If Palantir Is Just Software, Why Can't Companies Build It Themselves?



Companies can build many of the things Palantir does. The harder question is whether they want to connect, secure, and maintain all of it themselves.

Palantir is one of the fastest-growing large software companies in the world.

Its software is used across government, defense, manufacturing, healthcare, energy, and other industries.

And lately, the company has been growing remarkably fast.

In the second quarter of 2026, Palantir reported nearly $2 billion in revenue, up 93% from a year earlier. Its U.S. business grew even faster, at 115%.

But there is something strange about the Palantir story.

Palantir doesn't manufacture advanced chips.

It doesn't build robots.

Much of what its software works with is something large companies already have:

data.

Companies already have cloud services.

They already have databases.

They have software engineers.

And today, they can access powerful AI models.

So why can't a large company simply build its own Palantir?

Is Palantir really doing something nobody else can build?

The short answer is:

No.

Companies can build many of these capabilities themselves.

The difficult part is making everything work together.

The Data Is Already There

Imagine a car factory.

The company may already have information about its parts, suppliers, machines, production, customer orders, and deliveries.

But that information may live in different systems.

The inventory system knows how many parts are available.

The production system knows how many cars need to be built.

The supplier system knows when new parts will arrive.

The order system knows which cars have been promised to customers.

Each system knows something.

The problem is understanding how those things affect one another.

Imagine the inventory system says:

“We are short 500 brake components.”

The production system says:

“We need to build 2,000 cars tomorrow.”

And each car needs one of those components.

Now the meaning becomes obvious.

Unless the factory finds more parts or changes its plan, it cannot build 500 of those cars as scheduled.

Then more questions appear.

Which customer orders will be delayed?

Can another supplier provide the parts?

Can the factory change its production schedule?

Which option costs less?

The information already existed.

The hard part was understanding how it was connected in the real world.

That is a big part of the problem Palantir is trying to solve.

This Is Where the “Ontology” Comes In

Palantir uses a word that sounds complicated:

Ontology.

The basic idea is much simpler.

Palantir describes its Ontology as a digital representation of an organization. In many cases, it acts as a kind of digital twin connecting a company's existing data and models to real-world things such as factories, equipment, products, customer orders, and transactions.

Think about the factory again.

Instead of seeing unrelated tables full of numbers, the system can represent things such as:

Factory

Production line

Car

Brake component

Supplier

Customer order

Then it connects them.

This component goes into this car.

This supplier provides that component.

This production line builds that car.

This customer is waiting for that order.



The result is something closer to a map of how the business actually works.

But understanding the business is only half of the problem.

Something has to happen next.

From Seeing a Problem to Doing Something About It

Suppose the system detects that a supplier delay could interrupt production.

A traditional analytics tool might show the problem on a dashboard.

That's useful.

But someone still has to decide what to do.

Palantir's Ontology is designed to connect data with actions too.

Those actions can range from simple changes to multi-step updates that are written back into operational systems. Palantir describes its architecture as connecting four things:

data, logic, actions, and security.

The difference is important.

The goal isn't only:

“Tell me what's happening.”

It is also:

“Help me decide what to do next, and connect that decision to the systems where work actually happens.”

That's much more ambitious than simply storing data.

So Why Not Build It Yourself?

A company can.

There is no technological rule saying only Palantir can connect data, build applications, use AI models, manage permissions, or create business workflows.

A company could build data pipelines.

It could use cloud infrastructure.

It could connect AI models.

It could build its own applications.

It could hire engineers to make everything work together.

And Palantir itself doesn't necessarily replace every other technology inside a company. Its platform is designed to work with existing data, AI, workflow, and security systems, including data stored in open formats and accessed through standard interfaces.

So the real question isn't:

“Can we build it?”

It is:

“How much work will it take to build and keep it running?”

Connecting Everything Is the Hard Part

Imagine a company decides to build the system itself.

It may need to connect dozens — sometimes far more — of old and new data sources.

Someone has to decide what all that data means.

Someone has to define who can see it.

Someone has to make sure a change in one system doesn't break another.

Employees need applications they can actually use.

AI needs access to the right information without getting access to information it shouldn't see.

And when the business changes, the system has to change too.

The individual technologies may already exist.

The integration is the difficult part.

That helps explain why Palantir has built its platform around a shared operational layer rather than simply selling another database.

Palantir Even Sends Engineers Into the Problem

This also explains an unusual part of Palantir's business.

The company is known for Forward-Deployed Engineers, or FDEs.

Instead of simply delivering software and leaving customers to figure it out, Palantir engineers work close to customers' real operational problems.

Palantir says this approach has shaped its software from the beginning: engineers work near customer problems, then feed what they learn back into the core product teams.

That matters because every large organization is different.

A hospital doesn't operate like an automaker.

An automaker doesn't operate like an energy company.

Even two car companies may store their information differently and make decisions in different ways.

So implementing a system like this isn't always as simple as:

Sign up → log in → start using it.

Someone first has to understand how the organization actually works.

Is Palantir Part Software Company, Part Consulting Company?

At first glance, it can look that way.

Traditional software has an attractive business model because one product can be sold repeatedly.

Consulting is different.

Every new project may require more people.

If Palantir needed large teams of engineers for every new customer forever, that could make scaling the business harder.

But Palantir's model is designed to turn lessons from individual customer problems into improvements to the software itself.

In other words:

Engineers solve a problem.

↓

The company learns from it.

↓

The software becomes better at solving similar problems.

That is the theory.

And now AI could change the economics of this model again.

Palantir Is Even Building an AI Engineer

Palantir now offers something called AI FDE — an AI-powered forward-deployed engineer.

Users can give it instructions in natural language, and the system can perform tasks inside Foundry such as building or modifying data pipelines, managing code repositories, editing an Ontology, auditing permissions, and building applications.

That's interesting for a simple reason.

Historically, complicated enterprise software often required expensive human experts to build and maintain it.

If AI can perform more of that work, the economics could change.

A human engineer might be able to accomplish more.

Customers might be able to build systems faster.

Palantir might be able to serve more organizations without human labor growing at exactly the same rate.

But that is a possibility, not a proven outcome.

Real companies are messy.

They have old software.

Incomplete data.

Security restrictions.

Exceptions.

Regulations.

And people who don't always follow perfectly designed processes.

How much of this work AI can truly automate is still an open question.

Maybe Palantir's Biggest Competitor Isn't Another Company

Palantir is often compared with other large data and cloud platforms.

But that can hide a more interesting competition.

A customer doesn't necessarily have to choose:

Palantir vs. another Palantir.

It can choose:

Palantir vs. build it ourselves.

This is one of the oldest questions in enterprise technology:

Build or buy?

Building can give a company more control.

Buying can save time and reduce the amount of technology the company has to assemble itself.

Neither answer is automatically correct.

“Build It Ourselves” Has a Hidden Price

Software licenses aren't the only cost.

Building internally may require:

software engineers,

data engineers,

security specialists,

cloud infrastructure,

monitoring,

maintenance,

upgrades,

and years of knowledge about how the system works.

Employees leave.

Business processes change.

New regulations appear.

New AI models arrive.

Old software still needs to work.



A system isn't finished when it launches.

Someone has to keep it working.

So the real comparison isn't simply:

Palantir's price vs. another software product's price.

It is closer to:

the total cost of buying and operating Palantir

versus

the total cost of building and maintaining something similar yourself.

That's a much harder calculation.

That Doesn't Mean Palantir Is Always the Answer

None of this means every company needs Palantir.

Many don't.

A smaller organization may not have enough complexity to justify a platform like this.

A company with a strong engineering organization may prefer to build more of its own systems.

Another business may already have a data architecture that works well.

And deep integration creates another question:

What happens if you eventually want to leave?

The more important any software becomes to the daily operation of a company, the more important switching costs become.

Competitors also aren't standing still.

If cloud platforms, data companies, and AI tools make it dramatically easier to connect these systems, some of the complexity Palantir helps manage could become easier for customers to handle themselves.

Palantir's advantage is not guaranteed forever.

So Why Can't Companies Build Their Own Palantir?

They can.

That may be the most important answer.

Palantir's value isn't necessarily that every individual piece of its technology is impossible to reproduce.

The problem is that large organizations are complicated.

Their data is scattered.

Their software is fragmented.

Their security rules matter.

Their decisions cross many different systems.

And connecting all of that into something humans — and increasingly AI — can actually use is difficult.

Palantir is trying to package that complexity into a platform.

The individual pieces may exist elsewhere.

Some may even be better elsewhere.

But somebody still has to put them together.

And somebody has to keep them working.

So perhaps the more interesting question isn't:

“Can a company build its own Palantir?”

It is:

“Yes. But does it really want to?”

Sources

BEYOND THE OBVIOUS.