Especially in this generative artificial intelligence, the costs in the cloud are the highest. But this is not only because enterprises use more calculations – they do not use it efficiently. In fact, enterprises are expected to waste $ 44.5 billion for unnecessary expenses for the cloud.
This is a strengthened problem Akamai Technologies: The company has a large and complex cloud infrastructure on many clouds, not to say quite a few strict safety requirements.
Down Solve this, the provider of cybersecurity and content supplier turned to the Kubernetes automation platform Cast that you just havewhich AI agents help optimize costs, security and speed in cloud environments.
Ultimately, the platform helped Akamai reduce from 40% to 70% of the cloud costs, depending on the load on the work.
“We needed a constant way to optimize our infrastructure and reduce the costs in the cloud without dedication of performance,” said VentureBeat Dekes Shavit, senior director of cloud engineering at Akamai. “We are those who process safety events. The delay is not an option. If we are not able to respond to real -time security, we failed.”
Specialized agents who monitor, analyze and act
Kubernetes manages the infrastructure that launches applications, facilitating the implementation, scaling and management, especially in native architecture and microservices.
Cast AI integrated with the Kubernetes ecosystem to assist customers scalp their clusters and loads, select the best infrastructure and manage computational cycles of life, explained the founder and general director of Laurent Gil. Its basic platform is the automation of application performance (APA), which operates through a team of specialised agents who continually monitor, analyze and take motion to enhance application performance, security, efficiency and costs. Companies only offer mandatory calculations from AWS, Microsoft, Google or others.
APA is powered by several machine learning models (ML) with reinforcement learning (RL) based on historical data and learned patterns, strengthened by a pile of remark and heuristics. This is connected to the tools of infrastructure-as-kodem (IAC) on several clouds, which makes it a completely automated platform.
Gil explained that APA was built on the principle that remark is only a place to begin; As he called it, remark is “a foundation, not a goal.” Cast AI also supports an incremental party, so customers do not have to tear and exchange; They can integrate with existing tools and work flow. In addition, nothing never leaves customer infrastructure; All analyzes and activities occur in their dedicated Kubernetes clusters, ensuring greater safety and control.
Gil also emphasized the importance of human concentration. “Automation complements people making decisions,” he said, and APA maintains the flow of human work.
Akamai unique challenges
Shavit explained that the large and complex and complex infrastructure in the cloud Akamai supplies content delivery (CDN) and cyber security services provided for “the most demanding clients and industries in the world”, while observing strict contracts at the level of services (SLA) and performance requirements.
He noticed that for some services they eat, they are probably the largest clients for their supplier, adding that they did “a lot of basic engineering and reengineeria” along with the hyperskara to support their needs.
In addition, Akamai serves clients of assorted sizes and industries, including large financial institutions and bank card corporations. The company’s services are directly related to the customer security attitude.
Ultimately, Akamai needed to balance all this complexity with costs. Shavit noticed that real clients’ attacks can increase the capability of 100x or 1000x to specific elements of their infrastructure. But “in advance, scaling our ability in the cloud 1000 times is simply not financially feasible,” he said.
His team considered optimization on the code side, but the inseparable complexity of their business model required focusing on the basic infrastructure itself.
Automatically optimizing the entire infrastructure Kubernetes
What Akamai really needed was the Kubernetes automation platform, which could optimize the costs of starting the entire basic infrastructure in real time in several clouds, explained Shavit and rock applications based on continually changing demand. But all this needed to be kept away from devoting the performance of the application.
Before the implementation of the forged, Shavit noticed that the Devops Akamai team manually tuned all Kubernetes loads only a few times a month. Given the scale and complexity of its infrastructure, it was difficult and expensive. Analyzing only sporadically loads, they clearly missed any real -time optimization potential.
“Now hundreds of cast agents are doing the same tuning, but they do what a second every day,” said Shavit.
The basic features of APA are used by Akamai is the automation of Kubernetes automation with packaging of containers (minimizing the variety of containers used), the automatic choice of the most profitable computing instances, work rights, automation of the instance of the place in the entire life cycle and cost evaluation.
“We have an insight into the costs of two minutes to integrate, which we have never seen before,” said Shavit. “After the implementation of active agents, the optimization began automatically and the savings began to appear.”
Point instances – in which enterprises can access unused cloud abilities at reduced prices – in fact that they had business sense, but they proved to be complicated attributable to the complex Akamai loads, especially Apache Spark. This meant that that they had to either burden the load or apply more work on them, which proved to be financially mandatory.
Thanks to Cast AI, they may use the points on Spark with “Zero Investment” from the engineering team or surgery. The value of point instances was “super bright”; They only had to search out the right tool to give you the chance to make use of them. Shavit noticed that it was one of the explanation why they went with the forged.
While saving 2x or 3x on the cloud account is great, Shavit indicated that automation without manual intervention is “priceless”. This resulted in “huge” time savings.
Before the implementation of Cast AI, his team “constantly moved on knobs and switches” to be sure that their production environments and customers were on a par with the service in which they needed to speculate.
“The biggest benefit is the fact that we no longer have to manage our infrastructure,” said Shavit. “The Cast Agent Team does it for us now. This has released our team to focus on what is most important: spending functions for our clients faster.”