Databricks

Snowflake

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.

Snowflake

Snowflake charges based on consumption — you pay for the compute time and data storage you actually use. Compute is measured in "credits" consumed by virtual warehouses (their term for compute clusters), and storage is billed per terabyte per month.

This model is beautiful for Snowflake because customers rarely shrink their data — they only ever accumulate more.

The key insight was separating compute from storage. Customers can scale compute up or down independently, spin up multiple compute clusters against the same data simultaneously, and auto-suspend when not in use.

This means a company can run a massive analytics query during the day, shut down the warehouse at night, and pay nothing until tomorrow. Try doing that with Oracle.

Snowflake also makes money from data sharing. Their Data Marketplace lets companies buy and sell datasets directly through the platform — weather data, financial data, demographic data — without any copying or ETL.

Snowflake takes a cut of marketplace transactions and benefits from the network effects: the more data on the platform, the more valuable it becomes for everyone.

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.

Snowflake

Snowflake was born from frustration with Oracle. Benoit Dageville and Thierry Cruanes were senior engineers at Oracle for over a decade, and they watched traditional data warehouses struggle with the cloud era.

The old approach — giant on-premise appliances that cost millions and took months to set up — was clearly dying. But nobody had built a data warehouse from scratch specifically for the cloud.

In 2012, Dageville and Cruanes teamed up with Marcin Żukowski, a Dutch computer scientist who'd built a high-performance analytical database engine called VectorWise. The three of them started building in San Mateo, California, with a radical idea: completely separate storage from compute.

In traditional databases, storage and compute are locked together — if you need more processing power, you have to buy more storage too, and vice versa. Snowflake said that was insane and decoupled them entirely.

They spent two years in stealth mode before launching in 2014. The product was immediately different from anything on the market.

You could spin up compute clusters in seconds, run queries across massive datasets without managing any infrastructure, and only pay for what you used. Companies that had been spending six figures a year on Teradata appliances could suddenly do the same work on Snowflake for a fraction of the cost.

The product-market fit was almost violent.

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.

Snowflake

Snowflake grew through a relentless enterprise sales motion combined with a product that genuinely sold itself. Early on, they offered free trials that let data engineers experience the speed difference firsthand.

Once someone ran a query in 10 seconds that took 20 minutes on their old system, the sale was basically done.

They also invested heavily in a world-class sales organization. Frank Slootman, who became CEO in 2019 after running ServiceNow, brought an aggressive operational playbook that dramatically accelerated growth.

Under Slootman, Snowflake went from $265 million to over $2.8 billion in annual revenue in four years. He was famous for saying "growth is oxygen" and running the company with military precision.

The Data Cloud strategy was the long game. By encouraging data sharing between organizations on the platform, Snowflake created network effects — the more companies use Snowflake, the more valuable it becomes for everyone.

Over 9,000 customers now share data through the platform, creating a gravity well that makes leaving increasingly painful.

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.

Snowflake

Databricks is the existential threat. What started as a data engineering company has built a competitive SQL analytics product (Databricks SQL) that goes directly after Snowflake's core business.

The two companies are converging fast — Snowflake is pushing into data engineering and AI, Databricks is pushing into analytics. Both are spending billions to win.

Margins are also a constant battle. Snowflake runs on top of AWS, Azure, and Google Cloud, which means a significant chunk of revenue goes right back to the cloud providers as infrastructure costs.

Gross margins hover around 70% — good for most companies, but the cloud providers themselves operate at higher margins on the same underlying infrastructure. There's always the risk that AWS or Google could build "good enough" alternatives and undercut Snowflake on price.

So far, Snowflake's ease of use and ecosystem have kept customers loyal, but it's a fight they can never stop fighting.

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.

Snowflake

Snowflake Data Cloud — the core platform that stores, processes, and shares data across organizations with near-infinite scalability. Snowpark — a developer framework that lets engineers build data pipelines and ML models in Python, Java, or Scala directly inside Snowflake, without moving data out.

Cortex AI — their generative AI and machine learning layer that lets users build AI applications directly on their Snowflake data using LLMs and vector search. Snowflake Marketplace — a data exchange where over 2,000 providers list datasets that customers can access instantly without copying.

Streamlit — the open-source Python framework for building data apps, acquired in 2022 for $800 million, now deeply integrated as Snowflake's app-building layer.

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.

Snowflake

Sutter Hill Ventures was the earliest and most consequential investor — managing director Mike Speiser actually served as founding CEO and incubated the company. Altimeter Capital, Dragoneer Investment Group, and Salesforce Ventures participated in growth rounds.

Warren Buffett's Berkshire Hathaway famously bought $250 million in shares at the IPO price — Buffett's first IPO participation in decades. Sequoia Capital and ICONIQ Capital also invested pre-IPO.

The September 2020 IPO raised $3.4 billion at a $33 billion valuation.