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Snowflake was reportedly discussing a possible acquisition of AI startup Reka for more than $1 billion in May 2024. That was an unconfirmed report about ongoing talks—not an announced deal or proof that Snowflake bought the company. Official Snowflake materials reviewed through August 18, 2026, do not confirm a completed Reka acquisition. The episode is best understood as a glimpse of Snowflake’s effort to build an enterprise AI business, and of the choice between owning models and offering customers a platform for using many models.
What the Reka acquisition report said
A May 16, 2024 report said Snowflake was in discussions to acquire Reka AI for more than $1 billion, citing unnamed sources. It described the talks as ongoing and said they could fall apart. Snowflake reportedly declined to comment, and Reka did not respond. The report did not establish that Snowflake made a binding offer, signed an agreement, received board approval, or set a closing date. Its proposed price was not confirmed transaction terms. Tech Times’ May 16, 2024 report.
The same report put Reka’s valuation at about $300 million in a 2023 funding round that included Snowflake’s venture arm. That, too, is a media-reported figure, not a verified purchase price or proof that an acquisition was agreed. The apparent gap between that valuation and the rumored price may have made the story striking, but the available information does not specify whether the $1 billion figure referred to equity value, enterprise value, or another estimate.
What Reka could have brought to Snowflake
Reka was described in the 2024 report as a startup developing large language models for applications such as image captioning and customer-service chatbots. The report said it was founded in 2022 by researchers with backgrounds at Google and Meta. These details and the company’s financing valuation should be treated as attributed reporting, not as confirmed acquisition disclosures.
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Reka models were already usable with data held in Snowflake, and Snowflake’s 2024 AI materials listed Reka Core and Reka Flash among models available through Cortex AI. That relationship could have lowered the friction of connecting Reka models to Snowflake customers’ data. It does not show that Snowflake owned Reka or its technology: model availability can result from a commercial or licensing arrangement. The model listings appear in Snowflake investor presentation material.
Why Snowflake might have considered a purchase
Snowflake’s core advantage is its role as a place where organizations store and work with data. Enterprise AI applications need access to that data, along with controls over who can use it and how. An acquisition could have given Snowflake a dedicated model-research and engineering team and more influence over model development, deployment, and integration. Buying capabilities can also be faster than building every component internally.
Competitive pressure offered another possible rationale. Snowflake was seeking to make its data platform a place to build and run AI applications, competing with Databricks and the AI services of major cloud providers. CEO Sridhar Ramaswamy, who replaced Frank Slootman in February 2024, had led AI work at Google and co-founded Neeva, which Snowflake acquired in 2023. That background gives context to Snowflake’s AI push, but does not establish why the company held talks with Reka or how advanced those talks were.
Snowflake had also launched Arctic, its own large language model, in April 2024. In its FY25 investor-day materials, Snowflake presented Cortex AI as a way to build generative-AI applications with enterprise data and use foundation, task-specific, and fine-tuned models. Arctic was a model family; Cortex was the broader platform. The strategic question was whether Snowflake needed to own another model developer or could compete by connecting customers to a range of models and providing tools to use them with governed data.
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Owning models versus offering a model platform
Acquiring Reka might have increased Snowflake’s control over a model roadmap and given it a team focused on model development. It might also have supported closer integration with Snowflake’s data services. Those benefits would have come with trade-offs: model ownership can create pressure to favor an in-house option, while customers may value the ability to choose among providers.
Model ownership also does not make AI infrastructure free. Training and serving models require compute, evaluation, safety work, and ongoing engineering. Model quality, cost, and customer preferences can change quickly, so a large acquisition would carry valuation and integration risks as well as potential strategic gains. A multi-model platform, by contrast, can leave more choice with customers but make Snowflake more dependent on external providers and their terms.
The reported talks do not reveal which side of that trade-off Snowflake preferred, whether it considered buying the whole company or selected assets, or what commercial rights it already had. They are evidence of reported discussions—not proof of a strategy decision or a completed investment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Snowflake’s current AI platform says
Snowflake’s current documentation describes Cortex as a broad suite spanning AI functions and LLM functions, Cortex Agents, Analyst, Fine-tuning, Search, Snowflake CoWork, Cortex Code, Snowflake ML, and features for structured and unstructured data. Snowflake says the platform can use proprietary, open-source, and licensed proprietary models. This breadth points to a platform strategy in which model access is one part of a larger set of data, application, and AI services; it does not by itself establish what happened in the Reka discussions. See Snowflake’s AI features documentation.
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Snowflake says customer data is not used to train models made available to its broader customer base. It also describes governance and trust controls on its AI Trust and Safety page. These are the company’s own product and policy statements, not independent performance findings. Buyers should assess them against the specific services, model, region, contract terms, and their own compliance requirements.
Snowflake says its AI features generally use consumption-based pricing, with charges varying by service and usage; the company does not present one all-in subscription price for the suite on its AI product page. For a customer, that means evaluating expected workloads and monitoring actual usage matters more than treating “Cortex” as a single fixed-cost product.
Did Snowflake acquire Reka?
No completed Reka acquisition is confirmed in the official Snowflake materials reviewed through August 18, 2026. The May 2024 report described talks, not a completed deal, and Snowflake’s current AI documentation describes Cortex and its model options without establishing a Reka takeover. The responsible conclusion is that a completion has not been confirmed in those sources—not that it is proven no transaction ever occurred.
Snowflake’s May 2026 announcement about plans to acquire Natoma is a separately disclosed AI-related transaction, with intended integration into Cortex Agents. It illustrates the difference between an explicitly announced acquisition and the earlier Reka report; it is not evidence that the Reka discussions closed. Snowflake’s Natoma announcement.
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What the report means for customers and investors
For enterprise buyers
- Compare platforms on the models available in your region and account, governance controls, workload performance, and the ability to switch providers—not on the assumption that Snowflake owns a model company.
- Estimate costs for the actual workload and identify how usage is measured, monitored, and controlled before deploying consumption-priced AI features.
- Review model-specific data handling and contract terms alongside vendor policy statements, especially where regulated or sensitive information is involved.
For investors
- The report reflected investor interest in whether Snowflake could establish a durable AI position as its data-platform competitors expanded their own offerings.
- Model ownership alone would not establish success. Adoption of AI workloads, customer retention, consumption, and the economics of serving those workloads are more informative measures of execution.
- An acquisition rumor should not be treated as evidence of a signed deal, purchase price, or realized strategic benefit.
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