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Cutting cloud waste at scale: Akamai saves 70% using AI agents orchestrated by kubernetes

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Particularly in this dawning era of generative AI, cloud costs are at an all-time high. But that’s not merely because enterprises are using more compute — they’re not using it efficiently. In […]

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more


Particularly in this dawning era of generative AI, cloud costs are at an all-time high. But that’s not merely because enterprises are using more compute — they’re not using it efficiently. In fact, just this year, enterprises are expected to waste $44.5 billion on unnecessary cloud spending. 

This is an amplified problem for Akamai Technologies: The company has a large and complex cloud infrastructure on multiple clouds, not to mention numerous strict security requirements.

To resolve this, the cybersecurity and content delivery provider turned to the Kubernetes automation platform Cast AI, whose AI agents help optimize cost, security and speed across cloud environments. 

Ultimately, the platform helped Akamai cut between 40% to 70% of cloud costs, depending on workload. 

“We needed a continuous way to optimize our infrastructure and reduce our cloud costs without sacrificing performance,” Dekel Shavit, senior director of cloud engineering at Akamai, told VentureBeat. “We’re the ones processing security events. Delay is not an option. If we’re not able to respond to a security attack in real time, we have failed.”

Specialized agents that monitor, analyze and act

Kubernetes manages the infrastructure that runs applications, making it easier to deploy, scale and manage them, particularly in cloud-native and microservices architectures.

Cast AI has integrated into the Kubernetes ecosystem to help customers scale their clusters and workloads, select the best infrastructure and manage compute lifecycles, explained founder and CEO Laurent Gil. Its core platform is Application Performance Automation (APA), which operates through a team of specialized agents that continuously monitor, analyze and take action to improve application performance, security, efficiency and cost. Companies provision only the compute they need from AWS, Microsoft, Google or others.

APA is powered by several machine learning (ML) models with reinforcement learning (RL) based on historical data and learned patterns, enhanced by an observability stack and heuristics. It is coupled with infrastructure-as-code (IaC) tools on several clouds, making it a completely automated platform.

Gil explained that APA was built on the tenet that observability is just a starting point; as he called it, observability is “the foundation, not the goal.” Cast AI also supports incremental adoption, so customers don’t have to rip out and replace; they can integrate into existing tools and workflows. Further, nothing ever leaves customer infrastructure; all analysis and actions occur within their dedicated Kubernetes clusters, providing more security and control.

Gil also emphasized the importance of human-centricity. “Automation complements human decision-making,” he said, with APA maintaining human-in-the-middle workflows.

Akamai’s unique challenges

Shavit explained that Akamai’s large and complex cloud infrastructure powers content delivery network (CDN) and cybersecurity services delivered to “some of the world’s most demanding customers and industries” while complying with strict service level agreements (SLAs) and performance requirements.

He noted that for some of the services they consume, they’re probably the largest customers for their vendor, adding that they have done “tons of core engineering and reengineering” with their hyperscaler to support their needs. 

Further, Akamai serves customers of various sizes and industries, including large financial institutions and credit card companies. The company’s services are directly related to its customers’ security posture. 

Ultimately, Akamai needed to balance all this complexity with cost. Shavit noted that real-life attacks on customers could drive capacity 100X or 1,000X on specific components of its infrastructure. But “scaling our cloud capacity by 1,000X in advance just isn’t financially feasible,” he said. 

His team considered optimizing on the code side, but the inherent complexity of their business model required focusing on the core infrastructure itself. 

Automatically optimizing the entire Kubernetes infrastructure

What Akamai really needed was a Kubernetes automation platform that could optimize the costs of running its entire core infrastructure in real time on several clouds, Shavit explained, and scale applications up and down based on constantly changing demand. But all this had to be done without sacrificing application performance.

Before implementing Cast, Shavit noted that Akamai’s DevOps team manually tuned all its Kubernetes workloads just a few times a month. Given the scale and complexity of its infrastructure, it was challenging and costly. By only analyzing workloads sporadically, they clearly missed any real-time optimization potential. 

“Now, hundreds of Cast agents do the same tuning, except they do it every second of every day,” said Shavit. 

The core APA features Akamai uses are autoscaling, in-depth Kubernetes automation with bin packing (minimizing the number of bins used), automatic selection of the most cost-efficient compute instances, workload rightsizing, Spot instance automation throughout the entire instance lifecycle and cost analytics capabilities.

“We got insight into cost analytics two minutes into the integration, which is something we’d never seen before,” said Shavit. “Once active agents were deployed, the optimization kicked in automatically, and the savings started to come in.”

Spot instances — where enterprises can access unused cloud capacity at discounted prices — obviously made business sense, but they turned out to be complicated due to Akamai’s complex workloads, particularly Apache Spark, Shavit noted. This meant they needed to either overengineer workloads or put more working hands on them, which turned out to be financially counterintuitive. 

With Cast AI, they were able to use spot instances on Spark with “zero investment” from the engineering team or operations. The value of spot instances was “super clear”; they just needed to find the right tool to be able to use them. This was one of the reasons they moved forward with Cast, Shavit noted. 

While saving 2X or 3X on their cloud bill is great, Shavit pointed out that automation without manual intervention is “priceless.” It has resulted in “massive” time savings.

Before implementing Cast AI, his team was “constantly moving around knobs and switches” to ensure that their production environments and customers were up to par with the service they needed to invest in. 

“Hands down the biggest benefit has been the fact that we don’t need to manage our infrastructure anymore,” said Shavit. “The team of Cast’s agents is now doing this for us. That has freed our team up to focus on what matters most: Releasing features faster to our customers.”

Editor’s note: At this month’s VB Transform, Google Cloud CTO Will Grannis and Highmark Health SVP and Chief Analytics Officer Richard Clarke will discuss the new AI stack in healthcare and the real-world challenges of deploying multi-model AI systems in a complex, regulated environment. Register today.

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Trump Overturns California Phaseout of Fossil Fuel Cars

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Israel Strikes Continue amid Reports Iran Keen to De-Escalate

Israel and Iran exchanged fire for the fourth consecutive day on Monday, stoking fears of an all-out war with the potential to drag in others in the oil-rich region and force the US into a more hands-on stance. Iran fired several waves of drones and missiles over the last 24 hours, while Israel continued hitting the Islamic Republic’s capital, Tehran, and killing one more senior military official.  Since Friday, 224 people have been killed in Iran, according to the government, which said most of the casualties were civilians. Iranian attacks killed 24 people in Israel, according to the Israeli government press office, and injured 592.  Tehran is signaling it wants to deescalate hostilities with Israel and is willing to resume nuclear talks with the US as long as Washington doesn’t join the Israeli attacks, the Wall Street Journal reported Monday citing Middle Eastern and European officials it didn’t identify. A similar report by Reuters says Iran conveyed the message through Qatar, Saudi Arabia and Oman.  Oil fell on the WSJ report, with Brent futures dropping around 4 percent – they rose over 10 percent Friday. US Treasuries pared earlier drops and European bonds gained as traders reacted to diminishing concerns about inflation.  It’s not clear whether Israel would agree to stop missile strikes. Israeli officials have said they want to ensure Iran doesn’t have the capacity to build a nuclear weapon. The exchange of missile salvos between Israel and Iran is the most serious escalation after years of shadow war. Analysts fear it might push the Middle East into a regional conflict, causing wider human loss and potentially disrupting energy flows and vital trade routes.  One missile landed near the US consulate in central Tel Aviv, causing minor material damages but no injuries to personnel, the ambassador to Israel, Mike Huckabee, said Monday.

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Will ERCOT’s streamlined connect-and-manage approach work for other markets?

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Oracle’s struggle with capacity meant they made the difficult but responsible decisions

IDC President Crawford Del Prete agreed, and said that Oracle senior management made the right move, despite how difficult the situation is today. “Oracle is being incredibly responsible here. They don’t want to have a lot of idle capacity. That capacity does have a shelf life,” Del Prete said. CEO Katz “is trying to be extremely precise about how much capacity she puts on.” Del Prete said that, for the moment, Oracle’s capacity situation is unique to the company, and has not been a factor with key rivals AWS, Microsoft, and Google. During the investor call, Katz said that her team “made engineering decisions that were much different from the other hyperscalers and that were better suited to the needs of enterprise customers, resulting in lower costs to them and giving them deployment flexibility.” Oracle management certainly anticipated a flurry of orders, but Katz said that she chose to not pay for expanded capacity until she saw finalized “contracted noncancelable bookings.” She pointed to a huge capex line of $9.1 billion and said, “the vast majority of our capex investments are for revenue generating equipment that is going into data centers and not for land or buildings.”

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Winners and losers in the Top500 supercomputer ranking

GPU winner: AMD AMD is finally making a showing for itself, albeit modestly, in GPU accelerators. For the June 2025 edition of the list, AMD Instinct accelerators are in 23 systems, a nice little jump from the 10 systems on the June 2024 list. Of course, it helps with the sales pitch when AMD processors and coprocessors can be found powering the No. 1 and No. 2 supercomputers in the world. GPU loser: Intel Intel’s GPU efforts have been a disaster. It failed to make a dent in the consumer space with its Arc GPUs, and it isn’t making much headway in the data center, either. There were only four systems running GPU Max processors on the list, and that’s up from three a year ago. Still, it’s pitiful showing given the effort Intel made. Server winners: HPE, Dell, EVIDAN, Nvidia The four server vendors — servers, not component makers — all saw share increases. Nvidia is also a server vendor, selling its SuperPOD AI servers directly to customers. They all gained at the expense of Lenovo and Arm. Server loser: Lenovo It saw the sharpest drop in server share, going from 163 systems in June of 2024 to 136 in this most recent listing. Loser: Arm Other than the 13 Nvidia Grace chips, the ARM architecture was completely absent from this spring’s list.

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Cisco reinvigorates data center, campus, branch networking with AI demands in mind

“We have a number of … enterprise data center customers that have been using bi-directional optics for many generations, and this is the next generation of that feature,” said Bill Gartner, senior vice president and general manager of Cisco’s optical systems and optics business. “The 400G lets customer use their existing fiber infrastructure and reduces fiber count for them so they can use one fiber instead of two, for example,” Gartner said. “What’s really changed in the last year or so is that with AI buildouts, there’s much, much more optics that are part of 400G and 800G, too. For AI infrastructure, the 400G and 800G optics are really the dominant optics going forward,” Gartner said. New AI Pods Taking aim at next-generation interconnected compute infrastructures, Cisco expanded its AI Pod offering with the Nvidia RTX 6000 Pro and Cisco UCS C845A M8 server package. Cisco AI Pods are preconfigured, validated, and optimized infrastructure packages that customers can plug into their data center or edge environments as needed. The Pods include Nvidia AI Enterprise, which features pretrained models and development tools for production-ready AI, and are managed through Cisco Intersight. The Pods are based on Cisco Validated Design principals, which offer customers pre-tested and validated network designs that provide a blueprint for building reliable, scalable, and secure network infrastructures, according to Cisco. Building out the kind of full-scale AI infrastructure compute systems that hyperscalers and enterprises will utilize is a huge opportunity for Cisco, said Daniel Newman, CEO of The Futurum Group. “These are full-scale, full-stack systems that could land in a variety of enterprise and enterprise service application scenarios, which will be a big story for Cisco,” Newman said. Campus networking For the campus, Cisco has added two new programable SiliconOne-based Smart Switches: the C9350 Fixed Access Smart Switches and C9610

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Qualcomm’s $2.4B Alphawave deal signals bold data center ambitions

Qualcomm says its Oryon CPU and Hexagon NPU processors are “well positioned” to meet growing demand for high-performance, low-power compute as AI inferencing accelerates and more enterprises move to custom CPUs housed in data centers. “Qualcomm’s advanced custom processors are a natural fit for data center workloads,” Qualcomm president and CEO Cristiano Amon said in the press release. Alphawave’s connectivity and compute technologies can work well with the company’s CPU and NPU cores, he noted. The deal is expected to close in the first quarter of 2026. Complementing the ‘great CPU architecture’ Qualcomm has been amassing Client CPUs have been a “big play” for Qualcomm, Moor’s Kimball noted; the company acquired chip design company Nuvia in 2021 for $1.4 billion and has also announced that it will be designing data center CPUs with Saudi AI company Humain. “But there was a lot of data center IP that was equally valuable,” he said. This acquisition of Alphawave will help Qualcomm complement the “great CPU architecture” it acquired from Nuvia with the latest in connectivity tools that link a compute complex with other devices, as well as with chip-to-chip communications, and all of the “very low level architectural goodness” that allows compute cores to deliver “absolute best performance.” “When trying to move data from, say, high bandwidth memory to the CPU, Alphawave provides the IP that helps chip companies like Qualcomm,” Kimball explained. “So you can see why this is such a good complement.”

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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