
- performance;
- cost;
- governance.
Government budgets and procurement
Government contracts depend on:
- appropriations;
- procurement process;
- contract awards;
- compliance.
This can lengthen sales cycles and change timing.
Customer trust
Palantir works with sensitive operational information.
Security, privacy and access-control failures could damage adoption.
Trust is therefore a commercial dependency, not only a compliance issue.
Who Depends on Palantir?
This does not mean customers have no alternatives.
It means Palantir can become embedded in important operating workflows.
Government operators
A deployed Gotham environment can connect intelligence and operational workflows that users rely on in ongoing missions.
Commercial operators
A Foundry / AIP deployment can become the operating layer for:
- supply planning;
- manufacturing;
- maintenance;
- healthcare operations;
- energy operations.
Internal developers
Organizations can build software on the Ontology and Palantir developer toolchain.
Once custom applications depend on that operational model, the platform becomes more deeply embedded.
AI agents
AIP agents can depend on Ontology context, permissions and actions rather than operating directly on raw data.
That increases the strategic importance of the platform's semantic layer.
Company Relationship Map
Government customers
government institution → Palantir
Type: software customer
Scope: mission / operational softwareThe institution buys software access and related services.
Commercial enterprises
enterprise → Palantir
Type: software customer
Scope: data + operations + AICommercial deployments can expand across multiple functions after an initial use case.
AWS / Microsoft Azure / cloud providers
cloud infrastructure → Palantir
Type: infrastructure provider
Direction: upstream
Scope: hosted software environmentsPalantir can also deploy outside these providers.
Foundation-model providers
LLM provider ↔ Palantir AIP
Type: integrates-with
Scope: AI model accessAIP's value proposition is that the customer can connect models to governed enterprise context without making the model the system of record.
Customer systems
ERP / CRM / database / data lake ↔ Foundry
Type: system integration
Scope: enterprise dataPalantir is often layered on top of existing systems rather than replacing them all.
Competition by Business Layer
Palantir does not have one simple competitor list.
Data integration and analytics
Competition includes:
- data platforms;
- analytics suites;
- internal data-engineering teams.
The question is whether data can move from storage into operational workflows.
Enterprise application platforms
Foundry competes for software-building and workflow territory with:
- enterprise SaaS;
- low-code / application platforms;
- custom internal software.
AI platforms
AIP competes with:
- cloud AI platforms;
- AI application platforms;
- internal LLM stacks;
- model-provider enterprise tools.
Palantir differentiates by tying AI to Ontology, permissions and operations.
Software deployment
Apollo competes with:
- DevOps platforms;
- cloud-native deployment tools;
- internal platform engineering.
Its distinctive use case is heterogeneous and difficult deployment environments.
Government mission software
Gotham competes with:
- defense software vendors;
- systems integrators;
- bespoke government software;
- internal government development.
Consulting / system integration
Palantir's Forward Deployed model overlaps with work a consulting or integration firm might do.
The difference is that Palantir's goal is to solve those problems through a reusable software platform.
History
2003 — Palantir is founded
Palantir was founded in 2003.
Current governance filings identify Alex Karp, Stephen Cohen and Peter Thiel as co-founders. [S17]
The company initially focused on software for intelligence and counterterrorism work.
2005 — Karp becomes CEO
2000s — Gotham develops around government missions
Palantir's early software focused on integrating fragmented information while preserving permissions.
The core problem was:
many sensitive data sources
+
many analysts / operators
+
strict access rules2010s — Commercial expansion and Foundry
Palantir expanded the same broad software principles into commercial institutions.
Foundry became the platform for enterprise data operations, analytics and workflows.
The customer problem changed from intelligence analysis to a much wider range of operational systems.
2010s — Apollo emerges from deployment complexity
Palantir needed to continuously deliver software into environments far more complex than ordinary SaaS.
Apollo turned that internal capability into a platform.
2020 — Direct Listing
Palantir became a public company through a direct listing.
Its public-market structure included multiple classes of common stock and founder voting arrangements. [S18]
2023 — AIP launches
Palantir introduced Artificial Intelligence Platform in 2023 as generative AI moved rapidly into enterprise software. [S9]
AIP connected LLMs to Palantir's existing data, governance and Ontology architecture.
2023–2024 — AIP Bootcamps change go-to-market
2024 — Nasdaq listing
Palantir's Class A common stock moved to Nasdaq.
The listing change did not alter the underlying business model.
2025 — Revenue reaches $4.48B and customers reach 954
Government represented 54% of revenue and commercial 46%. [S1]
2026 — U.S. commercial and government growth accelerate
Q2 2026 revenue reached $1.935 billion.
U.S. commercial revenue was $764 million and U.S. government revenue $809 million. [S3]
How Palantir Changed
| Dimension | Early Palantir | Palantir in 2026 |
|---|---|---|
| Core identity | Intelligence data software | Enterprise / government operating software + AI |
| Primary customer | Intelligence / government | Government + commercial enterprises |
| Product center | Gotham | Gotham + Foundry + AIP + Apollo |
| Data role | Integrate sensitive sources | Data + Ontology + AI + actions |
| Deployment | Mission-specific environments | Cloud + on-prem + edge + disconnected |
| User | Analyst | Analyst + operator + engineer + frontline worker + AI agent |
| AI | Models / analytics | LLMs, agents and automations through AIP |
| Go-to-market | Deep deployments | Deep deployments + rapid Bootcamps |
| Geography | U.S.-centered | Global, increasingly U.S.-weighted revenue |
| Revenue | Small number of large relationships | 954 customers and $4.48B revenue in 2025 |
The core architecture changed less than the market around it.
Palantir kept returning to the same problem:
fragmented information
↓
governed operational model
↓
decision
↓
actionThe difference is that the stack expanded from analytics into application building and AI.
Turning Points
Turning Point 1 — Government-first deployment
What changed
Palantir started with difficult intelligence use cases rather than broad horizontal enterprise software.
Why it mattered
The company learned to build for:
- sensitive data;
- complex permissions;
- high-stakes operations;
- difficult infrastructure.
What it enabled next
A product architecture built for complexity rather than only simple SaaS workflows.
Turning Point 2 — Foundry expands the customer problem
What changed
Palantir moved from intelligence missions into commercial enterprise operations.
Why it mattered
The same integration / operational-model approach could be used for:
- factories;
- supply chains;
- healthcare;
- energy.
What it enabled next
Commercial revenue became a large second engine.
Turning Point 3 — Ontology becomes central
What changed
The platform moved beyond data integration into a common model of business objects, relationships, logic and actions.
Why it mattered
Operational users and AI systems could work with enterprise concepts rather than raw data alone.
What it enabled next
Applications, workflows and later AIP.
Turning Point 4 — Apollo turns deployment into a platform
What changed
Software delivery across cloud, on-premises and disconnected environments became a formal platform capability.
Why it mattered
Palantir could continuously update software in environments ordinary SaaS products may not reach.
What it enabled next
A more scalable platform across government and regulated commercial environments.
Turning Point 5 — AIP connects generative AI to operations
What changed
Palantir added LLM and agent capabilities on top of its existing data / Ontology stack.
Why it mattered
AI could operate with governed enterprise context and actions.
What it enabled next
AI-driven workflows and a new commercial adoption cycle.
Turning Point 6 — Bootcamps compress customer acquisition
What changed
Prospects could build working AI use cases with Palantir engineers in days rather than discuss software abstractly for months.
Why it mattered
Value became easier to demonstrate.
What it enabled next
Faster commercial expansion.
Leadership
Alex Karp
Alex Karp is co-founder, CEO and director.
His business role is associated with:
- government-first focus;
- long-term customer partnerships;
- expansion into commercial markets;
- AIP-era go-to-market;
- company positioning around mission-critical software.
Stephen Cohen
Stephen Cohen is co-founder, President, Secretary and director.
His role is important to understanding Palantir as a product company rather than only a customer-services organization.
Shyam Sankar
Shyam Sankar joined Palantir in 2006 and is currently Chief Technology Officer and Executive Vice President. [S16]
His career inside Palantir has connected:
- product;
- operations;
- forward deployment;
- customer software;
- AIP-era platform development.
What Most People Miss
1. Palantir is not in the business of collecting and selling customer data
Palantir's 10-K says it is not a data broker and builds software for customers to integrate data they are already authorized to use. [S1]
Its business is the software layer.
2. The Ontology is commercially more important than a dashboard
A dashboard explains.
An Ontology can also support:
- actions;
- permissions;
- applications;
- AI agents.
This is why Palantir presents it as the center of the architecture.
3. Palantir's services-heavy deployment model does not make it a pure consulting company
Palantir uses engineers deeply with customers.
But the contractual product remains software access, maintenance and related services.
The scalability question is how customer-specific work feeds reusable platform capability.
4. Government and commercial are not separate technology stacks
5. AIP did not replace Foundry
AIP sits on top of the data and Ontology system.
Without enterprise context, permissions and operational objects, generative AI would be much less useful inside Palantir's target workflows.
6. Apollo is strategically important because Palantir cannot assume normal SaaS deployment conditions
Many customers operate in environments with:
- strict regulation;
- private infrastructure;
- edge systems;
- disconnected networks.
Continuous deployment is therefore part of the product, not only an internal engineering tool.
7. Palantir's 2026 growth is both government and commercial
Q2 2026 U.S. commercial revenue grew faster, but U.S. government revenue was also substantial at $809 million in the quarter. [S3]
The company has two major customer engines rather than a clean transition from one to the other.
Common Misconceptions
"Palantir is a data broker."
No.
Palantir sells software platforms. Its filings explicitly say it is not in the business of collecting, mining or selling customer data. [S1]
"Palantir is only a government contractor."
"Palantir is just dashboards and analytics."
Too narrow.
Foundry / Ontology / AIP are designed to support applications, workflows and actions.
"AIP is Palantir's first AI product."
No.
Palantir used machine learning and analytical models before AIP. AIP specifically extends the platform into generative and agentic AI.
"AIP is a foundation model."
No.
AIP connects third-party and other models to customer context, permissions and workflows.
"Forward Deployed Engineers mean Palantir sells only consulting."
No.
They help deploy and improve software. Palantir's revenue model is primarily software subscriptions / licenses plus maintenance and professional services.
"Gotham and Foundry are completely unrelated products."
No.
Current Palantir architecture increasingly integrates them through shared Foundry / AIP / Apollo infrastructure.
Common Questions
What does Palantir actually sell?
Software platforms that help governments and enterprises integrate data, model operations, build applications and run AI-enabled workflows.
Who are Palantir's customers?
Government institutions and commercial enterprises.
How many customers does Palantir have?
Palantir reported 954 customers at December 31, 2025. [S1]
How does Palantir make money?
Through software access / subscriptions, software licenses, ongoing maintenance and related professional services.
Is Palantir a consulting company?
It has a deployment-heavy model, but its core commercial product is software.
What is the Palantir Ontology?
A governed operational model connecting enterprise objects, relationships, logic, permissions and actions.
What is AIP?
Palantir's Artificial Intelligence Platform, designed to connect AI models to enterprise data and operations.
What is Gotham?
Palantir's defense and intelligence mission platform.
What is Foundry?
The core data-operations and application platform used widely in commercial and government environments.
What is Apollo?
The deployment platform that manages continuous software delivery across infrastructure environments.
What Industry is Palantir in?
The canonical GICS Industry is:
451030 — Software45103010 Application Software is Sub-Industry metadata only.
What Changed Recently
2025 — Revenue reached $4.48B
Palantir generated $4.475 billion of revenue in 2025, up 56% from 2024. [S1]
Why it matters
The platform moved into a substantially larger commercial scale.
2025 — Customer count reached 954
Palantir ended 2025 with 954 customers. [S1]
Why it matters
The company is no longer dependent only on a narrow set of early government relationships.
H1 2026 — U.S. became 80% of revenue
Palantir reported that 80% of first-half 2026 revenue came from U.S. customers. [S2]
Why it matters
Growth is increasingly concentrated in the U.S. even as Palantir remains internationally deployed.
Q2 2026 — Revenue reached $1.935B
Quarterly revenue grew 93% year over year. [S3]
Q2 2026 — U.S. commercial revenue reached $764M
U.S. commercial revenue grew 149% year over year. [S3]
Why it matters
Commercial enterprise software is now a major growth engine rather than only an adjacent business.
Q2 2026 — U.S. government revenue reached $809M
U.S. government revenue grew 90% year over year. [S3]
Why it matters
The original government business is still growing alongside commercial expansion.
2026 — Product positioning shifts toward governed operational AI
Palantir's current product architecture increasingly emphasizes AI systems that keep customer data, operations and decision rights under customer control.
Why it matters
The platform's differentiation is moving from "data integration" toward "governed AI operating system."
Evidence
- Palantir has four principal platforms: Gotham, Foundry, Apollo and AIP. [S1]
- 2025 revenue was $4.475 billion and customer count was 954. [S1]
- 2025 revenue mix was 54% government / 46% commercial. [S1]
- Palantir generally offers software contracts of one to five years, with revenue generally recognized over the term. [S1]
- AIP, Foundry and Apollo are designed as an integrated enterprise operating system. [S4]
- The Ontology connects data, logic, actions and security. [S5][S6]
- Q2 2026 revenue was $1.935 billion. [S3]
Explore Palantir
Platforms
- Palantir Gotham
- Palantir Foundry
- Palantir AIP
- Palantir Apollo
- Palantir Ontology
People
- Alex Karp
- Stephen Cohen
- Shyam Sankar