Dive Brief:
- Venture capital has funneled $113 billion to AI and data infrastructure startups in the Snowflake Partner Network since 2020, according to a Crunchbase report analyzing 1,300 private companies in the data cloud company’s ecosystem.
- Machine learning and data science startups earned 45% of the investments, totaling $51 billion. Nearly two-thirds of the deals flowed through AI and analytics companies, per the analysis.
- The funding flow underscores pressure on enterprises to generate returns on their AI investments, Amy Kodl, Snowflake SVP of worldwide alliances and channels, told Channel Dive. “AI is only effective when it’s connected to governed, high-quality enterprise data,” Kodl said in an email. “That’s why we’re seeing significant investments in machine learning and data science companies helping customers move from isolated pilots to scalable AI applications and agents built on governed enterprise-ready data.”
Dive Insight:
VC funding in Snowflake’s ecosystem peaked during the pandemic as companies migrated en masse to the cloud. In 2021, investors spent $34 billion across 760 deals. Crunchbase found that venture capitalists have grown increasingly selective since then, pouring roughly $25 billion into the Snowflake ecosystem across just 260 startups – though total funds rebounded amid the AI spending spree in the last year.
The uptick preceded a VC funding surge in the first three months of 2026, which saw investments rise over $300 billion globally — an all-time high, according to Crunchbase. AI companies captured the majority of financing, according to an April report.
“AI is driving a fundamentally different investment cycle,” Crunchbase research lead Gené Teare said in a press release accompanying the report. “Capital is increasingly concentrated in a small number of companies raising billions, while across the broader ecosystem, investors are picking winners as competition to break through intensifies.”
As the capabilities of AI models grow, so does data value as a strategic asset. Businesses are grappling with data challenges related to managing and securing customer records, inventory information and sales figures.
“Enterprises are no longer investing in AI for experimentation alone,” Kodl said. “They’re investing in enterprise AI systems that can scale securely across the business. What stood out most was the concentration of funding around companies solving data, governance, and interoperability challenges. It reinforces Snowflake’s belief that there can be no AI strategy without a data strategy and highlights the critical role partners play in helping customers operationalize AI at scale.”
Governance gaps and security concerns are two of the biggest factors limiting widespread AI adoption. A study by McKinsey & Company found that more than one-third of enterprises lack mature governance capabilities. As AI agents proliferate, channel firms can play a key role in addressing these lapses.
In Snowflake’s partner network, about a quarter of venture capital funds since 2020 went to startups working on security, governance and observability. The bulk of investment flowed into companies using machine learning and data science to build, train and deploy predictive models and AI-driven analytics on data.
As demand for AI and data infrastructure grows, the Snowflake startups are approaching investment milestones. According to Crunchbase’s models, 44% of the companies that have raised $5 million or more in funding are likely to move on to their next funding round, and about 34% will likely achieve a successful acquisition or IPO.
The channel has a stake, as well. Kodl said partners play a critical role in helping startups bridge innovation and enterprise adoption.
“Success in AI will come down to delivering outcomes, not just implementations,” she said. “Customers need partners who can combine industry expertise, trusted data foundations, and AI innovation into repeatable, scalable solutions. The partners that win will be the ones helping customers operationalize AI securely, govern it effectively, and continuously optimize business value on top of the Snowflake AI Data Cloud.”
