Upwind Security is closing a $300 million funding round that values the company at $3.8 billion, more than doubling the valuation it carried just eight months ago.
The round is being led by Bessemer Venture Partners, with participation from existing backers. It follows a $250 million raise in January 2026 that initially valued Upwind at $1.5 billion before extending to roughly $1.6 billion. With this latest infusion, the San Francisco-based startup has now raised more than $680 million in total funding.
From scrappy startup to cybersecurity heavyweight
Upwind was founded in 2022 by Amiram Shachar and former members of the Spot.io team. It builds a runtime-focused cloud security platform that watches cloud infrastructure in real time and tells security teams which threats actually matter.
Upwind’s annual recurring revenue sat below $20 million at the end of 2025. By mid-2026, that figure has ballooned to an estimated $50 million to $60 million. The company is targeting $100 million ARR by the end of this year.
The client roster includes Siemens, Peloton, and Roku, a mix that spans industrial technology, consumer hardware, and streaming infrastructure.
The Wiz-shaped hole in the market
To understand why investors are so eager to write checks to Upwind, you have to rewind to Google’s $32 billion acquisition of Wiz. That deal effectively removed one of the most prominent independent cloud security players from the market.
A flurry of M&A activity across the cybersecurity sector in late 2025 and early 2026 further reinforced the thesis that standalone cloud security companies are valuable targets.
Upwind’s investor base includes Bessemer Venture Partners, Craft Ventures, Salesforce Ventures, and Stephen Curry’s investment fund.
What the valuation jump signals
Going from $1.5 billion to $3.8 billion in under a year is aggressive by any standard. The platform uses real-time context to prioritize risks, helping security teams decide what to fix first across multi-cloud environments that have expanded further with the addition of AI workloads.
Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy.

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