Databricks Valued at $188 Billion, Accelerates Shift to AI Company

Databricks Reaches $188 Billion Valuation

On July 17, Databricks announced a new funding round led by Coatue, pushing the company’s valuation to $188 billion (approximately ¥28 trillion). Although the exact amount raised wasn’t disclosed, multiple reports suggest it is around $3 billion. Notably, the announcement came before the funds were officially deposited, with the company stating that the round is expected to close later this summer.

According to Julie Bort of TechCrunch AI, who cited venture capital sources, the deal has already been secured, and the lack of secrecy around the valuation stems from widespread interest among investment funds. Over the past 18 months, Databricks has repeatedly raised funds, significantly boosting its valuation each time.

Remarkable Growth:

A Threefold Increase in 18 Months

A look at Databricks’ valuation trajectory highlights its rapid growth. Just five months earlier, in February 2026, the company closed a $5 billion Series L round with a valuation of $134 billion. Five months prior to that, in September 2025, it raised $1 billion at a valuation of $100 billion. Going back nine months to December 2024, it held a record-setting $10 billion round at a $62 billion valuation.

These consecutive funding rounds have drawn significant attention within the industry, even sparking memes about running out of alphabetical labels for funding series. One user on X (formerly Twitter) joked, “Turn on notifications when they hit Series AA,” highlighting how frequent these rounds have become.

Strategic Shift to AI Enterprise

Founded in 2013, Databricks initially achieved success by offering software for rapid enterprise data analysis in the era of big data. However, following the rise in AI demand since the advent of ChatGPT, the company has been rapidly transforming its identity.

Already managing vast amounts of enterprise data, Databricks was well-positioned to extend the security and governance features required for enterprise software to AI workloads. The company has released a slew of new AI products, including Lakebase (a database for AI agents), Unity (an AI gateway), and Omnigent (a meta-harness for managing multiple agents).

Through this transformation, Databricks has successfully redefined itself from a “mere SaaS success story” to an “AI provider,” exemplifying how enterprise data platforms can be connected to the AI era.

Focus on Open-Weight Models

Another key pillar of Databricks’ strategy is its active adoption of cost-efficient open-weight models. Open-weight models are AI models whose foundational code is publicly available and can be freely used or modified. These models have become a major trend in 2026, with Databricks particularly valuing GLM 5.2, developed by China’s Z.ai, for coding applications.

Last week, CEO Ali Ghodsi revealed the results of an internal benchmark conducted with 3,000 of the company’s software engineers. The study compared AI models based on real programming tasks and concluded the following in a blog post:

“Open models, particularly GLM 5.2, can handle even the most challenging levels of coding tasks.”

Moreover, these models deliver comparable performance to proprietary models like those from Anthropic or OpenAI but at a significantly lower cost. This finding positions open-weight models as a compelling option for enterprises aiming to reduce AI adoption costs.

However, Ghodsi emphasized that the performance of the models alone isn’t the only factor; the choice of agentic coding tools (such as Codex or Claude Code, which wrap around these models) is equally critical.

Industry Impact and Challenges

Databricks’ latest moves offer several insights for the AI industry. First, they demonstrate that businesses with large-scale data platforms transitioning to AI can achieve exceptionally high market valuations. Second, the practical demonstration of open-weight models’ utility—backed by real-world data—marks a significant milestone. Showing that these models can match proprietary ones in coding tasks at lower costs may influence many companies’ AI adoption strategies.

However, the rapid increase in valuation also reflects sky-high expectations for future revenue growth. Databricks will need to continue expanding the market share of its AI products and generating returns that justify these investments.

Additionally, the company’s emphasis on enterprise data security and governance necessitates a robust foundation. Cyberattacks targeting enterprise data platforms are a significant risk, as demonstrated by the recent disclosure of the Microsoft Defender privilege escalation vulnerability “RoguePlanet”. Databricks’ platform, including its AI gateways and data lakehouse, must undergo continuous security audits and rapid patching to avoid becoming a target.

From a developer community perspective, the integrated environment offered by Databricks contrasts with the freedom of customization seen in projects like the DIY Steam Machine with fiber-optic HDMI and Bazzite. Both approaches share the commonality of enabling users—whether enterprises or individuals—to build on a reliable foundation. Striking the right balance between trustworthiness and flexibility will remain a challenge for Databricks in the AI era.

The diversification of user interfaces for data analytics is another trend to watch. The growing popularity of mobile applications, as seen in evaluations like Fermata Auto: Practical Review of Video Playback on Android Auto, suggests a shift from fixed desktop environments to more versatile setups. Databricks’ response to this new wave of front-end applications could be a key factor in its future growth.

Editorial Opinion

In the short term, this substantial funding round equips Databricks with the resources needed to accelerate AI product development and global expansion. As AI functionalities become essential differentiators against competitors like Snowflake and Google BigQuery, increased investment in R&D is expected. Additionally, the public endorsement of open-weight models may challenge the business models of companies that continue to rely on proprietary models.

From a long-term perspective, Databricks’ combination of “enterprise data platforms + AI” could become a standard architecture across various industries. For cost-conscious companies, the findings on open-weight models will likely encourage their adoption. For the industry as a whole, the increase in AI model options could reduce vendor lock-in and foster a more open ecosystem.

That said, the $188 billion valuation raises concerns about sustainability. If revenue growth doesn’t meet market expectations, the risk of a downward valuation adjustment looms large. While promoting open-weight models, reliance on a Chinese-developed model like GLM 5.2 introduces geopolitical and regulatory considerations that cannot be ignored.

References

• “Databricks hits $188B valuation, extending its run as AI’s favorite second act”, by Julie Bort — TechCrunch AI, 2026-07-17T22:12:56.000Z (ARR)
• Source URL: https://techcrunch.com/2026/07/17/databricks-hits-188b-valuation-extending-its-run-as-ais-favorite-second-act/