About StackBooster

StackBooster is an AI-driven platform that optimizes Kubernetes cluster performance and reduces cloud costs. It proactively scales nodes and pods based on predicted demand, dynamically right-sizes pods, and intelligently manages spot instances for zero-downtime operations.

<problem> Managing Kubernetes clusters at scale presents significant operational overhead, leading to inefficient resource utilization and increased cloud expenditure. Traditional autoscaling solutions often react to existing load rather than proactively optimizing for future demand, and manual configuration of spot instances introduces complexity and potential downtime. </problem> <solution> StackBooster provides an AI-driven automation platform designed to optimize Kubernetes cluster performance and reduce operational costs. The system proactively scales nodes and pods based on predicted demand, ensuring resources are precisely allocated. It dynamically right-sizes pods in real-time to prevent over-provisioning and continuously defragments clusters by selecting cost-efficient worker nodes. Furthermore, StackBooster enables zero-maintenance Kubernetes setups and leverages AI for seamless, zero-downtime management of spot instances, enhancing overall cluster reliability and availability. </solution> <features> - AI-powered proactive node and pod autoscaling for optimized cluster efficiency. - Dynamic pod right-sizing to ensure precise resource allocation based on real-time application needs. - Continuous cluster defragmentation through intelligent selection of cost-effective worker instances. - Zero-maintenance Kubernetes cluster setup with a simplified, click-based deployment process. - AI-driven spot instance handling for high availability and zero-downtime workload migration. - Advanced scheduling capabilities, including TopologyAwareScheduler, to reduce network latency and improve resource utilization. - StackSizing module for adaptive pod resource allocation and performance optimization. - ClusterPilot for autonomous scaling and intelligent instance type selection on AWS. - SpotPilot for ML-driven prediction of spot instance terminations and proactive workload migration. </features> <target_audience> The primary target audience includes DevOps engineers, SREs, and IT operations teams responsible for managing Kubernetes environments, as well as businesses seeking to reduce cloud infrastructure costs and improve operational efficiency. </target_audience>

What does StackBooster do?

Where is StackBooster located?

StackBooster is based in Kyiv, Ukraine.

When was StackBooster founded?

StackBooster was founded in 2020.

How much funding has StackBooster raised?

StackBooster has raised 100000.

Location

Kyiv, Ukraine

Founded

2020

Funding

100000

Employees

10 employees

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StackBooster

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Executive Summary

stackbooster.webflow.io 100+

Crunchbase

Founded 2020 – Kyiv, Ukraine

Funding

Estimated Funding

$100K+

Team (10+)

No team information available.

Company Description

Problem

Managing Kubernetes clusters at scale presents significant operational overhead, leading to inefficient resource utilization and increased cloud expenditure. Traditional autoscaling solutions often react to existing load rather than proactively optimizing for future demand, and manual configuration of spot instances introduces complexity and potential downtime.

Solution

StackBooster provides an AI-driven automation platform designed to optimize Kubernetes cluster performance and reduce operational costs. The system proactively scales nodes and pods based on predicted demand, ensuring resources are precisely allocated. It dynamically right-sizes pods in real-time to prevent over-provisioning and continuously defragments clusters by selecting cost-efficient worker nodes. Furthermore, StackBooster enables zero-maintenance Kubernetes setups and leverages AI for seamless, zero-downtime management of spot instances, enhancing overall cluster reliability and availability.

Features

AI-powered proactive node and pod autoscaling for optimized cluster efficiency.

Dynamic pod right-sizing to ensure precise resource allocation based on real-time application needs.

Continuous cluster defragmentation through intelligent selection of cost-effective worker instances.

Zero-maintenance Kubernetes cluster setup with a simplified, click-based deployment process.

AI-driven spot instance handling for high availability and zero-downtime workload migration.

Advanced scheduling capabilities, including TopologyAwareScheduler, to reduce network latency and improve resource utilization.

StackSizing module for adaptive pod resource allocation and performance optimization.

ClusterPilot for autonomous scaling and intelligent instance type selection on AWS.

SpotPilot for ML-driven prediction of spot instance terminations and proactive workload migration.

Target Audience

The primary target audience includes DevOps engineers, SREs, and IT operations teams responsible for managing Kubernetes environments, as well as businesses seeking to reduce cloud infrastructure costs and improve operational efficiency.