Kazuhm VS Flagship.io

Kazuhm SaaS platform unifies the compute resources of an organization from desktops, to servers, to cloud, to edge, creating a private grid to place and process containerized workloads, optimize IT costs, security, and performance.

Through an easy user interface, customers leverage Kazuhm today to simplify Kubernetes and the deployment of popular data science applications, build their own private distributed compute networks, run workloads on-premises enabling the lowest possible latency, and easily manage multi-cloud and hybrid cloud environments.

Kubernetes-Made-Easy -- Set up and cluster deployment is super quick with container placement and host monitoring intuitively simple.

Multi-Cloud, Hybrid-Cloud Management -- Escape from vendor lock-in and centrally manage all your Public Cloud Hosts for FREE.

Data Science On Demand -- Simplify deployment of Spark and Jupyter and process workloads both on-premise and in the cloud.

Offset Cloud Costs -- Get “Cloud Smart”. Process containerized workloads on your Linux and Windows desktops and servers to offset cloud costs.

Low-Latency Workload Processing -- Reduce latency and improve performance by processing your data on-premise or at the edge – when milliseconds count.

Distributed Computing Anywhere -- Connect your desktops, both Windows and Linux, and servers or even your edge devices to create a powerful compute fabric.

Flagship.io is a feature flagging platform for modern engineering and product teams that eliminates the risks of future releases by separating code deployments from these releases. With Flagship.io, we push the concept of feature flags to the next level giving you full control over the release process. With Flagship.io, you can:

• Switch features on or off through remote config.
• Separate code deployments from future releases.
• Automatically roll-out your features gradually.
• Roll back any feature should any issues arise.
• Segment users by granting access to a feature based on certain user attributes.
• Carry out A/B tests by easily assigning feature variations to groups of users.