Dust vs Weights & Biases
Side-by-side comparison of AI visibility scores, market position, and capabilities
Paris-based AI assistant platform for teams; raised $21.5M total (Sequoia-led); hit $7.3M ARR with 66-person team; connects company data to LLMs securely
About
Dust is a Paris-based enterprise AI assistant platform founded to help teams connect their internal knowledge and data to large language models in a secure, governed way. The company was founded by former Stripe engineers who saw that the core challenge in deploying AI at work was not the model itself but the data integration layer — making company knowledge accessible to AI without creating security and compliance risks. Dust's platform allows organizations to connect data sources like Notion, Slack, GitHub, Google Drive, and custom internal tools into a unified knowledge layer that powers AI assistants tuned for the company's specific context.\n\nDust's product enables teams to build and deploy custom AI agents — internally called "assistants" — that can answer questions, summarize documents, draft communications, and complete workflows using the company's proprietary data. The platform includes role-based access controls, ensuring that AI assistants only surface information appropriate for the requesting user's permissions. Customers use Dust to replace fragmented AI experiments across different departments with a centralized, IT-governed deployment that scales across the organization.\n\nDust raised $21.5M in total funding, including a Sequoia-led round, and reached $7.3M ARR with a 66-person team — a capital-efficient growth profile that reflects the founders' engineering discipline. The company competes in the enterprise AI assistant market against Microsoft Copilot, Glean, and Notion AI, differentiating through its developer-friendly customization, European data residency options, and focus on mid-market companies that need enterprise-grade governance without the complexity of large-company deployments.
MLOps platform with $1.25B valuation used by OpenAI and NVIDIA; experiment tracking, model versioning, and LLM evaluation competing with MLflow and Comet for AI development teams.
About
Weights & Biases (W&B) is the leading MLOps and AI developer platform for tracking machine learning experiments, visualizing training runs, managing model versions, and evaluating AI model performance — providing infrastructure that data scientists and ML engineers use to build, train, and deploy machine learning models systematically. Founded in 2018 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis in San Francisco, Weights & Biases has raised approximately $250 million at a $1.25 billion valuation and is used by major AI labs and enterprise ML teams including OpenAI, NVIDIA, and Samsung.\n\nW&B's core product Wandb (the MLOps platform) provides experiment tracking that automatically logs model hyperparameters, training metrics, hardware utilization, and output artifacts — enabling data scientists to compare hundreds of training runs, identify which configurations produce better results, and reproduce experiments months later. Artifacts manages model versioning and dataset versioning with lineage tracking. Sweeps automates hyperparameter optimization by running parallel experiments across configuration spaces.\n\nIn 2025, Weights & Biases has evolved from experiment tracking into a comprehensive AI development platform — W&B Prompts addresses LLM prompt versioning and evaluation, W&B Launch enables compute-agnostic ML job orchestration, and W&B Reports provides narrative-rich ML research documentation. The company competes with MLflow (open-source, Databricks), Comet ML, Neptune.ai, and AWS SageMaker Experiments for MLOps platform share. W&B's 2025 strategy focuses on the AI era — expanding its LLM evaluation capabilities (comparing outputs across model versions and prompts), growing its enterprise adoption among companies fine-tuning foundation models, and deepening integrations with major GPU cloud providers (CoreWeave, Lambda Labs, Together AI) where AI training is concentrated.
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