Smart ScalerReduce your cloud costs from 20-70% with continuous predictive autoscaling of Kubernetes resources driven by AI
Reinforcement Learning engine continuously optimizes number of pods
Predicts traffic based on learned patterns
Predicts number of pods needed based on load
Smart Scaler accurately predicts demand ahead of time and 'precisely' scales up or down infrastructure and application resources.Smart ScalerReduce your cloud costs from 20-70% with continuous predictive autoscaling of Kubernetes resources driven by AI Smart Scaler accurately predicts demand ahead of time and 'precisely' scales up or down infrastructure and application resources.
Reinforcement Learning engine continuously optimizes 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 EnglishPrincipal Analyst at Intellyx
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Avesha KubeSlice consists of other products that enable automation, easy interconnectivity across a wide area network, and adaptive & autonomous configuration of the network based on the application's needs.
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