Weights and Biases vs Databricks

By valuation, Databricks is the larger company at $134B versus $1.3B — a 107.2x difference. Size can also be measured by team: Weights and Biases employs 300 people while Databricks has 6,000 employees.

Databricks has raised more in total funding at $20.2B, compared to Weights and Biases's $250M — a gap of $20B. Combined, the two companies have completed 10 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Weights and Biases sits at 80/100. That 13-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Weights and Biases was founded by Lukas Biewald in 2017. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Weights and Biases: Weights and Biases is a machine learning platform founded in 2017 that provides infrastructure for experiment tracking, model management, and collaboration in AI development. The company's core product enables data scientists and ML engineers to log, visualize, and compare machine learning experiments, addressing the reproducibility and collaboration challenges inherent in modern AI workflows. The platform integrates with popular ML frameworks including PyTorch, TensorFlow, and scikit-learn, allowing teams to track metrics, parameters, and outputs across training runs. W&B's offering extends to model registry capabilities, enabling organizations to version, document, and deploy models systematically. The company serves enterprises across computer vision, natural language processing, and reinforcement learning domains. As of its Series C funding stage, Weights and Biases has raised $250 million at a $1.3 billion valuation, positioning it among well-capitalized AI infrastructure startups. The company competes in the ML operations space alongside platforms like Databricks and Neptune, differentiating through its focus on experiment tracking and accessibility to individual practitioners and teams. Notable adoption spans research institutions and technology companies implementing large-scale ML pipelines. The platform's freemium model has facilitated rapid adoption within the academic and startup ecosystems, while enterprise offerings target organizations requiring advanced governance and integration capabilities. Growth trajectory reflects increasing enterprise demand for ML operations infrastructure. Weights and Biases occupies a critical position in the ML operations stack by specializing in experiment tracking and model management, essential infrastructure that bridges individual data scientist workflows and enterprise-scale ML deployment. Databricks: Databricks is an AI and data platform founded in 2013 that provides a unified analytics workspace for data engineering, data science, and machine learning. The company developed Databricks Lakehouse, which combines data lake and data warehouse capabilities, built on Apache Spark technology. Its platform enables organizations to process large-scale data, build machine learning models, and deploy AI applications through a single interface. The company offers several core products: Databricks SQL for analytics, Databricks Machine Learning for model development, and Databricks Jobs for workflow automation. The platform supports multi-cloud deployment across AWS, Azure, and Google Cloud. Databricks serves enterprises across various industries, with customers including organizations in financial services, technology, and healthcare sectors. As of its latest funding round, Databricks has raised $11.2 billion in total funding and maintains a valuation of $134 billion, positioning it among the highest-valued private AI and data companies. The company achieved Series J funding status, indicating significant capital accumulation and investor confidence. Databricks competes with platforms like Snowflake, Teradata, and cloud-native data solutions from major hyperscalers. The company's growth trajectory reflects strong market demand for integrated data and AI infrastructure, driven by increasing enterprise adoption of machine learning and data-driven decision-making. Databricks unified the traditionally separate data warehouse and data lake approaches through its Lakehouse architecture, creating a single platform for analytics and AI workflows.

Databricks got there first, launching in 2013 — that's 4 years of extra runway. Weights and Biases didn't arrive until 2017. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Weights and Biases has about 300 employees; Databricks has about 6,000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Weights and Biases and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.