TomTom vs Weights and Biases

By valuation, Weights and Biases is the larger company at $1.3B versus $740M — a 1.7x difference. Size can also be measured by team: TomTom employs 1000+ people while Weights and Biases has 300 employees.

Weights and Biases has raised $250M in disclosed funding across 5 known rounds. TomTom's funding history is not publicly available.

Weights and Biases leads with an Awaira Score of 80/100, while TomTom sits at 62/100. That 18-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

TomTom was founded by Harold Goddijn in 1991. Weights and Biases was founded by Lukas Biewald in 2017. Visit each company's profile on Awaira for a full founder biography.

TomTom: TomTom is a Dutch location technology company that develops AI-powered mapping, navigation, and real-time traffic data products for automotive OEMs, fleet operators, and enterprise location services customers. The Amsterdam-listed company has evolved from consumer GPS devices into a B2B data and software business, supplying digital map data, live traffic intelligence, and navigation APIs to vehicle manufacturers for in-car systems and to enterprise developers building location-aware applications.\n\nPublicly traded on Euronext Amsterdam, TomTom divested its telematics business to focus on its digital map and automotive software divisions, partnering with Microsoft to build a next-generation AI map platform announced in 2023. The company supplies map data that underlies location services from a significant share of global automotive brands and maintains a map coverage network spanning over 185 countries. TomTom HD Maps and its RoadDNA localisation technology are positioned as foundational infrastructure for autonomous vehicle development.\n\nTomTom competes in the mapping data market against HERE Technologies, Google Maps Platform, and Mapbox, with differentiation in the automotive OEM segment where its decades of vehicle integration experience give it established supply chain relationships. The company is navigating a transition period in which traditional navigation revenue is declining as smartphones displace in-car navigation units, while automotive OEM software platforms and AI mapping represent the primary growth vectors. Its Microsoft collaboration for AI-enhanced mapping tools reflects a strategic bet on integrating large language model capabilities into location intelligence products. 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.

TomTom got there first, launching in 1991 — that's 26 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.

TomTom has about 1000+ 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 TomTom 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.