Service Catalog

Service Catalog

Choose from dozens of open source and commercial software to fit your needs. Then deploying your selections are made relaible, secure, stable by default.

We understand how hard it can be to analyze then select the appropriate combination of software to run your business. Our Service Catalog is your shortcut to finding the best-of-breed software and tools. Over fifty components to chose from are just a few clicks away.

Through this wizard-like experience, teams receive properly configured software stacks based on the selected application type and end-point environment. Using a DevOps-first approach, Dev and Ops teams now work together to build software stacks that are consistent across all environments. A single stack can be used to push stack images to any end-point - whether cloud or bare metal, whether test or prod. And in every case, you have the agility to deploy to any combination of end-points.

From choosing your software and tools to deployment your software is a matter of clicks not code. Dev and Ops teams collaborate to add value to the business in minutes not hours.




Mix and Match What You Need

Whether you choose one of our pre-generated stack templates, or you create your own - there is plenty of flexibility. Software and tools are ready at your fingertips to deploy when you need them and as you need them.

Pre-generated Stack
Stack Description
Available Tool Choices

Kubernetes Stack

Contains everything needed to secure and run a container-based set of services in a sound architectural way.
Docker, Kubernetes, Flannel, ElasticSearch, Kibana, Prometheus, Grafana, Sysdig, Vault, Dex, Traefik, Istio, PostgreSQL, MongoDB, Redis, Portworx, Minio, Okta

CI/CD Pipelines

A template consisting of the base tooling you need for continuous integration, continuous delivery and continuous deployment.
Jenkins, Spinnaker, GitLab, Docker Registry, Vault, Sonar, Selenium, WireMock, Artillery, Chaos Monkey, Okta

Machine Learning Stack

Machine learning toolset that supports the full lifecycle of an ML application. It provides cloud based tools for data ingestion, analysis, transformation, storage management, model training, simulation, and serving, as well as monitoring, logging, and other operational tools to run machine learning on Kubernetes.
KubeFlow, Jupyter Notebook, Keras, TensorFlow, TensorBoard, Caffe, Argo, Seldon, Minio, Ceph, HDFS, PostgreSQL, Cassandra