Databricks hits $188B valuation, extending its run as AI’s favorite second act
Databricks hits $188B valuation, extending its run as AI’s favorite second act
Databricks 估值达到 1880 亿美元，延续其作为 AI 领域“第二春”宠儿的势头
Databricks on Thursday announced a new round of funding that values the company at $188 billion. The round was led by Coatue. Databricks didn’t disclose exactly how much it raised; it said the money isn’t in its hands yet and that the round will close later this summer. (Other outlets have since reported the raise is roughly $3 billion.) Databricks 周四宣布完成新一轮融资，公司估值达到 1880 亿美元。本轮融资由 Coatue 领投。Databricks 并未披露具体的融资金额；公司表示资金尚未到账，该轮融资将于今年夏天晚些时候完成。（据其他媒体报道，此次融资额约为 30 亿美元。）
While it’s unusual for a company to announce before it gets the money, a VC tells TechCrunch that the deal is solid, with so many firms wanting in that the company had no reason to keep its shiny new valuation a secret. In fact, Databricks has been on a year-and-a-half fundraising tear as it successfully transitioned its image into an AI provider and not just a yesteryear SaaS sensation. Yesteryear being back in the BC times (Before ChatGPT). 虽然在资金到账前就宣布融资并不常见，但一位风投人士告诉 TechCrunch，这笔交易非常稳固，由于太多机构争相入局，公司完全没有理由对这一亮眼的新估值保密。事实上，在过去一年半的时间里，Databricks 一直处于融资狂潮中，因为它成功地将自身形象转型为一家 AI 提供商，而不仅仅是往日的 SaaS 明星。所谓的“往日”，指的是 ChatGPT 出现之前的“BC 时代”。
Only five months ago, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation. Five months before that, in September 2025, it raised $1 billion at a $100 billion valuation. And roughly nine months before that, in December 2024, it raised what was a record-breaking round at the time of $10 billion at a $62 billion valuation. Databricks has raised so many rounds over the years that this latest one became the subject of memes about running out of letters of the alphabet. “Turning on alerts for when we get a Series AA,” one person posted. 就在五个月前的 2 月，Databricks 完成了 50 亿美元的 L 轮融资，估值为 1340 亿美元。在此之前的五个月，即 2025 年 9 月，它以 1000 亿美元的估值筹集了 10 亿美元。再往前约九个月，即 2024 年 12 月，它完成了当时破纪录的 100 亿美元融资，估值为 620 亿美元。多年来，Databricks 完成了如此多的融资轮次，以至于最新一轮融资引发了关于“字母表不够用”的梗。“已开启提醒，坐等 AA 轮融资，”有人发帖调侃道。
But its image reconstruction has been legit. Founded in 2013, it initially grew to success back in the big data era, with software that enabled enterprises to store enormous amounts of data in the cloud, yet produce speedy analytics. Because it already sat on troves of enterprise data, Databricks was then well-positioned to respond as companies started wanting AI with the same security and governance they expect from traditional enterprise software. 但其形象重塑确实是货真价实的。Databricks 成立于 2013 年，最初在大数据时代崛起，其软件使企业能够在云端存储海量数据，同时实现快速分析。由于已经掌握了海量的企业数据，当企业开始要求 AI 具备传统企业软件所期望的安全性和治理能力时，Databricks 便处于有利地位，能够迅速做出响应。
The company began rolling out one AI product after another, like Lakebase, its database built for AI agents, and Unity, its AI gateway, along with a “meta-harness” called Omnigent that manages multiple agents. Databricks also increasingly became known as one of the big examples of enterprises adopting more affordable Chinese-based open-weight models (models whose underlying code is published for anyone to use and modify) for cost control, one of the big trends of 2026. It is a particular champion of Z.ai’s GLM 5.2 as a model for coding. 该公司开始陆续推出一系列 AI 产品，例如为 AI 智能体构建的数据库 Lakebase、AI 网关 Unity，以及用于管理多个智能体的“元工具”Omnigent。Databricks 也日益成为企业采用更具性价比的中国开源权重模型（即底层代码公开供任何人使用和修改的模型）以控制成本的典型代表，这也是 2026 年的一大趋势。它尤其推崇 Z.ai 的 GLM 5.2 作为编程模型。
Last week Databricks CEO Ali Ghodsi shared the results of some internal benchmarking done to manage his own AI costs for his 3,000 software engineers. The company compared AI models on the actual tasks its programmers do. Not surprisingly, in the blog post revealing the results, Databricks shared that “open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty” in coding, and at a total lower cost than proprietary models from Anthropic and OpenAI. 上周，Databricks 首席执行官 Ali Ghodsi 分享了一些内部基准测试结果，旨在管理其 3000 名软件工程师的 AI 使用成本。该公司针对程序员的实际工作任务对 AI 模型进行了对比。不出所料，在披露结果的博客文章中，Databricks 分享道：“开源模型，尤其是 GLM 5.2，现在已经能够处理编程中最高难度的任务”，且总成本低于 Anthropic 和 OpenAI 的专有模型。
But it did surprise people by finding that the choice of harness — the agentic coding tool, like Codex or Claude Code, that wraps around a model and manages its context and instructions — equally impacted costs. It found that open-source harness Pi to be one of the best at managing context surrounding each prompt, and therefore one of the lowest costs choices without sacrificing quality. “The lesson here isn’t that one harness is always cheaper or that native harnesses are worse,” the post declared. “Instead, model choice is only one piece of the puzzle.” 但令人惊讶的是，研究发现“工具框架”（harness）的选择——即像 Codex 或 Claude Code 这样包裹在模型外围、管理上下文和指令的智能编程工具——对成本的影响同样巨大。研究发现，开源框架 Pi 在管理每个提示词的上下文方面表现最佳，因此是在不牺牲质量的前提下成本最低的选择之一。“这里的教训并不是说某种框架总是更便宜，或者原生框架更差，”文章写道，“相反，模型选择只是拼图的一部分。”
All of this has added to Databricks image as an AI company, even if it wasn’t founded as an AI lab. This, in turn, has granted it the AI-halo for raising money and leaping its valuation. As we previously reported, the AI effect is so strong these days, that even sandwich shop Jersey Mike’s mentioned AI 22 times in its S-1 documents. 所有这些都强化了 Databricks 作为一家 AI 公司的形象，尽管它成立之初并非一家 AI 实验室。这反过来又为其融资和估值飞跃赋予了“AI 光环”。正如我们之前报道的那样，如今 AI 的影响力如此之大，以至于连三明治连锁店 Jersey Mike’s 都在其 S-1 文件中提到了 22 次 AI。