Hyper Anna vs Weaviate

Weaviate has a disclosed valuation of $200M, while Hyper Anna'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 Hyper Anna's $10M — a gap of $57.5M. Combined, the two companies have completed 5 known funding rounds.

Weaviate leads with an Awaira Score of 72/100, while Hyper Anna sits at 38/100. That 34-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Hyper Anna was founded by Natalie Nguyen in 2015. Weaviate was founded by Bob van Luijt in 2019. Visit each company's profile on Awaira for a full founder biography.

Hyper Anna: Hyper Anna built an AI analytics assistant that enabled business users to ask questions about their data in natural language and receive charts, insights, and explanations without requiring SQL skills or data analyst support, applying natural language understanding and automated statistical analysis to business intelligence data from sales, marketing, and operations teams. The Sydney company positioned its product as a self-service AI analyst for non-technical business users.\n\nThe company raised approximately $10 million in Series A funding from investors including Sequoia Capital and Reinventure. Hyper Anna was acquired by National Australia Bank in 2020, integrating its conversational analytics technology into NAB internal data analytics tools for business banking and retail teams. The acquisition gave NAB a natural language interface for data exploration that reduced dependency on centralized data analyst resources for routine business reporting queries.\n\nHyper Anna competed in the AI business intelligence market against ThoughtSpot, Tableau Ask Data, and Microsoft Power BI Q&A, all of which added natural language query features to established BI platforms around the same period. The acquisition by NAB reflects the pattern of Australian financial institutions acquiring local AI startups to build internal analytical capabilities, rather than deploying international BI platform vendors for all data access needs. The natural language analytics market has since been transformed by large language model capabilities that enable more sophisticated analytical dialogue than the structured query approaches pioneered by first-generation NL BI tools. 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.

Hyper Anna got there first, launching in 2015 — that's 4 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.

Hyper Anna has about 1-50 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 Hyper Anna and Weaviate compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.