Databricks

GitPod

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.

GitPod

GitPod started as freemium: a free tier of monthly cloud hours, paid plans for more hours and bigger workspaces, and enterprise contracts with self-hosted and dedicated options. As Ona it shifted toward enterprise contracts for running AI coding agents inside a customer's own cloud, with governance and security controls.

It said enterprise annual recurring revenue quadrupled year over year before the rename. OpenAI acquired the company in 2026, and existing customer commitments continue to be supported.

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.

GitPod

The GitPod team came out of TypeFox, a German developer tools company in Kiel behind the Theia cloud IDE framework. Sven Efftinge and his colleagues realized the biggest pain point was not the editor itself but the environment.

Developers spent hours configuring machines, installing dependencies and debugging "it works on my machine" problems.

They launched GitPod to provide instant, automated cloud development environments. One click from a GitHub or GitLab repo opened a fully configured workspace with dependencies installed and tools ready.

Johannes Landgraf, who became CEO, and CTO Christian Weichel later led the company through its rename to Ona in 2025 and its sale to OpenAI in 2026.

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.

GitPod

GitPod grew through open source community adoption and one-click integrations with GitHub, GitLab and Bitbucket, where a "Gitpod" button on a repo launched a ready workspace. It later moved upmarket to banks and pharmaceutical companies that wanted cloud development under tight security.

In 2025 it bet the company on AI agents, renaming itself Ona and claiming its own agents were producing most of its merged code. That bet ended in a sale to OpenAI in 2026.

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.

GitPod

GitHub Codespaces, backed by Microsoft and GitHub's enormous distribution, was a direct competitor, and AWS stopped taking new Cloud9 customers in 2024, a sign of how tough the cloud IDE market was. Convincing developers to give up their carefully configured laptops proved hard.

Then AI coding tools threatened to make the whole category a feature. GitPod's answer was to reinvent itself as Ona, an AI agent platform, and ultimately to sell to OpenAI in 2026 rather than compete alone.

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.

GitPod

Before 2025: cloud development environments, instant pre-configured workspaces defined in a .gitpod.yml file, with prebuilds and support for VS Code and JetBrains, plus a self-hosted enterprise option. As Ona (since September 2025): a platform for running software engineering AI agents in secure, sandboxed cloud environments, with VS Code in the browser and access from phone, tablet or laptop.

Since August 2026 the technology is part of OpenAI's Codex team.

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.

GitPod

Speedinvest led the $3 million seed in October 2020, with Crane Venture Partners and Vertex Ventures US. General Catalyst led a $13 million round in April 2021.

GitHub founder Tom Preston-Werner led the $25 million Series A in November 2022. Total funding was about $41 million.