BreezoMeter vs Weights and Biases

Weights and Biases has a disclosed valuation of $1.3B, while BreezoMeter's valuation is not publicly available, making a direct size comparison difficult. Weights and Biases employs 300 people.

Weights and Biases has raised more in total funding at $250M, compared to BreezoMeter's $21M — a gap of $229M. Combined, the two companies have completed 8 known funding rounds.

Weights and Biases leads with an Awaira Score of 80/100, while BreezoMeter sits at 42/100. That 38-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

BreezoMeter was founded by Ran Korber in 2014. Weights and Biases was founded by Lukas Biewald in 2017. 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. Weights and Biases: Weights and Biases is a machine learning platform founded in 2017 that provides infrastructure for experiment tracking, model management, and collaboration in AI development. The company's core product enables data scientists and ML engineers to log, visualize, and compare machine learning experiments, addressing the reproducibility and collaboration challenges inherent in modern AI workflows. The platform integrates with popular ML frameworks including PyTorch, TensorFlow, and scikit-learn, allowing teams to track metrics, parameters, and outputs across training runs. W&B's offering extends to model registry capabilities, enabling organizations to version, document, and deploy models systematically. The company serves enterprises across computer vision, natural language processing, and reinforcement learning domains. As of its Series C funding stage, Weights and Biases has raised $250 million at a $1.3 billion valuation, positioning it among well-capitalized AI infrastructure startups. The company competes in the ML operations space alongside platforms like Databricks and Neptune, differentiating through its focus on experiment tracking and accessibility to individual practitioners and teams. Notable adoption spans research institutions and technology companies implementing large-scale ML pipelines. The platform's freemium model has facilitated rapid adoption within the academic and startup ecosystems, while enterprise offerings target organizations requiring advanced governance and integration capabilities. Growth trajectory reflects increasing enterprise demand for ML operations infrastructure. Weights and Biases occupies a critical position in the ML operations stack by specializing in experiment tracking and model management, essential infrastructure that bridges individual data scientist workflows and enterprise-scale ML deployment.

BreezoMeter got there first, launching in 2014 — that's 3 years of extra runway. Weights and Biases didn't arrive until 2017. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

BreezoMeter has about 1-50 employees; Weights and Biases has about 300. 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 BreezoMeter and Weights and Biases compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.