Palantir Foundry is the Best Software I've Ever Used (And Why That Sits Uneasily With Me)

Let’s be completely honest upfront:

Palantir Foundry is probably the best piece of enterprise software I have ever used.

I don't say that lightly. I’ve spent the last 10 years working across enterprise and government environments, building on Azure, experimenting with Google Cloud, and wrangling pipelines on Databricks.

Like most developers, I’d heard the reputation for years. Palantir has a bad rap in tech. People call it a shadowy spy tool, protest outside their offices, and argue about whether working there is a moral compromise. The reflexive reaction from the tech community is usually instant distaste.

So when I finally got hands-on with Palantir’s developer environment, played with Foundry, and built workflows in AIP, I was expecting an overhyped, clunky enterprise suite that only worked because of high-priced consultants.

Instead, I was blown away by the engineering.

And that immediately triggered an uncomfortable internal conflict.

---

1. Why the Tech Blew My Mind

If you’ve built data apps in typical enterprise environments, you know the drill.

To take raw enterprise data and turn it into something a frontline user can actually interact with, you are constantly duct-taping half a dozen separate tools together:

• An ETL pipeline in Azure Data Factory or Google Cloud Dataflow
• A lakehouse layer in Databricks Delta Lake or BigQuery
• A transformation layer in dbt
• A catalog or governance tool like Purview or Dataplex
• An AI service on Vertex AI or Azure OpenAI
• A frontend in PowerApps, Retool, or custom Angular/React
• A fragile write-back API to push user decisions back to the source database

Every single seam between those systems is a place for things to break. You spend 80% of your time fighting auth tokens, syncing schema mismatches, and writing glue code just to keep the lights on.

The Power of the Kinetic Ontology

When you open Palantir Foundry, that entire headache disappears.

The core breakthrough of Palantir isn't just fast compute—it's the Ontology.

Most cloud tools give you read-only views of your data. You look at a dashboard, see a metric, and then have to log into another system to do anything about it.

Palantir turns your entire organisation into a living digital twin made of three things:

1. Objects (Nouns): The real-world things (Patients, Assets, Vehicles, Shipments, Invoices).
2. Links (Relationships): How those objects connect to each other in real time.
3. Actions (Verbs / Write-backs): Governed operations that let users or AI agents actually update source systems directly from the UI with strict security.

Architecture Layer	Standard Cloud Stack (Fragmented)	Palantir Foundry (Kinetic Closed-Loop)
Ingestion & Storage	Raw Data → Lakehouse → dbt (Read-Only)	Live Data Streams → Object Storage (OSv2)
Semantic Layer	Disconnected metric views & static catalogs	Kinetic Ontology (Living Objects & Links)
Write-Back Mechanism	Fragile custom APIs & bespoke glue code	Governed, auditable Actions to source systems
Operational UI	Separate BI dashboards & disconnected forms	Integrated Workshop Apps & AIP Agents

Effortless UIs, Dashboards, and Web Apps

One of the most impressive parts of the platform is how easily you can build interactive UIs and web apps directly off the backing datasets.

Because the Ontology already knows how everything is linked, assembling an operational dashboard or full web interface in Workshop is ridiculously fast. Any UI element you drop on a canvas can immediately traverse relationships. If you click on a vehicle, every linked maintenance log, assigned driver, active shipment, and open ticket is right there—automatically wired up without you having to write manual joins, state management, or custom GraphQL/REST endpoints.

What takes an enterprise team three to six months to build on AWS or Azure—wiring up data models, UI components, state management, and secure write-back pipelines—you can literally link together in Foundry in an afternoon.

It’s the smoothest, most coherent data developer experience I’ve ever seen.

---

2. Navigating the Moral Grey Area

Once you see how good the software is, you can’t ignore the friction: Can you separate the technology from the company and its reputation?

This is something I wrestled with deeply. In my personal life, I hold myself to a strict ethical framework—I’ve been a vegan for years specifically for animal ethics and systemic water resource impact. When you're used to making daily choices based on systemic consequences rather than convenience, you don't just brush ethics aside.

So I started breaking down where the hostility against Palantir actually comes from.

The Double Standard

There is a massive double standard in how we judge software versus traditional industries:

• People happily fly on Boeing planes for vacations, even though Boeing is a massive global defence contractor.
• People drive petrol cars, heat their homes, and buy everyday plastics from BP or Shell, knowing full well the environmental toll of fossil fuels.
• Tech workers use AWS and Google Cloud every day, even though both hyperscalers hold massive government and military contracts.

So why does Palantir cop so much heat?

In my view, Palantir pays what I call the "PR Tax on Efficiency."

Platforms like Snowflake or Databricks are viewed as neutral, blank-slate infrastructure because of their academic roots. But an organisation could take Databricks or Google Cloud and build the exact same controversial surveillance or intelligence pipeline.

The only difference is that on standard tools, it’s slow and hard to build. On Palantir, the tool is so well-engineered that it makes organisations hyper-efficient. We end up blaming the toolmaker for the actions of the client simply because the tool is too good at what it does.

Scalpel vs. Sledgehammer

Then there's the defence question. Defence tech is inherently messy and uncomfortable.

Critics often view AI in defence as an automated destruction machine. But from an engineering and systems perspective, the entire purpose of having superior decision intelligence is precision over brute force.

Having software that can pierce the fog of war, accurately map friendly and civilian assets, and clarify real-time logistics acts as a scalpel rather than a sledgehammer. In high-stakes conflict, better intelligence prevents catastrophic mistakes and reduces collateral damage.

Democratic societies need strong, modern infrastructure to protect themselves. Pretending that need doesn't exist is a luxury that ignores how the real world operates.

---

3. The Real Challenge: Legacy Modernisation, Skills Deficits, and the Complexity of Scale

Having spent a decade working across government and large enterprise environments, I have a deep appreciation for just how difficult large-scale modernisation really is.

Large public institutions and enterprise organisations aren't slow by choice—they are managing extraordinary operational complexity, strict regulatory guardrails, and a severe structural skills deficit.

Consider the real-world numbers from recent Australian public sector workforce reports:

• The "Keep the Lights On" Tax: Hundreds of full-time ICT roles across government are tied up purely maintaining obsolete legacy platforms and preventing critical outages.
• The Impending Knowledge Cliff: Over 20% of the public service digital workforce is aged 55 or older, holding decades of unwritten, foundational domain knowledge on legacy systems that is at risk of walking out the door upon retirement.
• The Dual-Stack Trap: Because essential services (welfare payments, tax, healthcare, defence logistics) can never have downtime, agencies are forced to run two workforces simultaneously: one maintaining legacy mainframes, and another attempting multi-year cloud transformations.

When you look at this through a systems lens, you realise why an ontology-driven platform like Palantir is such a massive capability uplift.

Modernisation Dimension	The Legacy Trap	The Ontology Uplift
System Integration	40-year mainframes & siloed databases	Connects directly to legacy data in-place
Logic & Relationships	Decades of fragile, undocumented patchwork code	Maps core logic into reusable Objects & Links
Workforce Continuity	Dual-stack maintenance paralysis	Captures retiring domain knowledge into software
Delivery Velocity	Multi-year rebuilds that often fail or stall	Rapid operational prototypes deployed in days

Instead of embarking on high-risk, multi-billion-dollar "big bang" rebuilds that take a decade and often fail, an ontology layer bridges the gap. It connects directly to existing data in place, maps the underlying business logic into structured Objects and Links, and preserves vital institutional knowledge in software rather than people's heads.

The Forward Deployed Engineer (FDE) Model

This is also why Palantir’s Forward Deployed Engineer (FDE) model is so effective.

Rather than handing over software and leaving already-stretched internal teams to figure it out, Palantir embeds top-tier engineers directly alongside operational teams on the front lines.

They work collaboratively with subject matter experts, linking siloed data sources into an initial ontology and standing up functional, secure operational prototypes in days or weeks. It acts as an immediate forcing function, proving value fast and bypassing the bureaucratic paralysis that usually stalls large-scale digital transformation.

---

4. The Outcome: Bringing the Ontology Mindset to the Google Stack

So where did this exploration lead me?

Testing Palantir fundamentally changed how I think about software and data architecture. Once you experience an environment where data, relationships, actions, and UIs are linked as a live operational digital twin, you can never go back to building passive, read-only dashboards with fragile glue code.

It directly shaped how and what I am building in Google Cloud.

I learned a massive amount from Palantir's design patterns, and it directly influenced the enterprise metamodel framework I am currently developing. The goal is simple: take the structural power of an operational ontology—mapping business entities, relationships, and kinetic actions—and implement it cleanly within modern cloud architecture.

While Palantir for Builders is an exciting avenue, for my own core projects and development path, I made a clear decision: to work within the Google ecosystem and build similar kinetic logic on top of the Google stack and the Agentic Lakehouse.

Palantir Primitive	Google Cloud / Agentic Stack Equivalent	Architectural Role
Kinetic Ontology	BigQuery Agentic Lakehouse	Active conversational memory & open Iceberg storage
Actions & Write-Backs	ADK & Model Context Protocol (MCP)	Governed execution tools & write-back triggers
Workshop UIs & Web Apps	Angular + Firebase App Hosting	Fast, reactive, and predictable operational frontends
AIP & AI Orchestration	Gemini Frontier Models + Antigravity IDE	Multimodal reasoning & autonomous agent workflows

By taking the architectural principles pioneered by Palantir's Ontology and applying them to an enterprise metamodel on Google Cloud:

1. Open Standards & Ownership: Storage is backed by open formats like Apache Iceberg and standard protocols like MCP (Model Context Protocol), avoiding proprietary lock-in.
2. True Serverless Economics: Instead of heavy platform minimums, the entire stack scales to $0 when idle.
3. Agentic Memory & Reasoning: BigQuery isn't treated as a dead warehouse; it serves as the active, conversational reasoning layer for autonomous agents.

Palantir gave me the architectural blueprint for what data software should feel like. The Google Agentic Lakehouse gives me the open, scalable toolkit to actually build it my way.

---

Wrapping Up

Testing Palantir Foundry was a massive eye-opener for me.

On an engineering level, it set a new benchmark for what modern data architecture should look like: a kinetic, interconnected digital twin that bridges the gap between raw data and real-world action.

On a personal level, it reinforced that the most impactful tools in the world are rarely simple or uncontroversial. If we want systems that actually work for society, we have to look past the superficial noise, respect elite engineering, and be willing to engage with the hard problems.

And most importantly, it gave me a standard of operational speed that I now bring into every system I architect.

---

This note is currently in 🌱 Seed stage. In future posts, I plan to share more detailed learnings, architectural blueprints of the enterprise metamodel framework, and walkthroughs of how I structure this agentic lakehouse logic in practice.

Explore related thoughts in My Tech Stack: The Google-Native Architecture for the Agentic Era, What is underground.tech?, and From Gemini Chats to Signals.