Managed application platform for growth-oriented cloud-native teams
Castor comes pre-integrated with 30+ services, with best practices, and sensible defaults. Complete hands-free operations after git-push
Fully automated CI/CD pipeline for quick, reliable, and secure Kubernetes deployments.
Git-push to live deployments in 5 mins.
Data-driven engineering with high visibility, simple configurations, and smart automations
Best-in-class principles codified for auto-scaling infrastructure, storage, and databases
Make product releases with high levels of certainty and predictability.
No more pre-deployment jitters and post-deployment fire-fighting.
Leverage best-practices in configuration and environment set-up. Have a strong foundation.
Serve your customers with zero downtime during product deployments and scaling.
Never miss planned deployments, scaling up/down opportunities, and testing cycles.
Mapping image vulnerability to running containers, allow only approved registries and images, automate base image upgrades, auto image-scanning.
Secure default configurations for service mesh, ingress, egress, namespaces etc
RBAC authorisation, continuous monitoring, secure access to Kubernetes API, and pod security standards adherence.
Move from DevOps to DevSecOps thinking.
Castor practices high standards for data and code integrity, access control vulnerability, privacy, and end-point security.
Get all the benefits of having a mature engineering team and an automated stack at a fraction of your current cost.
Avail best-in-class built-in optimisations. Pro-rata costs for servers, databases, file system storage. Smart defaults with Fargate Profiles, Spot instances etc. Custom configurations and consulting to lower cloud service costs
Zero manual errors. Lean engineering team. Productive development team.
Our team of engineers constantly upgrade and optimise every component to ensure highest levels of stability and security
Autoscaling database update
The latest Aurora serverless v2 database is now available to be used with Castor.
Kubernetes update
Castor is now compatible with Kubernetes v1.22. Clusters are available now for both the new k8s version along with support for those on the older version.
Load-balancer update
We have added a new component, Network load balancer with proxy protocol V2 is now available on Castor.
Storage update
Castor’s storage options now include both AWS EFS and EBS storage classes.
Ingress update
Our Ingress component now supports both TCP and UDP services.
Our fully managed services help engineering teams at all stages adopt Kubernetes with full functionality and minimum disruption
Reliable product releases and infrastructure from Day 1. Outsource engineering and DevOps automation to Castor. Grow at your own pace.
Stay ahead of market demand. We help you migrate your mature product to microservices, and automate your CI/CD pipeline with Castor.
Improve specific metrics of infrastructure cost, reliability, or downtime. Castor consulting provides domain-specific solutions. End-to-end automation.
Get an infrastructure that supports your growth plans.
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Our client’s core focus is a machine learning application. It was resource-intensive work, which was turning out very expensive to operate on the cloud. Limiting infrastructure expenses would in turn impact the productivity of the model - each iteration would cost them weeks to a few months. All the while, the data scientist experts are waiting on the model output. They were facing a dilemma - improve speed but lose on infrastructure costs or save costs but lose time and improvements to the ML model.
Castor implemented a solution that helped them make progress on both cost and velocity, at the same time. Castor implementation was configured to take advantage of spot instances, creating a system that decreased infrastructure costs. The manual processes were fully automated in DevOps, making auto-scaling available 24*7. Castor took care of the Kubernetes expertise required to support ML applications on the cloud.
The costs decreased to 33% since Castor, while the model processing time reduced 50%. Data experts were freed up from infrastructure tasks, and they had a lesser wait time between model iterations.
Talk to us about ML and big data specific optimization
