Capillary reduced ops tickets by 95%

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Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

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Application Orchestration

Volcano

An open-source platform for orchestrating batch processing and AI workloads on Kubernetes

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Summary

A cloud-native batch scheduling system that extends Kubernetes' capabilities to handle batch processing and AI workloads efficiently. It provides features such as advanced job management, resource-aware scheduling, and support for heterogeneous workloads. With Volcano, organizations can effectively manage and optimize their batch processing tasks in Kubernetes clusters, improving resource utilization and reducing operational overhead.

Key Features

  • Volcano offers sophisticated job management capabilities, allowing users to define, submit, monitor, and manage batch processing jobs with ease.
  • The scheduler in Volcano is resource-aware, considering factors like CPU, memory, and GPU availability to optimize job placement and maximize resource utilization.
  • Volcano supports heterogeneous workloads, including traditional batch processing tasks and AI/ML workloads, enabling users to run a variety of applications on Kubernetes clusters.
  • Volcano's architecture is designed to be extensible, allowing users to customize and extend its functionality to meet their specific requirements.

How does it work?

Real world example

Pros

  • Offers sophisticated capabilities for managing batch processing jobs, including job queuing, prioritization, and scheduling.
  • Resource-aware scheduling optimizes job placement and resource utilization based on workload requirements and cluster availability.
  • Accommodates diverse workloads, including AI/ML tasks, data processing pipelines, and traditional batch jobs.
  • Architecture allows customization and extension to meet specific needs, with support for custom schedulers, plugins, and integration with existing systems.

Cons

  • Initial setup and configuration of Volcano may require additional effort, particularly in environments with specific infrastructure requirements or security considerations.
  • Resource-intensive workloads may require substantial infrastructure resources, including compute, storage, and networking, potentially leading to higher operational costs.
  • Scaling Volcano to large clusters or high-throughput environments may present challenges in certain scenarios, such as managing job concurrency, optimizing resource utilization, and ensuring reliability under heavy workloads.

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Capillary reduced ops tickets by 95%

“Our releases are fast. And with less developer time needed our teams can focus on building exciting features. We’ve saved countless hours and costs.”

Piyush K,
Chief Architect, Capillary Technologies,

Treebo reduced production issues by 70%

"With Facets, our staging environments look identical to production environments. So in case of production issues, we can be sure there are no infra drifts."

Kadam Jeet Jain,
Co-Founder & CTO, Treebo Hotels and Hotel Superhero.

GGX switched from AWS to GCP in 2 weeks

"Facets has radically changed our DevOps for the better. They did all the heavy lifting and saved us precious time and resources in our when we switched from AWS to GCP."

Kaustubh Bhoyar,
Head of engineering, GGX

Trusted by companies to run production at scale

Capillary | FacetsMPL | FacetsTreebo | FacetsPurplle | Facets

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