Dataloop vs Weaviate

Weaviate has a disclosed valuation of $200M, while Dataloop's valuation is not publicly available, making a direct size comparison difficult. Weaviate employs 80 people.

Weaviate has raised more in total funding at $67.5M, compared to Dataloop's $49M — a gap of $18.5M. Combined, the two companies have completed 6 known funding rounds.

Weaviate leads with an Awaira Score of 72/100, while Dataloop sits at 55/100. That 17-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Dataloop was founded by Eran Shlomo in 2017. Weaviate was founded by Bob van Luijt in 2019. Visit each company's profile on Awaira for a full founder biography.

Dataloop: Dataloop builds an AI data management and annotation platform that covers the full data pipeline for computer vision and NLP model development, combining data labelling tools, annotation workflow management, quality assurance automation, and model training integration in a single platform. The Tel Aviv company targets AI teams at enterprises and AI product companies that need to manage large-scale data labelling operations with quality controls and annotator workforce management.\n\nThe company raised approximately $49 million including a Series B round from investors including Viola Ventures and Samsung Next. Dataloop counts enterprise AI teams in automotive, retail, and agriculture verticals among its clients, with the platform used for large-scale computer vision dataset creation including bounding box annotation, semantic segmentation, and polygon annotation for object detection model training. The platform supports human-in-the-loop annotation workflows where model predictions are reviewed and corrected by human annotators to improve efficiency.\n\nDataloop competes in the data annotation and management market alongside Scale AI, Labelbox, Encord, and V7, as well as annotation services providers. The market has evolved from pure annotation tooling toward platforms that combine labelling with dataset management, model evaluation, and active learning capabilities. Dataloop positioning as a full data management platform rather than a labelling tool aims to capture more of the AI development workflow and reduce customer dependency on multiple point solutions across the data pipeline. 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.

Dataloop got there first, launching in 2017 — that's 2 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.

Dataloop has about 100-500 employees; Weaviate has about 80. 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 Dataloop and Weaviate compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.