Weights and Biases vs Cropin

Weights and Biases has a disclosed valuation of $1.3B, while Cropin'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 Cropin's $110M — a gap of $140M. Combined, the two companies have completed 6 known funding rounds.

Weights and Biases leads with an Awaira Score of 80/100, while Cropin sits at 70/100. That 10-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Weights and Biases was founded by Lukas Biewald in 2017. Cropin was founded by Krishna Kumar in 2010. Visit each company's profile on Awaira for a full founder biography.

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. Cropin: Cropin is a digital agriculture and crop intelligence platform that collects, processes, and analyzes data from satellite imagery, weather systems, IoT sensors, and farm management records to generate AI-powered crop performance analytics and risk assessments for agribusinesses, food companies, governments, and financial institutions with agricultural exposure. The platform enables enterprise-level visibility into crop production, supply chain traceability, and climate risk across large farmer networks.\n\nThe company raised approximately $110M in funding from investors including Google, ABC World Asia, and the Bill and Melinda Gates Foundation, and has built a platform covering tens of millions of acres of farmland across 60 countries. Cropin's satellite-based crop monitoring capabilities are used by multinational food companies to track sustainability commitments and supply chain traceability requirements.\n\nCropin occupies a unique position as a global agri-intelligence platform with deep roots in India and emerging market agriculture. The combination of Google investment and Gates Foundation support signals both commercial and development impact relevance, and the company's scale of farmland coverage creates a satellite and AI training data advantage that will be difficult for new entrants to replicate.

Cropin got there first, launching in 2010 — that's 7 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.

Weights and Biases has about 300 employees; Cropin has about 200-500. 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 Weights and Biases and Cropin compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.