Databricks

Zoom

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.

Zoom

Zoom uses a freemium model. Free accounts get unlimited one-on-one meetings and 40-minute group meetings.

Paid plans start at $13.33/month per user for Pro (meetings up to 30 hours), $18.33/month for Business, and custom pricing for Enterprise. Zoom also charges for add-ons — Zoom Phone (cloud phone system), Zoom Rooms (conference room hardware), and Zoom Contact Center.

The free tier is the hook. The 40-minute limit on group calls creates just enough friction to push power users to pay.

And once one person in a company pays, the whole team follows because nobody wants to be the one whose meetings keep getting cut off.

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.

Zoom

Eric Yuan grew up in Tai'an, a mining city in Shandong province, China. In the mid-1990s he was in his twenties and inspired by the internet boom happening in America.

He applied for a US visa. He was rejected.

He applied again. Rejected again.

Eight times in total over about two years before he was finally approved in 1997. He moved to Silicon Valley with barely any English and joined WebEx as one of its first engineers.

Yuan spent 14 years at WebEx, eventually becoming VP of Engineering. Cisco acquired WebEx in 2007 for $3.2 billion.

Yuan watched Cisco slowly bloat the product with enterprise features while the core video quality deteriorated. He kept telling Cisco leadership they needed to rebuild the product from scratch.

They kept saying no.

In 2011, Yuan quit and took 40 WebEx engineers with him. He founded Zoom Video Communications with a simple thesis: video meetings should just work.

No downloads. No lag.

No "can you hear me?" No IT department required. He built the product that Cisco refused to build.

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.

Zoom

Zoom grew on one thing: it worked. In a world where every video call started with five minutes of technical issues, Zoom calls just connected.

That reliability was the entire marketing strategy for the first five years.

The freemium model did the rest. Teachers, coaches, therapists, book clubs, and small teams all started on the free tier.

When they hit the 40-minute limit, enough of them converted to paid. And every free meeting was essentially a demo — everyone in the meeting saw how good Zoom was.

Yuan obsessed over simplicity. While competitors like WebEx and GoToMeeting required downloads, plugins, and IT involvement, Zoom worked in the browser with one click.

The learning curve was essentially zero. Your grandmother could figure it out.

That turned out to be extremely important when a pandemic suddenly required your grandmother to figure it out.

Then COVID happened. In March 2020, the world shut down.

Every meeting — work, school, family, doctor, church, happy hour — moved to video. Zoom was already the easiest option.

Daily participants exploded from 10 million to 300 million in four months. The stock went from $70 to $588.

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.

Zoom

The post-pandemic hangover has been severe. Zoom's stock peaked at $588 in October 2020 and crashed to under $70 by 2022 — an 88% drop.

The company went from the most exciting tech stock on the planet to a cautionary tale about pandemic valuations. Revenue growth slowed from 300%+ to single digits as people returned to offices and competitors caught up.

Security and "Zoombombing" were early crises. In early 2020, as millions of new users flooded in, trolls discovered they could join open Zoom meetings and share offensive content.

Schools, churches, and AA meetings were disrupted. It turned out Zoom's encryption wasn't truly end-to-end as advertised.

Yuan had to halt all feature development for 90 days and focus exclusively on security fixes. It was a near-death reputational crisis.

Competition from Microsoft Teams and Google Meet intensified. Both companies bundled video calling for free into products that hundreds of millions of people already used.

Microsoft Teams integrated directly into Office 365. Google Meet was built into Gmail.

Zoom had to compete against free products from two of the richest companies on Earth.

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.

Zoom

Zoom Meetings is the core — video calls that actually work. Zoom Webinars handles large-scale events with up to 50,000 attendees.

Zoom Phone is a full cloud-based phone system replacing traditional office phones. Zoom Rooms turns physical conference rooms into one-click video meeting spaces.

Zoom Contact Center competes with established call center software. Zoom Team Chat is their Slack/Teams competitor.

Zoom Whiteboard is a collaborative digital canvas. Zoom Revenue Accelerator uses AI to analyze sales calls.

And Zoom AI Companion summarizes meetings and drafts messages.

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 later rounds multiplying that several times over with participation from Andreessen Horowitz, DST Global, GIC, Insight Partners, and WCM Investment Management.

Zoom

Sequoia Capital, Emergence Capital, Horizons Ventures (Li Ka-shing), Qualcomm Ventures