Databricks

Klarna

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.

Klarna

Klarna makes money from merchant fees and consumer interest. Merchants pay Klarna 3-6% of each transaction — they're willing to pay because Klarna increases conversion rates by 30%+ and average order values by 45%.

On "Pay in 4" (interest-free installments), Klarna makes money purely from merchant fees. On longer financing (6-36 months), Klarna charges consumers interest up to 25% APR.

Klarna also earns revenue from its shopping app (affiliate commissions when users discover and buy from merchants), and from its Klarna Card.

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.

Klarna

Sebastian Siemiatkowski, Niklas Adalberth, and Victor Jacobsson were students at the Stockholm School of Economics. In 2005, they entered a startup competition with an idea: let people buy things online and pay later.

At the time, online shopping was still new and most people were terrified of entering their credit card details on the internet. The idea was simple — Klarna would pay the merchant immediately, and the customer would get an invoice with 14-30 days to pay.

The competition judges hated it. The idea was dismissed as financially irresponsible and the team didn't win.

But Siemiatkowski pressed on. Swedish e-commerce was growing fast and merchants were desperate for any way to reduce cart abandonment.

Klarna's "pay after delivery" model was a hit because it shifted the risk — customers could receive the product, try it on, and only pay for what they kept.

The first customers were Swedish e-commerce merchants selling fashion and home goods. Klarna handled the invoicing, fraud detection, and collections.

Merchants saw conversion rates jump because customers were more willing to buy when they didn't have to pay immediately.

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.

Klarna

Klarna grew by being embedded at checkout. The strategy was to sign up the biggest online retailers and become a payment option alongside Visa and PayPal.

Once Klarna was at checkout, consumers discovered it organically. The "Pay in 4" button became ubiquitous across fashion, electronics, and home goods retailers.

The Klarna app became a growth engine beyond checkout. By building a shopping app where users could browse products, discover deals, and track deliveries, Klarna turned from a payment method into a shopping destination.

The app has 35+ million monthly active users who start their shopping journey inside Klarna before even visiting a retailer.

International expansion was aggressive. Starting in Sweden, Klarna rolled out across Europe, then into the US, UK, and Australia.

The US became the biggest growth market — American consumers were especially receptive to Pay in 4 as an alternative to credit cards. By 2023, Klarna had 34 million US users.

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.

Klarna

The valuation collapse was humiliating. Klarna raised at a $46 billion valuation from SoftBank in 2021.

One year later, they raised a down round at $6.7 billion — an 85% haircut. It was the most dramatic valuation drop in fintech history.

Employee stock options were underwater. Siemiatkowski had to lay off 10% of the workforce.

The entire BNPL category went from hot to radioactive in months.

Credit losses are the existential risk. Klarna is lending money to consumers who want to buy things they can't afford to pay for right now.

When the economy slows, defaults rise. Klarna's credit losses hit $1 billion in 2022.

The company had to tighten underwriting significantly and pull back from riskier markets. The tension between growth (approve more loans) and profitability (reject risky borrowers) defines every quarter.

The IPO in 2025 was a comeback story but with caveats. Klarna went public at $15 billion — a major recovery from the $6.7 billion trough but still less than a third of its 2021 peak.

The company finally turned profitable by slashing costs with AI (replacing hundreds of customer service agents with AI chatbots) and tightening credit standards. But investors remain cautious about the BNPL model's long-term sustainability.

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.

Klarna

Pay in 4 is the signature product — split any purchase into four interest-free payments over six weeks. Pay in 30 lets customers receive the product first and pay within 30 days.

Financing offers longer-term payment plans with interest for larger purchases. The Klarna App is a shopping destination — browse deals, track orders, manage payments, and earn cashback.

The Klarna Card is a physical Visa card that lets users Pay in 4 anywhere. Klarna Creator is a platform for influencers to earn commissions sharing products.

Klarna AI is their customer service chatbot that handles two-thirds of support queries.

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.

Klarna

Sequoia Capital, SoftBank, Silver Lake, GIC, Atomico, Commonwealth Bank of Australia, Heartland