Technology

Anthropic Commits $11.6 Billion to Akamai in Landmark CPU-Focused Cloud Deal

Anthropic has pledged $11.6 billion over seven years to Akamai's cloud infrastructure, a CPU-focused deal that could reach about $20 billion. Akamai is also offering Anthropic a potential equity stake of up to 5% that increases with spending.

Sylvester Eyamah

By Sylvester Eyamah

Sep 26, 2026, 7:07 AM  ·  3 min read

Anthropic Commits $11.6 Billion to Akamai in Landmark CPU-Focused Cloud Deal
Anthropic Commits $11.6 Billion to Akamai in Landmark CPU-Focused Cloud Deal — Technology

Anthropic has agreed to spend at least $11.6 billion with Akamai Technologies over seven years for cloud infrastructure, marking the largest contract in Akamai's history and a significant bet on CPU-based computing for AI workloads .

The agreement, announced September 24, centers on central processing units rather than the GPUs typically associated with AI training. Akamai will supply Anthropic with access to its distributed cloud platform, with analysts noting that CPUs are more power-efficient and cost less to acquire than GPUs, potentially driving higher margins . The deal follows a $1.8 billion agreement the two companies signed in May 2026 .

Expansion Potential and Warrant Structure

The contract could grow by an additional $9 billion, bringing the total potential commitment to approximately $20 billion . As part of the arrangement, Akamai issued Anthropic a warrant for up to 5% of its outstanding common stock — the first time Akamai has attached such a provision to a cloud deal .

The warrant grants Anthropic the right to purchase Series B shares at $111.33 each, convertible into approximately 7.7 million shares of Akamai common stock . About 2% of Akamai's outstanding shares vest upon the initial $11.6 billion commitment, while the remaining 3% vests as Anthropic commits additional tranches, with each $3 billion in additional spending unlocking roughly 1% more .

Capital Investment and Revenue Timeline

Akamai expects total capital expenditure related to the initial commitment to reach approximately $5.5 billion, including a $1.7 billion increase in 2026 spending to pre-purchase components such as memory . The company has authorized contract manufacturer Jabil to purchase about $1.7 billion worth of memory components under an existing services agreement .

Akamai does not expect revenue from the deal this year. Executives projected $150 million to $300 million in 2027, starting in the second half, with revenue reaching an annual run rate of approximately $1.7 billion by the end of 2028 .

Market Reaction and Strategic Significance

Akamai shares surged as much as 20% in extended trading following the announcement . The deal adds to more than $2.8 billion in multi-year cloud infrastructure commitments Akamai has announced across its customer base this year .

"This is the first time Akamai has agreed to a warrant as part of a cloud deal with a customer," CEO Tom Leighton told Bloomberg. "It's a serious step, but I think in this case it made sense to do. It helps bring the companies together" .

The arrangement inverts the more common pattern in AI infrastructure deals, where chipmakers and cloud providers invest directly in the AI labs that buy their products. Here, the supplier is giving its customer a potential stake that grows as spending increases .

Broader Context

The deal represents a significant expansion of Anthropic's compute portfolio. The company recently agreed to spend $45 billion to rent AI cloud computing power from Nscale's West Virginia data center campus . Anthropic has also received investments from Amazon, Google, Microsoft, and AMD while purchasing chips or cloud capacity from them .

The contract requires Akamai to deploy between 95 and 105 megawatts of power capacity, sourced across multiple U.S.-based colocation providers and locations . Akamai's traditional strengths in distributed infrastructure and edge computing position it to handle CPU-oriented AI workloads, which include inference, data processing, and orchestration alongside model training .

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