Databricks vs Pinecone

By valuation, Databricks is the larger company at $134B versus $750M — a 178.7x difference. Size can also be measured by team: Databricks employs 6,000 people while Pinecone has 150 employees.

Databricks has raised more in total funding at $20.2B, compared to Pinecone's $138M — 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 Pinecone sits at 76/100. That 17-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Databricks was founded by Ali Ghodsi in 2013. Pinecone was founded by Edo Liberty in 2019. Visit each company's profile on Awaira for a full founder biography.

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. Pinecone: Pinecone is a vector database platform founded in 2019 that enables developers to build and deploy AI applications using vector embeddings. The company provides a fully managed cloud service that indexes and searches high-dimensional vector data at scale, serving as critical infrastructure for retrieval-augmented generation (RAG) and other machine learning workflows. Pinecone's core offering allows applications to store, index, and query embeddings generated from text, images, and other data types, facilitating semantic search and similarity matching capabilities essential for modern AI systems. The platform operates in the rapidly expanding vector database market, competing with alternatives including Weaviate, Milvus, and cloud provider solutions. Pinecone's approach emphasizes ease of use through a serverless architecture that abstracts infrastructure complexity, targeting developers building generative AI applications without deep database expertise. The company has achieved significant adoption among enterprises and startups applying large language models for production systems. As of the latest disclosed information, Pinecone has raised $138 million in funding and holds a valuation of $800 million. The company remains in Series B stage, indicating substantial market validation while operating in the pre-profitability phase typical of infrastructure software companies. Its growth trajectory reflects increasing demand for vector storage solutions as enterprises integrate AI models into production workflows. Pinecone's serverless vector database architecture uniquely abstracts infrastructure complexity for developers building retrieval-augmented generation systems at scale.

Databricks got there first, launching in 2013 — that's 6 years of extra runway. Pinecone didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Databricks has about 6,000 employees; Pinecone has about 150. 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 Databricks and Pinecone compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.