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Smart Scaler

Slash cloud costs by 20-70% using Generative AI for predictive autoscaling in Kubernetes

Generative AI powered Reinforcement Learning engine continuously optimizes the number of pods

Predicts traffic based on learned patterns

Predicts number of pods needed based on load

Smart Scaler, powered by Generative AI, accurately predicts demand in advance, thus precisely autoscaling infrastructure and application resources.

Smart Scaler

Slash cloud costs by 20-70% using Generative AI for predictive autoscaling in Kubernetes Smart Scaler, powered by Generative AI, accurately predicts demand in advance, thus precisely autoscaling infrastructure and application resources.

Generative AI powered Reinforcement Learning engine continuously optimizes the number of pods

Predicts traffic based on learned patterns

Predicts number of pods needed based on load

Smart Scaler Features

Extracts performance data from Prometheus

Pod capacity estimator predicts number of pods needed for a given load.

Traffic pattern predictor predicts traffic based on learned patterns

Reinforcement Learning Engine accurately estimates K8s resources

Smart Scaler Diagram

Avesha Reinforcement Learning Model - Results

SLA Violations Less

Proactively creating pods for internal load

Reducing pods as load increases

Hyperscalar Autoscaling Performance - Results

Maintain SLA

Proactively scale with demand

Reduce wastage when load decreases

Smart Scaler Benefits

Companies are raking up huge costs due to lack of predictive capabilities in current autoscaling solutions. Smart Scaler is the only “intelligent” HPA (Horizontal Pod Autoscaling) Solution in the market

Reduces cloud costs

Reduces carbon footprint

Maintains SLAs

Eliminates Over Provisioning

Uses AI and Reinforcement Learning

Multi-cluster and Multi Cloud “Intelligent” HPA

How to set up Smart Scaler?

Setting up Smart Scaler RL autoscaling to reduce Kubernetes cloud costs.

Gather historical system performance and application metrics for the service to train smart scaler RL model.

Estimate the capacity of the pod for the service.

Train the RL model for the service.

Deploy smart scaler HPA model to performance testing environment.

Reduce costs by deploying smart scaler RL to production environment.

Monitor and re-train model for traffic and environment changes.

Gather historical system performance and application metrics for the service to train smart scaler RL model.

Estimate the capacity of the pod for the service.

Train the RL model for the service.

Deploy smart scaler HPA model to performance testing environment.

Reduce costs by deploying smart scaler RL to production environment.

Monitor and re-train model for traffic and environment changes.

How to set up Smart Scaler?

Setting up Smart Scaler RL autoscaling to reduce Kubernetes cloud costs.

Gather historical system performance and application metrics for the service to train smart scaler RL model.

Estimate the capacity of the pod for the service.

Train the RL model for the service.

Deploy smart scaler HPA model to performance testing environment.

Reduce costs by deploying smart scaler RL to production environment.

Monitor and re-train model for traffic and environment changes.

Gather historical system performance and application metrics for the service to train smart scaler RL model.

Estimate the capacity of the pod for the service.

Train the RL model for the service.

Deploy smart scaler HPA model to performance testing environment.

Reduce costs by deploying smart scaler RL to production environment.

Monitor and re-train model for traffic and environment changes.

Testimonials

“To date, application and cloud operations teams spend a lot of underappreciated effort trying to predict the cost and performance tradeoffs of different settings for autoscaling pods. Solutions like Avesha’s Smart Scaler can offload the heavy lifting of these estimation processes so cloud native engineers can realize just-in-time optimized HPA settings across their Kubernetes application environments.”

Jason English

Principal Analyst at Intellyx

Learn about our other products

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KubeSlice is a platform that offers an easy and rapid way to isolate and segment your applications within and across K8s clusters. Improve your team's velocity by putting applications on KubeSlices!

KubeSlice Manager (UI)

KubeSlice Manager (UI) is a user-friendly self-service application deployment portal to help customers manage resource consumption, access controls, network policies and monitor latencies across clusters & clouds from one interface.

Simplifying Kubernetes Operations with AI

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