Akamas optimizes full-stack application configurations using reinforcement learning and observability data, targeting performance engineers, DevOps, and SREs at enterprise scale. The company is engineering-heavy and sales-focused (4 of 6 open roles in these functions), with hiring accelerating across Italy and the US—a pattern typical of early-stage optimization platforms scaling GTM after product validation. Their tech stack (Java, Python, Kubernetes, Dynatrace, Datadog, Grafana, AWS, GCP) reflects the multi-cloud, observability-rich environment their customers operate in.
Akamas builds an autonomous optimization platform that tunes application workloads across cloud infrastructure while accounting for performance, reliability, and cost constraints. The product uses reinforcement learning to identify and apply system configurations in both live production and pre-production test environments. The company serves performance engineers, developers, platform engineers, SREs, and DevOps teams at enterprise financial services, SaaS, and consumer services companies. Akamas partners with major cloud and observability vendors including AWS, Google Cloud, Dynatrace, Datadog, and Splunk. Founded in 2019 and headquartered in Milan, the company maintains offices in Boston, Los Angeles, and Singapore, with current headcount in the 11–50 employee range.
Akamas uses Java, Python, Spring, and Node.js on the backend, Kubernetes for orchestration, and integrates with observability platforms including Dynatrace, Datadog, and Grafana. The platform runs on AWS and Google Cloud.
Akamas autonomously optimizes application performance, reliability, and cloud costs using reinforcement learning and observability data. It adjusts full-stack configurations in production and test environments for enterprises at scale.
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