BreezoMeter vs Weaviate

Weaviate has a disclosed valuation of $200M, while BreezoMeter'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 BreezoMeter's $21M — a gap of $46.5M. Combined, the two companies have completed 3 known funding rounds.

Weaviate holds the higher Awaira Score at 72/100, compared to BreezoMeter's 42/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 30-point gap that reflects meaningful differences in scale or traction.

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

BreezoMeter: BreezoMeter provides AI-powered environmental data APIs covering air quality, pollen, weather, and wildfire risk, aggregating data from government monitoring stations, satellite imagery, and IoT sensors and applying machine learning models to produce street-level resolution environmental intelligence that application developers and enterprises integrate into health, insurance, and mobility products. The Haifa company delivers real-time and forecast environmental data at hyperlocal granularity that government monitoring networks cannot match.\n\nThe company raised approximately $21 million in venture funding and was acquired by Google in 2022, integrating its environmental data capabilities into Google Maps, Google Search, and Google Nest products. Prior to acquisition, BreezoMeter had built API customers including insurance companies for climate risk underwriting, asthma and respiratory health applications for personalised air quality alerts, and HVAC companies for air filtration product recommendations tied to local pollution levels.\n\nBreezoMeter competed in the environmental data and climate intelligence market alongside AirVisual (IQAir), PurpleAir, and government air quality data portals. The acquisition by Google reflected the strategic value of hyperlocal environmental data as an enrichment layer for mapping, local search, and home automation products where air quality and pollen information enhances the relevance of location-based recommendations. The combination of machine learning interpolation and sensor fusion to generate street-level environmental data from sparse monitoring networks represented a technically defensible approach that aligned with Google investment in location data quality. 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.

BreezoMeter was founded first in 2014, giving it 5 years of additional market experience. Weaviate was founded later in 2019. In AI, even a year or two of head start can translate into significantly more training data, customer relationships, and institutional knowledge.

BreezoMeter has approximately 1-50 employees, while Weaviate has approximately 80. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, BreezoMeter and Weaviate are direct competitors — both operate in the AI Data space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.