Coralogix vs Weights and Biases

By valuation, Weights and Biases is the larger company at $1.3B versus $1B — a 1.3x difference. Size can also be measured by team: Coralogix employs 100-500 people while Weights and Biases has 300 employees.

Coralogix has raised more in total funding at $350M, compared to Weights and Biases's $250M — a gap of $100M. Combined, the two companies have completed 11 known funding rounds.

Weights and Biases leads with an Awaira Score of 80/100, while Coralogix sits at 68/100. That 12-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Coralogix was founded by Ariel Assaraf in 2015. Weights and Biases was founded by Lukas Biewald in 2017. Visit each company's profile on Awaira for a full founder biography.

Coralogix: Coralogix provides a machine learning-powered log analytics and observability platform that uses AI to reduce the volume of logs requiring full indexing and storage, applying streaming ML analysis to detect patterns, anomalies, and insights in log data without requiring all data to be indexed at query time. The Tel Aviv and San Francisco company stateful streaming approach reduces observability costs significantly compared to traditional log management tools that index and store all data regardless of utility.\n\nThe company raised approximately $270 000 000 including a Series D round from investors including Brighton Park Capital, Greenfield Partners, and Telstra Ventures. Coralogix counts technology companies and enterprises across cloud-native software development, fintech, and SaaS as clients, competing on cost efficiency relative to Datadog, Splunk, and New Relic for engineering teams with high log ingestion volumes and constrained observability budgets.\n\nCoralogix competes in the log management and observability market where Datadog, Elastic, and Grafana hold dominant positions. Its cost optimization positioning resonates with engineering teams managing rapid data growth who face steeply rising observability bills from usage-based pricing models at incumbent vendors. The company TCO reduction messaging is backed by architectural decisions that trade query flexibility at indexing time for streaming ML analysis that surfaces issues without storing all log data, a trade-off that appeals to cost-conscious engineering operations teams at high-growth software companies. 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.

Coralogix got there first, launching in 2015 — that's 2 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.

Coralogix 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 Coralogix 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.