Anodot vs Weaviate

Weaviate has a disclosed valuation of $200M, while Anodot'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 Anodot's $66M — a gap of $1.5M. Combined, the two companies have completed 7 known funding rounds.

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

Anodot was founded by David Drai in 2014. Weaviate was founded by Bob van Luijt in 2019. Visit each company's profile on Awaira for a full founder biography.

Anodot: Anodot provides autonomous business monitoring powered by AI, using time-series anomaly detection algorithms to continuously monitor business metrics including revenue, usage, and operational KPIs across enterprise data sources and alert teams to significant deviations before they become customer-visible incidents. The Herzliya company machine learning models learn seasonality, trends, and normal variance patterns for each metric automatically, reducing alert noise by filtering out expected variation and surfacing only genuine anomalies.\n\nThe company raised approximately $66 million in venture funding including a Series C from investors including Aleph VC, Redline Capital, and Transformative AI. Anodot serves telecommunications, fintech, gaming, and digital media clients including T-Mobile, Pandora, and Wix, deploying monitoring across billions of data points daily across client environments. The platform connects to data warehouses, streaming pipelines, and cloud monitoring sources through native integrations.\n\nAnodot competes in the AIOps and business monitoring market against Datadog, New Relic, and Sumo Logic for infrastructure monitoring use cases, and against business intelligence anomaly detection features in Tableau, Looker, and Power BI for business metric monitoring. Its focus on business-layer metric anomaly detection rather than infrastructure-layer monitoring differentiates it in the revenue operations and product analytics segments, where engineering-focused monitoring tools are insufficient for the metric breadth and business context that business operations teams require. 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.

Anodot got there first, launching in 2014 — that's 5 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.

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