11 Feb
Akamas reaches a new milestone: Announcing our $10m funding round and US expansion
I am thrilled to share some exciting news with you all today. Akamas has successfully raised $10 million in our first institutional funding round, from United Ventures – one of Italy’s most prominent venture capital firms. This investment is not
25 Nov
We optimized 50,000 Kubernetes pods: Our top 10 lessons learned
Learn from our three-year journey optimizing thousands of Kubernetes pods across multiple enterprise environments. In this Ignite episode, Stefano Doni and Mauro Pessina share the key lessons they discovered while maximizing application reliability and reducing cluster resource costs, challenging many common
02 Aug
Sabre Case Study: optimal cloud performance and cost efficiency
Sabre Corporation, with nearly $3 billion in revenue and customers in 160 countries, is a leading travel technology vendor, connecting travel suppliers and buyers around the globe through innovative software products and next-generation technology solutions. The Challenge One of the most critical
18 Apr
The performance engineer’s secret co-pilot: Embracing AI-powered tuning at Sabre
Explore the integration of machine learning and automation in performance engineering. In this webinar, Pawel Popiolek of Sabre and Stefano Doni, CTO of Akamas, shared Sabre’s journey, highlighting the introduction of Akamas for performance optimization challenges and covering practical use
13 Mar
Akamas Product Tour
Making applications faster, cheaper, and more reliable with AI Akamas is the optimization software that improves the performance, reliability, and cost of enterprise applications. We use AI to radically enhance the productivity and capabilities of performance engineers, SREs and DevOps.
15 Apr
Sisal Case Study: Pioneering innovation in resource optimization with AI
Explore how Sisal, a leading gaming operator with 35 million customers, achieved a 58% cost reduction for critical Kubernetes microservices using Akamas’ AI-powered optimization platform. The Challenge: Sisal needed to optimize Kubernetes microservices for maximum cost savings without impacting application
30 Sep
Performance optimization for banking apps
During the event “Continuous Core Banking Performance Optimization” organized together with our partners Performetriks and Dynatrace, we presented how Akamas allows banks and financial companies to maximize their service performance and resilience, while also reducing costs. In the recorded video (about 20
15 Sep
Busting 4 Java tuning myths with Stefano Doni
Ascolta “Busting 4 Java Tuning Myths with Stefano Doni” su Spreaker. In this PurePerformance episode, Stefano Doni (Akamas CTO) illustrates how some established industry approaches and beliefs about Java Tuning are actually myths: 1) Garbage Collector tuning: do the industry guidelines
11 Aug
Make sure to choose the right GC for your Java application to achieve maximum performance
The Java platform continues to be developed and improved over time. The OpenJDK community has been quite active in improving the performance of the JVM and the garbage collector (GC): new GCs are being developed and existing ones are constantly
16 Jul
Java application throughput, response time, and cost: how to optimize
Developers and system owners usually take for granted that there are some intrinsic tradeoffs in the Java design. For example, it is commonly accepted that if you aim at reducing resource usage (e.g. CPU), you must accept some performance degradation.
20 Jun
Beware: lowering GC time may not lead to better application performance
One of the most established approaches to improve Java application performance is to tune JVM options, in particular Garbage Collector (GC) parameters. Indeed, the main task of garbage collection is to free memory up which requires stopping the application threads
09 Jun
Debunking long-standing Java Tuning myths by leveraging AI Optimization
This presentation by Stefano Doni (Akamas CEO) at Neotys PAC 2021 illustrates how some established industry approaches and beliefs about Java Tuning are simply wrong. By debunking four common JVM tuning myths, some surprising results achieved by adopting AI-based optimization are also
03 Jun
AI-driven performance optimization: A real-world example
Modern applications sit atop of many layers, such as Java Virtual Machines, container, database, cloud, and more, each providing hundreds of settings all interacting in complex and counterintuitive ways. Manual and trial-and-error performance tuning cannot cope with this complexity with
24 Mar
Understanding and measuring CPU throttling in containerized environments
Francesco Fabbrizio, IT Performance Engineer at Moviri, introduces what is throttling and shows how popular open-source and enterprise monitoring solutions address this problem. He also tries to identify new metrics to better understand when throttling is happening. Finally, by the
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