Databricks

Databricks sits at the intersection of data infrastructure and AI, building the platform that thousands of enterprises use to store, process, and analyze their data — and increasingly, to train AI models on that data. The company's valuation reached $134 billion in its December 2025 Series L round, making it one of the most valuable private companies in technology.

Databricks' journey into AI accelerated with the $1.3 billion acquisition of MosaicML in 2023, which gave them the team and technology to build DBRX, their open-source foundation model. DBRX uses a mixture-of-experts architecture and cost only $10 million to create — demonstrating Databricks' belief that efficient, data-driven model training matters more than raw compute spending.

With revenue approaching $4.8 billion annually and over 10,000 enterprise customers, Databricks processes some of the world's most valuable enterprise data. Their Data Intelligence Platform integrates data lakehouse storage with generative AI capabilities, enabling enterprises to build custom AI models on their own data.

For data sellers, Databricks represents a unique buyer because they both need data for their own models and serve as an infrastructure layer through which their enterprise customers consume data. This creates two-sided demand.

Databricks' position in the AI ecosystem is uniquely strategic. They are not just building AI models — they are building the platform on which thousands of enterprises build, train, and deploy their own AI models. This means Databricks has visibility into the data needs and patterns of the entire enterprise AI market, giving them unparalleled insight into which data types are most valuable and most scarce.

The company's acquisition strategy further strengthens their position. The MosaicML acquisition ($1.3 billion) brought model training expertise. The Tabular acquisition ($600 million+) brought the creators of Apache Iceberg, the leading open table format for data lakehouse architecture. These acquisitions build a vertically integrated stack from data storage through model training, and each layer benefits from better training data.

Databricks' customer base includes many of the world's most data-intensive organizations: financial institutions processing billions of transactions, healthcare systems managing millions of patient records, retailers analyzing purchasing patterns across thousands of stores, and technology companies running massive data pipelines. Each of these customers represents both a potential data partner and a use case that drives Databricks' model training priorities.

Databricks' data strategy is fundamentally different from consumer AI companies. While OpenAI and Google optimize for broad general knowledge, Databricks optimizes for making enterprise data useful for AI.

The MosaicML acquisition brought world-class expertise in efficient model training. DBRX was designed to be trained on curated, high-quality data rather than simply crawling the entire internet. This philosophy means Databricks is willing to pay premium prices for well-curated, domain-specific datasets.

Databricks' platform processes exabytes of enterprise data daily, and their Unity Catalog provides data governance across the entire pipeline. This infrastructure means they understand data quality, metadata management, and data lineage better than most AI companies — and they evaluate potential data acquisitions with that sophistication.

The Mistral AI investment and integration reflects Databricks' strategy of bringing multiple model providers into their platform. As their platform hosts more models, the demand for diverse training data increases.

Databricks also generates unique data through their own platform — SQL queries, data engineering workflows, notebook patterns, and ML experiment metadata from thousands of enterprise customers. This telemetry data is used to improve their AI assistants and code generation capabilities.

Databricks' Unity Catalog provides a unified governance layer for all data within an enterprise — structured data, unstructured data, AI models, and feature stores. This technology gives Databricks unique insight into how enterprises organize, govern, and use their data for AI applications. The patterns they observe across thousands of customers inform their own model training priorities.

The company's open-source commitment (DBRX, Apache Spark, Delta Lake, MLflow) creates community goodwill that translates into data partnerships. Academic researchers, open-source contributors, and enterprise users who benefit from Databricks' open-source contributions are often willing to share data or participate in research collaborations.

Databricks' approach to training DBRX was notable for its efficiency. By spending just $10 million to create a competitive open-source model, Databricks demonstrated that smart data curation can substitute for massive compute spending. This philosophy means they are willing to pay premium prices for expertly curated, high-quality datasets rather than acquiring data in bulk.

Databricks' acquisition of Tabular — the company behind Apache Iceberg — was partly a data strategy play. Iceberg's open table format is becoming the standard for data lakehouse storage, and as more enterprises adopt Iceberg, the metadata and query patterns generated by these tables provide training data for Databricks' AI features. The acquisition ensures Databricks has privileged access to the evolution of this critical data standard.

Databricks' data needs reflect their dual role as both an AI model builder and an enterprise data platform.

SQL and data engineering data is a core need. Query patterns, database schemas, data pipeline configurations, and ETL workflow documentation help Databricks build better AI assistants for data engineers and analysts. Real-world SQL query logs from enterprise environments are particularly valuable.

Code repositories focused on data science, ML engineering, and data infrastructure help improve Databricks' code generation and notebook assistance features. Python, Scala, SQL, and R codebases with ML/data science context are in high demand.

Enterprise domain data across finance, healthcare, manufacturing, and other verticals helps Databricks demonstrate the value of their platform to potential customers and improves their models' performance on industry-specific tasks.

Technical documentation — API references, architecture guides, and infrastructure documentation — improves Databricks' ability to help users navigate complex technical environments.

Data governance and compliance documentation helps Databricks improve Unity Catalog and their data management capabilities. Policies, audit logs, and compliance frameworks from regulated industries are valuable.

Data catalog and metadata information — including table schemas, column descriptions, data lineage, and data quality metrics — helps Databricks build better AI assistants for data management. Real-world examples of how enterprises organize and document their data are particularly valuable.

ML experiment data — including model architectures, hyperparameters, training curves, and evaluation results — helps Databricks improve Mosaic AI's model recommendation and AutoML capabilities. Data about what model configurations work best for what types of tasks is uniquely valuable.

Industry-specific analytics patterns — how financial analysts query data, how healthcare researchers structure studies, how retailers analyze customer behavior — help Databricks build domain-specific AI features into their platform.

Data quality and observability data — including data freshness metrics, schema drift logs, anomaly detection results, and data validation rules — helps Databricks improve their data quality monitoring products. As enterprises increasingly recognize that AI model quality depends on data quality, tools that automatically detect and remediate data issues become more valuable.

Business intelligence query patterns — how analysts structure dashboards, what metrics they track, how they slice data — help Databricks build AI assistants that can anticipate analytical needs and generate insights proactively.

FileYield connects data owners with Databricks' data procurement team. Given Databricks' sophisticated understanding of data quality, they are one of the most discerning buyers in the market — and they pay accordingly.

Submit a data appraisal through FileYield. Datasets related to data engineering, SQL, ML workflows, and enterprise domain data are particularly strong matches. Our team provides a valuation within 48 hours.

Databricks evaluates data with the same rigor they bring to their own data platform. Expect thorough quality assessments that consider data lineage, metadata richness, and potential biases. Deals are structured as licensing agreements with terms appropriate for enterprise use.

Databricks' technical evaluation process is among the most sophisticated in the industry. Their team includes ML engineers, data engineers, and domain experts who assess datasets against specific quality metrics and benchmark improvements. This rigor means deals may take slightly longer to close, but the resulting valuations tend to be fair and well-justified.

FileYield has established relationships with Databricks' data team and can facilitate introductions to the specific product group most relevant to your data. Whether your dataset is best suited for DBRX training, Unity Catalog improvement, or Mosaic AI fine-tuning, we ensure it reaches the right team.

Founded: 2013 Headquarters: San Francisco, California CEO: Ali Ghodsi Employees: 7,000+

Valuation: $134 billion (December 2025, Series L) Total Funding: $18+ billion Key Investors: Thrive Capital, a16z, DST Global, BlackRock, T. Rowe Price

Revenue: $4.8 billion annualized run rate (2025) Customers: 10,000+ enterprises

Key Products: Data Intelligence Platform, Unity Catalog, DBRX (open-source LLM), Mosaic AI Acquisitions: MosaicML ($1.3B), Tabular ($600M+)

Databricks is the dominant enterprise data platform and an increasingly important AI model builder. Their combination of data infrastructure expertise and model training capabilities makes them a sophisticated and well-paying buyer of training data.

Open-Source Leadership: Databricks created or maintains Apache Spark, Delta Lake, MLflow, and other foundational open-source data and AI tools used by millions of data professionals worldwide. This open-source commitment creates goodwill and network effects that drive enterprise adoption.

IPO Expectations: Market observers expect Databricks to pursue an IPO in 2026-2027, which could value the company at $150-200 billion or more. This anticipated public offering creates strong incentives to grow revenue and capabilities rapidly, including through data acquisition.