Snowflake’s Next Act?

Snowflake is done merely storing your data; it wants to think with it.

At Snowflake Summit 2025 in San Francisco, CEO Sridhar Ramaswamy set the tone early, saying, “There is no AI strategy without a data strategy.” The message? Snowflake no longer wants to be seen as just a warehouse. It’s now laying the rails for AI-native enterprises, ones that can automate, reason, and build faster across the whole stack.

Joining him on stage, OpenAI CEO Sam Altman didn’t mince words either. “The technology is ready for mainstream use,” he said. “We’ve reached an inflection point where the models are reliable enough for enterprise use.”

Ramaswamy’s message was clear—stay curious and experiment. Enterprise AI is accessible, and Snowflake wants companies to begin using it without hesitation.

Altman doubled down. “What we’re seeing with enterprises and AI is the people that are making the early bets and iterating very quickly are doing much better than the people that are waiting to see how it’s all going to shake out.”

This year, Snowflake laid out its most ambitious reinvention yet, one that positions it at the centre of enterprise AI infrastructure. From ingest to intelligence, every layer of the stack saw a product shift targeting developers, data engineers, and AI agents alike.

Snowflake Bets on Agentic AI

The company’s biggest pivot yet is built around agent-first infrastructure. The new Snowflake Intelligence and Data Science Agent tools bring LLMs directly into the platform’s core experience.

Snowflake Intelligence empowers anyone, even those without technical expertise, to effortlessly query both structured and unstructured data using plain English. Powered by OpenAI and Anthropic, it operates within customer environments, respecting existing governance layers. Plus, it seamlessly connects to external content through Cortex Knowledge Extensions, with sources like Stack Overflow, CB Insights, and Packt already on board.

The Data Science Agent automates tasks like feature engineering and model training using Anthropic’s Claude. It produces validated code, trims the debug loop, and lets teams focus on model refinement rather than boilerplate.

Another release, AISQL, pulls AI directly into SQL, making functions like sentiment analysis or file processing native to analysts.

​“In 2024, we started seeing [AI] systems going into production…and then in this year, we expect to see a lot of value in terms of these systems going in production,” Vijayant Rai, managing director, Snowflake, told AIM. 

Before we discuss the announcements in detail, let’s take a look at the top stories of the week. 

• As AI tools like ChatGPT and Perplexity reshape how people discover content, traditional SEO is giving way to generative engine optimisation (GEO). It’s no longer about climbing Google’s ranks—it’s about being remembered by language models. Full story here. 

• IIT Kanpur is emerging as a powerhouse for deep tech startups, driven by its product-first incubation model and strong public-private partnerships. Through SIIC, one of India’s oldest tech incubators, it supports over 100 startups across AI, defence, healthcare, and more, offering funding, mentorship, and international scale-up opportunities. Click here to read the full article. 

• Bengaluru’s Outer Ring Road is emerging as India’s top GCC hub, with tech giants like Google, Amazon, and NVIDIA clustered across a 31-km stretch. Among the IT parks, Embassy TechVillage leads the charge, backed by strong infrastructure, Metro access, and a growing network of global firms. Experts say ORR offers the right mix of scale, talent, and innovation to power next-gen tech growth. Read the full article here. 

Feeding the Stack with Openflow

Following its acquisition of Datavolo, Snowflake introduced Openflow, its new multimodal data ingestion layer built on Apache NiFi, to keep the AI engine running.

Openflow handles both batch and streaming data, offers hundreds of prebuilt connectors (Oracle, Salesforce, Box, Google Ads), and allows fully custom workflows. It supports Snowflake’s “Bring Your Own Cloud” model, reducing reliance on tools like Airflow or Talend and consolidating ingestion inside Snowflake.

The theme here is clear—Snowflake wants to be the first stop for all enterprise data, regardless of source or format.

Migration, Simplified

Enterprises still struggle with legacy migrations. Snowflake’s answer to that is SnowConvert AI, an agent that automates code translation from platforms like Oracle, Teradata, and BigQuery. It speeds up schema and pipeline migration and lowers dependence on manual rewrites or third-party tools.

Before moving ahead, let’s explore some exciting collaborations from the AIM ecosystem. 

AWS Summit Mumbai returns on June 19 at Jio World Convention Centre, bringing together India’s top tech minds for a day-long deep dive into cloud and generative AI. Click here to register.

Join AIM and Intel on June 26 to understand GenAI optimisation across hardware, frameworks, and applications. Learn how to cut costs and boost performance using Intel AMX, Gaudi accelerators, and more. It’s perfect for AI/ML developers and tech teams scaling GenAI. Click here to register.

Postgres Push and the Crunchy Buy

Snowflake also made a bold move into transactional workloads with the launch of Snowflake Postgres, backed by its $250 million acquisition of Crunchy Data. This brings native Postgres support into the Snowflake platform, allowing developers to build transactional, AI-ready apps without rewriting code. 

Before Snowflake jumped in, Databricks had already played its hand, a $1 billion buyout of Neon, whose AI-native Postgres is already powering 80% of its instances through AI agents.

The Bigger Pattern

Snowflake’s announcements fit a broader pattern of platform consolidation across the enterprise AI stack. Just in the last few weeks:

• Salesforce bought Informatica for $8 billion

• ServiceNow acquired Data.World

• Alation picked up Numbers Station

• Genpact snapped XponentL Data  

Each move signals the same intent, which is to unify data, apps, and AI into a single, purpose-built platform.

The Bottom Line

What began as a cloud-native data warehouse has now evolved into an AI operating system for the enterprise. Snowflake is not only adapting to the agentic future,  it’s actively building for it.