Weaviate vs Databricks

By valuation, Databricks is the larger company at $134B versus $200M — a 670x difference. Size can also be measured by team: Weaviate employs 80 people while Databricks has 6,000 employees.

Databricks has raised more in total funding at $20.2B, compared to Weaviate's $67.5M — a gap of $20.1B. Combined, the two companies have completed 8 known funding rounds.

Databricks leads with an Awaira Score of 93/100, while Weaviate sits at 72/100. That 21-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Weaviate was founded by Bob van Luijt in 2019. Databricks was founded by Ali Ghodsi in 2013. Visit each company's profile on Awaira for a full founder biography.

Weaviate: Weaviate is a Netherlands-based vector database company founded in 2019 that enables organizations to build AI applications using vector search and semantic search capabilities. The platform stores, indexes, and searches unstructured data—including text, images, and audio—by converting them into vector embeddings, making it suitable for large language model applications and retrieval-augmented generation (RAG) systems. The core product is an open-source vector database with both community and enterprise versions. Weaviate allows developers to perform similarity searches and build knowledge graphs with semantic understanding. The platform integrates with machine learning frameworks and supports various embedding models, enabling organizations to power AI applications without extensive machine learning infrastructure expertise. Founded during the emergence of modern AI applications, Weaviate operates in the expanding vector database category competing with Pinecone, Milvus, and Qdrant. The company has raised $68 million across funding rounds with a valuation of $200 million as of its Series B stage. Weaviate serves use cases across e-commerce recommendation systems, content discovery, semantic search, and enterprise search applications. The company has gained adoption among developers and organizations building AI-powered products. Its open-source approach provides both community engagement and enterprise monetization pathways. The vector database market has experienced significant growth as organizations increasingly adopt large language models requiring efficient vector storage and retrieval infrastructure. Weaviate combines open-source accessibility with enterprise vector database capabilities positioned to capture growth in RAG and semantic search application development. 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 6 years of extra runway. Weaviate didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Weaviate has about 80 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 Weaviate and Databricks compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.