Databricks 周四宣布了一轮新的融资，该轮融资使公司估值达到 1880 亿美元：https://www.databricks.com/company/newsroom/press-releases/databricks-raising-strategic-round-funding-188-billion-valuation。该轮融资由 Coatue 领投。
Databricks 没有披露具体筹集了多少资金；它表示资金尚未到手，该轮融资将在今年夏末完成。（其他媒体报道称，这轮融资大约为 30 亿美元：https://www.wsj.com/tech/ai/databricks-set-to-hit-188-billion-valuation-with-new-investment-from-coatue-9eda18d6。）虽然公司在获得资金前宣布融资是不常见的，但一位风险投资人士告诉 TechCrunch，这笔交易是稳固的，许多公司都希望参与，这使得公司没有理由隐瞒其崭新的估值。
事实上，Databricks 在过去一年半内一直在融资热潮中，因为它成功地将自身形象转变为 AI 提供商，而不仅仅是过去的 SaaS 风潮。所谓的过去是指 BC（ChatGPT 之前的时代）时期。
就在五个月前的二月，Databricks 完成了 50 亿美元的 L 系列融资，估值为 1340 亿美元：https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/。五个月前的 2025 年九月，它融资 10 亿美元，估值为 1000 亿美元：https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/。再往前大约九个月，即 2024 年十二月，它完成了一轮当时创纪录的融资 100 亿美元：https://techcrunch.com/2024/12/17/insight-vc-describes-databricks-wild-10b-deal-and-the-bad-advice-the-ceo-ignored/，估值为 620 亿美元。
Databricks 多年来筹集了如此多轮资金，以至于最新一轮融资成为关于字母快用完的梗话题：https://x.com/Iron_Yuppie/status/2078193459370815497。“开启提醒功能，当我们获取 AA 系列融资时通知我，”有人发帖写道。
但它的形象重塑是真实的。Databricks 成立于 2013 年，最初在大数据时代取得了成功，其软件使企业能够在云中存储海量数据，同时实现快速分析。
因为它已经掌握了大量企业数据，当公司开始希望在AI中实现与传统企业软件相同的安全性和治理时，Databricks 当时处于良好位置以作出响应。
该公司开始推出一个又一个AI产品，比如为AI代理构建的数据库 Lakebase：https://techcrunch.com/2025/08/19/databricks-ceo-says-fresh-1b-will-help-him-attack-a-new-ai-database-market/，以及其AI网关 Unity，同时还有一个叫 Omnigent 的“元控制器”，用于管理多个代理。
Databricks 也越来越为人所知：https://x.com/Yuchenj_UW/status/2070166719839326396，成为企业采用更实惠的中国本土开源权重模型（其底层代码公开，任何人都可以使用和修改）以控制成本的典型案例之一，这也是2026年的一大趋势：https://techcrunch.com/2026/07/13/satya-nadella-has-issued-a-shocking-warning-to-companies-using-ai/。它尤其支持将 Z.ai 的 GLM 5.2 用作编程的模型。
上周，Databricks CEO Ali Ghodsi 分享了他为了管理自己3000名软件工程师的AI成本所做的一些内部基准测试结果：https://x.com/alighodsi/status/2074996561306955958。
公司比较了AI模型在其程序员实际执行任务中的表现。不出所料，在揭示结果的博客文章中：https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase，Databricks 分享称，“开源模型，尤其是 GLM 5.2，现在甚至能够处理最高难度的编码任务”，而且总成本比 Anthropic 和 OpenAI 的专有模型更低。
但让人意外的是，他们发现工具选择——即环绕模型并管理其上下文和指令的代理编程工具，例如 Codex 或 Claude Code——同样影响成本。研究发现开源工具 Pi 在管理每个提示的上下文方面表现出色，因此在不降低质量的情况下，是成本最低的选择之一。
“这里的教训并不是说某一种工具总是更便宜，或者本地工具更差，”这篇文章声明：https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase。“相反，模型选择只是拼图中的一块。”
所有这些都增加了Databricks作为人工智能公司的形象，即使它最初并不是作为AI实验室成立的。反过来，这又赋予了它在融资和估值跳升中的AI光环。正如我们之前报道的那样，如今AI效应非常强大，即使是三明治店Jersey Mike’s在其S-1文件中也提到了AI 22次：https://techcrunch.com/2026/07/02/jersey-mikes-ipo-illustrates-how-bad-the-ai-hype-has-become/
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Databricks on Thursday announced a new round of funding that values the company at $188 billion：https://www.databricks.com/company/newsroom/press-releases/databricks-raising-strategic-round-funding-188-billion-valuation. 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：https://www.wsj.com/tech/ai/databricks-set-to-hit-188-billion-valuation-with-new-investment-from-coatue-9eda18d6.) 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).
Only five months ago, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation：https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/. Five months before that, in September 2025, it raised $1 billion at a $100 billion valuation：https://techcrunch.com/2025/12/16/databricks-raises-4b-at-134b-valuation-as-its-ai-business-heats-up/. And roughly nine months before that, in December 2024, it raised what was a record-breaking round at the time of $10 billion：https://techcrunch.com/2024/12/17/insight-vc-describes-databricks-wild-10b-deal-and-the-bad-advice-the-ceo-ignored/ 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：https://x.com/Iron_Yuppie/status/2078193459370815497 of the alphabet. “Turning on alerts for when we get a Series AA,” one person posted.
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.
The company began rolling out one AI product after another, like Lakebase, its database built for AI agents：https://techcrunch.com/2025/08/19/databricks-ceo-says-fresh-1b-will-help-him-attack-a-new-ai-database-market/, and Unity, its AI gateway, along with a “meta-harness” called Omnigent that manages multiple agents.
Databricks also increasingly became known：https://x.com/Yuchenj_UW/status/2070166719839326396 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：https://techcrunch.com/2026/07/13/satya-nadella-has-issued-a-shocking-warning-to-companies-using-ai/. It is a particular champion of Z.ai’s GLM 5.2 as a model for coding.
Last week Databricks CEO Ali Ghodsi shared the results：https://x.com/alighodsi/status/2074996561306955958 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：https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase, 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.
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：https://www.databricks.com/blog/benchmarking-coding-agents-databricks-multi-million-line-codebase. “Instead, model choice is only one piece of the puzzle.”
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：https://techcrunch.com/2026/07/02/jersey-mikes-ipo-illustrates-how-bad-the-ai-hype-has-become/ AI 22 times in its S-1 documents.
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