Dust vs Snowflake
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.
Cortex AI platform for enterprise LLM deployment within the data cloud; $900M+ ARR from AI/ML workloads. AI Data Cloud serves 10,000+ enterprise customers. Cortex Analyst, Cortex Search enable natural-language querying of enterprise data.
About
Snowflake was founded in 2012 by data warehousing veterans from Oracle with the mission of building a data platform designed from scratch for the cloud — one that separated compute from storage to enable elastic scaling, multi-cloud portability, and a consumption-based pricing model that aligned cost with actual use. The company identified that legacy data warehouses required customers to over-provision hardware for peak demand, creating enormous waste, and that the emerging cloud infrastructure layer made a fundamentally different architectural approach possible. Snowflake's core technology, the Data Cloud, provides a single platform for data warehousing, data lakes, data engineering, data science, and data sharing across AWS, Azure, and Google Cloud.\n\nSnowflake's platform has expanded beyond structured analytics into an AI and machine learning infrastructure layer through Cortex AI — a suite of capabilities that allows enterprises to build, deploy, and serve LLM-powered applications directly on their Snowflake data without moving data to external AI platforms. Cortex AI includes LLM fine-tuning, vector search, and inference APIs that integrate with leading foundation models, enabling enterprises to build RAG applications and AI agents on top of their governed Snowflake data. Snowflake serves more than 10,000 enterprise customers globally, including the majority of the Fortune 500, across industries from financial services and healthcare to retail and media.\n\nSnowflake's AI and ML workloads generate over $900 million in annualized revenue, one of the fastest-growing segments of its business. The company trades on NYSE as SNOW and competes with Databricks, Google BigQuery, and Amazon Redshift. Its enterprise penetration, multi-cloud neutrality, and the Cortex AI platform position Snowflake as a foundational layer for enterprise AI deployment where data governance and security are non-negotiable.
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