Optimize cloud-native applications in production
Akamas continuously optimizes applications across the Kubernetes stack, leveraging real-time data. It provides safe, incremental recommendations that meet SLOs, backed by safety policies that ensure that changes are implemented gradually and securely.
The Platform
Application-aware.
Autonomous.
AI-powered.
Akamas uses reinforcement learning, telemetry and user-defined goals to autonomously optimize full-stack configurations of enterprise applications, both live in production and offline in testing environments.
Akamas Platform Akamas Platform-60%
REDUCED CLOUD COSTS
Cut your applications’ demand for compute and infrastructure resources.
+30%
IMPROVED QUALITY
Increase throughput, and reduce response time, with lower fluctuations and peaks.
zero
DOWNTIME
Ensure that apps and microservices work smoothly through workload peaks and anomalies.
5x
INCREASED TEAM PRODUCTIVITY
Automate application tuning, cutting entirely time spent on manual configuration.
Akamas Live Optimization in Action
Your copilot to optimize production applications’ performance, resilience and costs. Set your goals, press play, relax.
Start Collecting Metrics
Find Configuration Improvements
Apply New Configurations
Pod sizing
Optimize resource allocation with automatic pod sizing, ensuring peak performance and cost efficiency in Kubernetes environments.
App-level tuning
Enhance application performance through precise tuning, balancing speed, efficiency, and reliability for superior user experiences.
HPA
Automate Kubernetes scalability with HPA, dynamically adjusting resources to meet demand, optimizing for both performance and cost.
SLO matching
Align services with business objectives using SLO matching, guaranteeing reliability and customer satisfaction through targeted performance metrics.
Autonomous performance engineering
Reduce the time spent on configuration tuning by an impressive 80% through the use of AI-driven optimization techniques.
Optimize pod resources and applications
Akamas supports various technologies in enterprise cloud-native stacks, including Kubernetes and runtimes like JVM, Node.js, .NET, and Golang.
Balance cost targets and performance optimization goals
Akamas enables users to set complex goals for resource usage, application performance, and response times, while adhering to latency, error rate, and SLO constraints.
Prevent dangerous application configurations
Akamas AI continuously learns from system signals to optimize configurations and prevent issues like out-of-memory errors.
Leveraging machine learning for application optimization
Akamas uses proprietary reinforcement learning algorithms, observability, and cloud technology to autonomously optimize workloads. Read more about Offline Optimization in the Akamas documentation.
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See for Yourself
Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.