Databricks

Palantir

Databricks

Databricks runs on a consumption-based pricing model. Companies pay for the compute and storage they actually use on the Databricks platform, measured in "Databricks Units" (DBUs).

The more data you process, the more you pay. This is brilliant because it means revenue grows automatically as customers' data volumes grow — which in the age of AI, they always do.

The platform runs on top of the major cloud providers — AWS, Azure, and Google Cloud. Databricks doesn't own servers.

They're a software layer that makes those clouds dramatically more useful for data work. They take a margin on top of the underlying cloud compute costs, essentially acting as a "toll booth" between companies and their data.

They also pioneered the "lakehouse" architecture — a mashup of data warehouses (structured, fast querying) and data lakes (cheap, handles any data format). Before Databricks, companies had to maintain both.

The lakehouse collapses them into one system. This isn't just clever marketing — it genuinely saves enterprises millions in duplicate infrastructure.

Palantir

Palantir's business model is enterprise software — specifically, large multi-year contracts with governments and corporations. Contracts typically start at $1-5 million and can scale to hundreds of millions annually for large government agencies.

The sales process is uniquely intensive. Palantir deploys "forward-deployed engineers" (FDEs) who embed directly with customers for months, configuring the platform for specific use cases.

This hands-on approach is expensive but creates deep integration that makes switching nearly impossible. Once Palantir is embedded in an organization's workflows, it's practically permanent.

Revenue split has shifted over time. Government contracts (US and allied nations) historically dominated, but commercial revenue has been growing faster.

By 2024, commercial revenue approached 45% of total. Annual revenue exceeded $2.8 billion.

The company has been profitable since 2023.

Databricks

Databricks started as a research project at UC Berkeley's AMPLab around 2009. Matei Zaharia, a PhD student, was frustrated with how slow Hadoop MapReduce was for iterative machine learning workloads.

His answer was Apache Spark — an open-source engine that could process data up to 100x faster than MapReduce by keeping data in memory instead of writing to disk after every step.

Spark took off fast in the open-source community. By 2013, it was the most active open-source project in big data.

Zaharia and six Berkeley colleagues — Ali Ghodsi, Andy Konwinski, Arsalan Tavakoli-Shiraji, Ion Stoica, Patrick Wendell, and Reynold Xin — decided to build a company around it. They incorporated Databricks in 2013 with the idea that Spark was powerful but brutally hard to set up and manage.

The company would offer a managed cloud platform that made Spark accessible to data teams who weren't distributed systems engineers.

Their first product was essentially "Spark as a service" — a collaborative notebook environment where data scientists and engineers could write Spark jobs without managing clusters. The bet was that enterprises had massive data problems but not enough PhDs to solve them.

They were right.

Palantir

Palantir was born from the aftermath of September 11, 2001. Peter Thiel — PayPal co-founder and contrarian investor — realized that the same fraud-detection algorithms PayPal used to catch financial criminals could help intelligence agencies catch terrorists.

The US government had mountains of data but terrible tools for connecting the dots.

Thiel co-founded Palantir in 2003 with Alex Karp (a Stanford Law PhD who had studied social theory under Jürgen Habermas in Frankfurt), Joe Lonsdale (a Stanford student who'd worked at Clarium Capital), Stephen Cohen (an engineer), and Nathan Gettings (a Clarium colleague). They named it after the palantíri in Tolkien's Lord of the Rings — the seeing stones that let you view distant events.

The CIA's venture arm, In-Q-Tel, was the first investor and first customer simultaneously. The initial product, Palantir Gotham, was built specifically for intelligence analysts who needed to find connections across massive, messy datasets — linking phone records, financial transactions, travel data, and classified intelligence into a single coherent picture.

The company operated in extreme secrecy for its first decade, with most employees unable to discuss what they actually built.

Databricks

Databricks grew by being genuinely useful before being profitable. They contributed massively to Apache Spark's open-source ecosystem, which meant thousands of companies were already using Spark when Databricks offered to manage it for them.

The open-source-to-enterprise pipeline is the most powerful go-to-market motion in software.

They also bet big on partnerships. The Microsoft partnership was transformational — Azure Databricks became a first-party service on Azure, meaning Microsoft's sales force was effectively selling Databricks to every enterprise customer.

That single deal probably added billions in annual recurring revenue.

Acquisitions were strategic and well-timed. MosaicML in 2023 for $1.3 billion gave them proprietary AI training capabilities right when every enterprise wanted to build custom AI models.

Tabular in 2024 brought the creators of Apache Iceberg, another critical open-source data format. They bought the talent and the technology simultaneously.

Palantir

Palantir's growth strategy for two decades was simple: get inside the US government, prove indispensable, and expand from there. CIA led to NSA.

NSA led to the Army. The Army led to the Air Force.

Each agency saw what the others were doing and wanted it.

The AIP launch in 2023 was the commercial growth inflection point. By integrating large language models into the platform, Palantir made its data analytics accessible to non-technical users.

A supply chain manager could ask questions in plain English and get answers from their data. This dramatically expanded the potential user base within existing customers and attracted new commercial clients.

"Boot camps" became the commercial go-to-market innovation. Palantir runs intensive multi-day workshops where potential customers bring their actual data and problems, and Palantir engineers build working prototypes on the spot.

Companies leave with tangible proof of value, which accelerates the sales cycle dramatically.

Databricks

The elephant in the room is Snowflake. Both companies want to be the single platform where enterprises do all their data work, and the overlap is growing fast.

Snowflake started in SQL analytics and is pushing into data engineering and ML. Databricks started in data engineering and ML and is pushing into SQL analytics.

The collision is inevitable and expensive — both are spending billions on sales and R&D.

There's also the cloud provider threat. AWS, Azure, and Google Cloud all have their own data analytics services and could theoretically squeeze Databricks by making their native tools better or cheaper.

Databricks runs ON these clouds, which means their biggest partners are also their biggest potential competitors. It's the classic platform risk problem.

So far, Databricks has stayed ahead by innovating faster than the cloud providers' internal teams, but it's a race that never ends.

Palantir

The ethical debate follows Palantir everywhere. Privacy advocates have criticized Palantir's work with ICE (Immigration and Customs Enforcement), police departments, and intelligence agencies.

The company has been accused of enabling mass surveillance. Karp has been unapologetic — arguing that democracies need powerful analytical tools and it's better that a company with ethical guidelines builds them than the alternative.

Customer concentration was a historical risk. For years, a handful of massive government contracts drove the majority of revenue.

Losing a single contract could crater a quarter. The push into commercial has diversified the revenue base, but government still represents over 55% of revenue.

Valuation has been the market debate. Palantir trades at astronomical revenue multiples (60-80x revenue at its 2024 peaks), which assumes massive future growth that may or may not materialize.

Bears argue it's the most overvalued stock in tech. Bulls argue that AIP will drive exponential commercial growth.

The debate is loud and ongoing.

Databricks

Unity Catalog — a universal governance layer that lets companies manage permissions, lineage, and access control across all their data and AI assets in one place. Delta Lake — an open-source storage layer that brings reliability to data lakes with ACID transactions, schema enforcement, and time travel (yes, you can query your data as it existed at any point in the past).

Databricks SQL — a serverless SQL analytics product that competes directly with Snowflake on their home turf. Mosaic AI — their machine learning and generative AI platform, supercharged after acquiring MosaicML in 2023 for $1.3 billion.

Databricks Notebooks — collaborative workspaces where data teams write code, visualize results, and build pipelines together in real time.

Palantir

Palantir Gotham — the original intelligence platform used by government agencies for counterterrorism, military operations, and law enforcement. Integrates and analyzes data from disparate classified and unclassified sources.

Palantir Foundry — the commercial platform that lets corporations build data-driven applications without coding. Used for supply chain optimization, clinical trials, financial modeling, and manufacturing.

Palantir AIP (Artificial Intelligence Platform) — launched in 2023, this layer brings large language models and generative AI into Palantir's existing platforms, letting users query and act on their data using natural language. The product that supercharged the stock price.

Palantir Apollo — a continuous delivery system that manages software deployment across every environment: cloud, on-premise, classified networks, and even air-gapped military systems.

Databricks

Andreessen Horowitz led multiple early rounds and has been the longest-standing institutional backer. Microsoft made a massive strategic investment alongside the Azure Databricks partnership.

T. Rowe Price, Tiger Global, and Franklin Templeton participated in later growth rounds.

NEA was an early investor. The $10 billion Series J in 2024 valued the company at $62 billion and was led by Thrive Capital with participation from Andreessen Horowitz, DST Global, GIC, Insight Partners, and WCM Investment Management.

Palantir

In-Q-Tel (the CIA's venture arm) was the first investor and provided both capital and credibility. Peter Thiel's Founders Fund invested from the founding.

The company raised extensively from institutional investors including Tiger Global, Dragoneer, and Sompo Holdings. The September 2020 direct listing on the NYSE (similar to Spotify — no new shares sold) valued the company at approximately $22 billion.

The stock subsequently surged past $200 billion market cap in late 2024.