Snowflake, Anthropic ink $200M AI agents deal❄️💰

Let's also talk about multimodal databases

Author

Gladstone Benjamin
January 12, 2026

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What’s in today’s newsletter:

Also, check out the weekly Deep Dive - Multimodal Databases

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SNOWFLAKE

TL;DR: Snowflake and Anthropic partnered in a $200M deal to integrate AI agents into Snowflake’s cloud platform, enhancing data workflows, automation, and decision-making, advancing AI's role in cloud data management.

  • Snowflake and Anthropic have partnered in a USD 200 million deal to develop AI agents for cloud data enhancements.

  • Anthropic's AI models will be integrated within Snowflake's platform to optimize data workflows and automate tasks.

  • The collaboration aims to improve data insights, decision-making, and business efficiency through advanced AI-driven analysis.

  • This partnership marks a significant advance in embedding AI in cloud infrastructures, influencing future AI-cloud integrations.

Why this matters: This $200M deal merges AI safety expertise with cloud data power, revolutionizing data analytics by integrating AI agents directly into Snowflake’s platform. It promises enhanced automation, deeper insights, and more efficient decision-making, setting a benchmark for future AI-cloud partnerships that will transform industries reliant on large-scale data processing.

TL;DR: Snowflake is evolving from a cloud data warehouse into an AI platform, enhancing AI capabilities and analytics, earning an "Outperform" rating and positioning for growth and stronger market competition.

  • Snowflake is evolving from a cloud data warehouse into a comprehensive AI and machine learning platform.

  • Raymond James rates Snowflake as "Outperform" due to its strategic AI-focused transformation.

  • New features support AI model deployment, data collaboration, and advanced analytics in a secure cloud environment.

  • This shift could expand Snowflake’s market, boost revenue growth, and improve competitive positioning.

Why this matters: Snowflake's AI-driven transformation signals a pivotal shift, enabling clients to seamlessly integrate AI with data management. This broadens its market reach and competitive edge, reflecting growing investor confidence and positioning Snowflake for significant revenue growth in the expanding AI technology landscape.

TL;DR: Snowflake Cortex AI and Sigma AI apps automate and simplify last-mile analytics, enabling business users to easily access insights, boosting collaboration, agility, and data-driven decisions without heavy reliance on data scientists.

  • Snowflake Cortex AI automates data preparation and insight generation, simplifying last-mile analytics for users.

  • Sigma AI apps enable intuitive interaction with data via AI-enhanced dashboards and natural language queries.

  • These tools reduce reliance on data scientists, improving accessibility for business professionals in decision-making.

  • Integration of Cortex AI and Sigma AI apps enhances organizational agility and collaboration across technical and non-technical teams.

Why this matters: By automating insights and enabling natural language queries, Snowflake Cortex AI and Sigma AI apps democratize analytics, empowering non-technical users to make faster, data-driven decisions. This reduces dependence on specialists, enhances agility, and strengthens collaboration, ultimately driving better outcomes and competitive advantage for organizations.

VECTOR AND GRAPH DATABASES

Courtesy: ChatGPT

TL;DR: Graph databases enable efficient, incremental updates and dynamic adaptability in AI knowledge bases, while vector search excels at semantic matching but struggles with updates; hybrid models may offer optimal AI performance.

  • Graph databases use nodes and edges to efficiently update and query interconnected AI knowledge bases incrementally.

  • Vector search excels at semantic similarity but struggles with dynamic updates due to costly retraining requirements.

  • Graph databases improve AI’s real-time adaptability, enhancing decision-making in recommendation engines and virtual assistants.

  • Hybrid solutions combining graph databases and vector search could optimize scalability and responsiveness in AI systems.

Why this matters: Efficiently updating AI knowledge bases with graph databases enhances real-time adaptability and decision-making, crucial for dynamic applications. Vector search excels in semantic matching but lags in updates, so combining both can create scalable, responsive AI systems that better handle evolving information and complex relationships.

RELATIONAL DATABASE

TL;DR: DuckDB now directly queries Apache Iceberg tables on AWS S3, simplifying scalable cloud analytics by eliminating complex ETL and enabling cost-effective, interoperable workflows with local analytic tools.

  • DuckDB now supports direct querying of Apache Iceberg tables stored on AWS S3 via an updated browser.

  • This integration eliminates complex ETL processes, enabling efficient, scalable cloud data analytics with local tools.

  • The feature uses AWS S3 native protocols, bridging local analytic databases and cloud-native data lakes seamlessly.

  • It promotes cost-effective, interoperable analytics workflows aligned with industry trends toward open formats and cloud storage.

Why this matters: Integrating DuckDB with Apache Iceberg and AWS S3 simplifies cloud analytics by enabling direct queries on large datasets without ETL overhead. This innovation supports cost-effective, scalable, and interoperable workflows, empowering analysts to leverage local tools while accessing cloud storage, accelerating adoption of open data formats and cloud-native solutions.

EVERYTHING ELSE IN CLOUD DATABASES

DEEP DIVE

Multimodal Databases

I hearken back sometimes to the good old days where daily work life entailed looking after about 50 SQL Server instances and a few Oracle instances, on top of any other projects that came my way. It was a relational database world I lived in, as did most others.

But for me, possibly around 2019, I began to notice other types of databases coming to the forefront, particularly MongoDB. Now, as we embark upon 2026, I am encountering a deluge of all manner of databases, table formats, and file formats.

The reason my mind is on multimodal databases is that I was doing some research on one in particular this week, from a business and technological standpoint.

It is interesting, that in 7 years, the database landscape has changed so drastically. If you don’t keep up to modern trends, you might as well work with CICS and JCL. That may sound a little harsh but there is no end to the torrent of new and emerging databases.

If you don’t believe me? Just take a look at DB-Engines and Database of Databases and you will find over a thousand database offerings between the two.

Now getting back to multimodal databases.

Without making this weeks newsletter too long, Here are some of the big-name multimodal databases that I have my eye one and keeping close track of:

Also, take a look at this research. I would implore you to get familiar with these database platforms, as they are not just some ephemeral technology that will disappear soon.

One final thing. Don’t confuse multimodal with multi-model. That is a nuance and subtlety I will discuss next week.

Gladstone Benjamin