The News: The Snowflake AI Research Team announced Snowflake Arctic, a top-tier enterprise-focused large language model (LLM) that looks to push the frontiers of cost-effective training and openness. Read more about the announcement here.
Snowflake and the AI Landscape Amid Databricks Competition
Analyst Take: As the artificial intelligence (AI) market accelerates, companies such as Snowflake and Databricks are jostling to carve out their niches. The AI industry, particularly in the enterprise sector, has become a battleground for supremacy in LLMs and efficient computing solutions. Databricks has consistently been a formidable player, emphasizing seamless integration with big data and a strong focus on machine learning (ML) operations that harmonize with its lakehouse architecture. In contrast, Snowflake’s approach, while ambitious, often appears as an effort to catch up rather than innovate.
Snowflake’s latest announcements seem to pivot heavily toward AI and LLMs, an arena where Databricks has already established significant prowess. While Snowflake promotes its new product offerings, the broader market context reveals a significant challenge: competing with not only Databricks’ established technology stack but also its entrenched customer loyalty and brand recognition. This struggle for market share is occurring in a space where computational costs and efficiency are critical, and Snowflake’s efforts can sometimes seem more like a response to market pressures than a proactive strategy.
What Was Announced
Snowflake AI Research recently introduced Snowflake Arctic, a new LLM touted and optimized for enterprise AI with a focus on cost-effective training and openness. The model is positioned as a superior solution for tasks such as SQL generation, coding, and instruction following, claiming to outperform competitors even with lower compute budgets. Snowflake emphasizes the model’s licensing under Apache 2.0, promoting “ungated access to weights and code,” and a commitment to open-sourcing their training recipes and research insights.
Moreover, Snowflake announced a suite of related AI initiatives, including the release of the Arctic Embed family of models, serverless access via Snowflake Cortex, and collaborations with major cloud services. While these announcements are robust, they bear a hint of playing catch-up with competitors such as Databricks, which has already deeply integrated similar AI functionalities within its unified analytics platform. The promise of lower costs and open access might appeal to some, but the real-world application and adoption remain speculative, with Snowflake’s track record in AI not yet proven at the scale and depth of Databricks.
Looking Ahead
Snowflake’s strategy in the AI market appears both ambitious and fraught with challenges. The announcement of Snowflake Arctic and associated products positions the company to potentially broaden its footprint in enterprise AI. However, this move comes amid an increasingly crowded market where companies such as Databricks have not only established a first-mover advantage but also continue to innovate at a pace that questions the long-term viability of Snowflake’s offerings.
Snowflake’s focus on cost efficiency and open licensing is commendable but might not be sufficient to sway enterprises that have already invested heavily in other ecosystems that offer more mature, integrated solutions. The reliance on strategic partnerships and the emphasis on open source could be seen as strengths, yet they also highlight potential weaknesses in Snowflake’s own core technology stack. Open source and its business models are well-understood today and are much less of a differentiator than they were 5 or 10 years ago.
Moreover, the AI market is rapidly evolving beyond just offering models that perform well on benchmarks. It demands comprehensive solutions that address the full spectrum of data handling, model training, deployment, and management—areas where Databricks continues to excel with its robust analytics and ML features integrated directly into its lakehouse platform.
Databricks has extensive guidance on Responsible AI, which should be considered the first item in any AI implementation, along with security. Snowflake should up its game in this area. It should look beyond its own documentation and messaging to match what the largest AI vendors offer in this regard.
Key Takeaway: If Snowflake wants to become a market leader instead of being a fast follower, it needs to adjust its strategy and roadmap for solving the AI use cases customers most care about and better differentiate itself and its messages from competitors.
While Snowflake’s recent announcements showcase a significant push toward becoming a key player in the AI space, there is a palpable sense of skepticism about its ability to significantly disrupt the current market dynamics. Its efforts, though noteworthy, might not be enough to unseat more entrenched competitors, particularly Databricks, that continue to lead with innovation and proven, scalable solutions in AI and data analytics.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The authors do not hold any equity positions with any other company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other Insights from The Futurum Group:
Quantum in Context: Quantinuum and the Quest for More 9s
AI in Context: First Test of Several Generative AI Code Generators
Quantum in Context: The Case for On-Premises Quantum Computers
Image Credit: Snowflake
Author Information
Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the Vice President and Practice Leader for Hybrid Cloud, Infrastructure, and Operations at The Futurum Group. With a distinguished track record as a Forbes contributor and a ranking among the Top 10 Analysts by ARInsights, Steven's unique vantage point enables him to chart the nexus between emergent technologies and disruptive innovation, offering unparalleled insights for global enterprises.
Steven's expertise spans a broad spectrum of technologies that drive modern enterprises. Notable among these are open source, hybrid cloud, mission-critical infrastructure, cryptocurrencies, blockchain, and FinTech innovation. His work is foundational in aligning the strategic imperatives of C-suite executives with the practical needs of end users and technology practitioners, serving as a catalyst for optimizing the return on technology investments.
Over the years, Steven has been an integral part of industry behemoths including Broadcom, Hewlett Packard Enterprise (HPE), and IBM. His exceptional ability to pioneer multi-hundred-million-dollar products and to lead global sales teams with revenues in the same echelon has consistently demonstrated his capability for high-impact leadership.
Steven serves as a thought leader in various technology consortiums. He was a founding board member and former Chairperson of the Open Mainframe Project, under the aegis of the Linux Foundation. His role as a Board Advisor continues to shape the advocacy for open source implementations of mainframe technologies.
Dr. Bob Sutor is a Consulting Analyst for Futurum and an expert in quantum technologies with 40+ years of experience. He is an accomplished author of the quantum computing book Dancing with Qubits, Second Edition. Bob is dedicated to evolving quantum to help solve society's critical computational problems. For Futurum, he helps clients understand sophisticated technologies and how to make the best use of them for success in their organizations and industries.
He’s the author of a book about quantum computing called Dancing with Qubits, which was published in 2019, with the Second Edition released in March 2024. He is also the author of the 2021 book Dancing with Python, an introduction to Python coding for classical and quantum computing. Areas in which he’s worked: quantum computing, AI, blockchain, mathematics and mathematical software, Linux, open source, standards management, product management and marketing, computer algebra, and web standards.
