Databricks

Sierra

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.

Sierra

Companies pay Sierra to run their customer service with AI agents instead of hiring rooms full of humans. The agent talks to your customers over chat.

It answers questions. It processes returns.

It updates subscriptions. Here is the clever part.

Sierra charges based on outcomes, not seats. Brands pay when the AI actually resolves a problem, not just for having the software installed.

That pricing is unusual. Most software charges a flat monthly fee no matter what.

Sierra only gets paid when the bot does the job.

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.

Sierra

Bret Taylor had already done the impossible twice. He co-created Google Maps.

He later became co-CEO of Salesforce, chaired Twitter's board during the messy Elon Musk takeover, and became chairman of OpenAI in late 2023. Clay Bavor spent 18 years at Google, most recently running its Labs group.

The two friends left their big jobs in 2023 to build something together. They picked customer service on purpose.

It is the least glamorous corner of software. Their logic was simple.

Companies hate their support costs. Customers hate waiting on hold.

AI that actually resolves problems could fix both sides at once. They launched Sierra in February 2024 with $110 million from Sequoia and Benchmark.

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.

Sierra

Sierra went straight for big brands, not tiny startups. SoFi, Ramp, ADT, Sonos and Brex signed up early.

By 2026 it said more than 40% of the Fortune 50 were customers. The trick was trust.

Enterprises are terrified of an AI bot going rogue and insulting a customer. So Sierra built guardrails and let each company control exactly how the agent behaves.

Bret Taylor's contacts did the rest. When the chairman of OpenAI calls a Fortune 500 CEO, they pick up.

Sierra hit $100 million in annual recurring revenue in November 2025, seven quarters after launch, and $150 million by February 2026. In April 2026 it launched Ghostwriter, which builds and deploys a new agent from a plain-English description.

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.

Sierra

Everyone is building AI customer service agents right now. Decagon has raised at a multi-billion valuation.

Salesforce, ServiceNow and Intercom all shipped their own agents. Sierra is not the cheap option.

It sells premium AI to giant companies that move slowly. The bigger risk sits underneath the product.

Sierra does not make its own AI models. It rents them from providers like OpenAI and Anthropic.

If those model makers decide to compete directly, Sierra's edge gets thin fast, and its co-founder chairing one of them only makes that question louder. A $15.8 billion valuation on roughly $150 million in revenue leaves almost no room for a stumble.

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.

Sierra

The core product is the Sierra AI agent. It is a branded customer service bot that companies deploy on their website, app, and phone lines.

It handles the boring stuff. Order tracking.

Refunds. Password resets.

Account changes. Sierra also built its own tools to test and monitor these agents so they do not make things up.

In 2024 the company added a voice version that talks to customers on the phone. In April 2026 it added Ghostwriter, a tool that builds and deploys a new agent from a plain-English description.

The whole pitch is one AI agent that carries your brand's name and never sleeps.

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.

Sierra

Sequoia Capital and Benchmark wrote the first checks at launch in February 2024, $110 million between them. Greenoaks Capital then led both the $175 million round in 2024 and the $350 million round in September 2025.

In May 2026 Tiger Global and Google's GV led a $950 million round at a $15.8 billion valuation, with Benchmark, Sequoia and Greenoaks back in. That takes total funding to about $1.6 billion.

That is partly the product and partly Bret Taylor. Investors bet on founders who have already built billion-dollar things.