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URL: https://akamas.io/

⇱ Autonomous Kubernetes Optimization Platform | Akamas


AI & GPU workload optimization

Get more from every GPU

Tune the whole stack together, engine, GPU, and infrastructure, not one layer at a time.
✓ Lower cost per token
✓ Latency that holds under burst
✓ Every change reviewed before it ships

Optimizing full stack
+57.5%
Decode throughput
+54.1%
Prefill throughput
-15.8%
GPU memory
E
Serving engine
vLLM  ·  Batching  ·  KV-cache
Optimized
G
GPU
NVIDIA GPU  ·  DCGM  ·  HBM
Optimized
R
Runtime
CUDA  ·  PyTorch  ·  TensorRT
62% Pending
I
Infrastructure
Kubernetes  ·  HPC  ·  Bare-metal GPU
71% Pending
Recommendation ready: lower the batched-token budget, raise max concurrent sequences Review & apply

Performance, Reliability, and Costs are one challenge

K8s Continuous Full-stack Optimization

Continuously optimize performance, reliability, and cost across your Kubernetes full stack from infrastructure to runtimes. Safely, Explainably. At scale.

-60%

CLOUD COSTS

+30%

PERFORMANCE

zero

DOWNTIME

5x

PRODUCTIVITY
👁 Akamas named Outperformer in GigaOm Cloud Resource Optimization report

THE AKAMAS PLATFORM

Application-aware. Autonomous. AI-powered.

Patented reinforcement learning that optimizes your entire stack – not just infrastructure.

THE PROBLEM

Optimization didn’t keep up with software delivery

Observability, CI/CD, and infrastructure automation have evolved rapidly. Optimization is still manual, reactive, and fragmented across teams. Configuration decisions are made under pressure – tracking reliability for cost, or performance for stability.

This isn’t a tooling problem. It’s a missing platform capability.

THE SHIFT

From manual tuning to autonomous optimization

Akamas turns optimization into a continuous, autonomous capability embedded into modern platforms. By analyzing real workload behavior across the full stack, Akamas identifies inefficiencies, reliability risks, and cost waste – then guides teams toward safe changes, or applies them autonomously.

Autonomy without loss of control.

Akamas helps us size our pods correctly and address configuration issues that often emerge with new services. It also fills a cross-team skills gap between developers and our Kubernetes administrators, delivering significant reliability improvements, and the cost savings donʼt hurt either.

Gabriele Bosisio

Head of Operations Reliability & Security, Sisal

Akamas helped us to rapidly mature on the performance tuning front, by allowing us to find an optimal configuration for our application. This resulted in significant cost savings as well as removing barriers to replatforming.

Chris Cholette

VP Productivity and Site Reliability Engineering, Navan

Thanks to Akamas, TeamSystem has improved the efficiency of our critical microservices as we would never be able to do manually. The ability to consistently deliver the highest level of quality to our end-users at the lowest possible cost is an important differentiator for us.

Luca Montecchiani

Lead Software Architect, Product Owner, TeamSystem

Within just a few hours, Akamas uncovered performance issues we had overlooked for months. This wasn’t just an improvement – it was a revelation. Akamas delivered insights we didn’t even know we needed and solved problems faster than any manual approach could.

Damjan Kumin

Chief Technology Officer, Perform IT

Akamas’ ease of integration with our CI/CD pipelines enabled us to automate the configuration deployment to quickly find optimized configurations that had not been previously found with our manual approach.

Gartner Peer Insights

Customer in Online Services

Blog

Tech deep dives, product news, and Akamas stories – all in one place

Blog
The Michelin Star Dilemma and the Secret to a Perfect Service

In the world of fine dining, the tension in the kitchen is palpable long before…

Blog
The State of Cloud Native Optimization 2026

A critical realization has emerged within the platform engineering community: while the industry has excelled…

Blog
Kubernetes Performance Tuning, Beyond One-Size-Fits-All: Introducing Tuning Profiles

Modern Kubernetes clusters are not monolithic; they host many applications with very different operational needs….

Blog
Why Your Kubernetes Cluster Autoscaler Wastes Resources, and How to Fix It

Platform engineers rely on the Kubernetes cluster autoscaler to control cloud cost. However, the autoscaler…

Blog
Beyond the JVM: Bringing Intelligent Full-Stack Optimization to Node.js

The promise of Kubernetes lies in seamless scalability, yet running high-performance Node.js applications often reveals…

Blog
Scaling from the Right Foundation: Introducing HPA-Aware Optimization in Akamas Insights

Modern Kubernetes environments rely heavily on the Horizontal Pod Autoscaler (HPA) and tools like KEDA…

See for Yourself

Experience the benefits of Akamas autonomous optimization.
No overselling, no strings attached, no commitments.