Amazon Decart Acquisition Reported

Amazon Decart acquisition talks reportedly value Decart at about $7 billion and would bolster Amazon's AI chip strategy while reshaping M&A pricing.

October 12, 2026·2 min read
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Flat-vector cover of a server chip fused with a compute module to symbolize the Amazon Decart acquisition and AI chip strategic fit.

KEY TAKEAWAYS

  • Amazon reportedly in talks to buy Decart for about $7 billion.
  • The price would exceed Decart's near $4 billion May 2026 valuation.
  • Deal would give Amazon tools to run AI workloads across chips and bolster Trainium3 strategy.

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Amazon (AMZN) is reportedly negotiating to acquire AI startup Decart for about $7 billion, the Wall Street Journal said on Oct. 11, 2026. Reuters reported on Oct. 12, 2026, that neither company commented and the report could not be independently verified.

Reported Deal Terms and Strategic Fit

Amazon is in ongoing talks to buy Decart, with the transaction potentially closing soon if negotiations hold and no competing bidder emerges. The reported terms remain subject to change. Neither Amazon nor Decart has confirmed the discussions.

Decart raised $300 million in May 2026 in a financing round led by Radical Ventures, with Nvidia joining as a new investor. At that time, the company’s valuation was near $4 billion. In August 2026, Anthropic explored acquiring Decart for about $6 billion but decided not to proceed after due diligence in September.

Founded in 2023 by brothers Dean and Orian Leitersdorf and Moshe Shalev, Decart develops software that helps developers run or move AI workloads across different chips. Its products include Lucy, a model for real-time video editing, and Oasis, used for robotics and autonomous-driving testing.

Amazon Web Services gave Decart early access to its Trainium3 AI chip in 2025. Acquiring Decart would provide Amazon with tools to operate AI workloads across multiple hardware types as it advances its Trainium processors in competition with Nvidia. This capability aligns with Amazon’s effort to control more of the AI stack while offering customers flexibility in where models run.

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