Dataloop vs Weights and Biases

Weights and Biases has a disclosed valuation of $1.3B, while Dataloop'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 Dataloop's $49M — a gap of $201M. Combined, the two companies have completed 8 known funding rounds.

Weights and Biases leads with an Awaira Score of 80/100, while Dataloop sits at 55/100. That 25-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Dataloop was founded by Eran Shlomo in 2017. Weights and Biases was founded by Lukas Biewald in 2017. Visit each company's profile on Awaira for a full founder biography.

Dataloop: Dataloop builds an AI data management and annotation platform that covers the full data pipeline for computer vision and NLP model development, combining data labelling tools, annotation workflow management, quality assurance automation, and model training integration in a single platform. The Tel Aviv company targets AI teams at enterprises and AI product companies that need to manage large-scale data labelling operations with quality controls and annotator workforce management.\n\nThe company raised approximately $49 million including a Series B round from investors including Viola Ventures and Samsung Next. Dataloop counts enterprise AI teams in automotive, retail, and agriculture verticals among its clients, with the platform used for large-scale computer vision dataset creation including bounding box annotation, semantic segmentation, and polygon annotation for object detection model training. The platform supports human-in-the-loop annotation workflows where model predictions are reviewed and corrected by human annotators to improve efficiency.\n\nDataloop competes in the data annotation and management market alongside Scale AI, Labelbox, Encord, and V7, as well as annotation services providers. The market has evolved from pure annotation tooling toward platforms that combine labelling with dataset management, model evaluation, and active learning capabilities. Dataloop positioning as a full data management platform rather than a labelling tool aims to capture more of the AI development workflow and reduce customer dependency on multiple point solutions across the data pipeline. 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.

Both Dataloop and Weights and Biases launched in 2017. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Dataloop has about 100-500 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 Dataloop 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.