Dataweave vs Pinecone

Pinecone has a disclosed valuation of $750M, while Dataweave's valuation is not publicly available, making a direct size comparison difficult. Pinecone employs 150 people.

Pinecone has raised more in total funding at $138M, compared to Dataweave's $22M — a gap of $116M. Combined, the two companies have completed 6 known funding rounds.

Pinecone leads with an Awaira Score of 76/100, while Dataweave sits at 55/100. That 21-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Dataweave was founded by Krishnamurthy Rengarajan in 2011. Pinecone was founded by Edo Liberty in 2019. Visit each company's profile on Awaira for a full founder biography.

Dataweave: Dataweave is a retail price intelligence and competitive data platform that continuously monitors millions of product listings across e-commerce marketplaces, brand websites, and retail channels, using AI to deliver real-time pricing analytics, assortment insights, and promotion tracking to retailers and brand manufacturers. The platform helps merchandising and pricing teams make data-driven decisions to stay competitive on digital shelves.\n\nThe company raised approximately $22M in Series B funding and serves major global retailers, consumer goods companies, and marketplaces that need systematic visibility into competitor pricing and shelf positioning. Dataweave processes billions of data points daily and provides structured analytics that replace manual price monitoring spreadsheets.\n\nAs e-commerce has shifted pricing from periodic catalog decisions to continuous dynamic optimization, the need for automated competitive price intelligence has become a core retail technology investment. Dataweave's decade of accumulated retail data and processing infrastructure gives it a scale advantage over newer entrants in the competitive intelligence category. 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.

Dataweave got there first, launching in 2011 — that's 8 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.

Dataweave has about 100-500 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 Dataweave and Pinecone compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.