CAST AI vs. Harness: Which Solution is Better for Kubernetes?

Kubernetes poses several cost-related challenges. Fortunately, teams are working hard to help engineers take full advantage of Kubernetes without making their CFOs faint at the sight of their cloud bills.

CAST AI vs. Harness: Which Solution is Better for Kubernetes?

CAST AI and Harness are two examples of such tools. But which one is a better pick for teams looking to reduce their cloud expenses for running Kubernetes? Keep on reading to find out.


CAST AI – cloud native cost optimization platform 

Harness – automated cloud cost management

Created by cybersecurity experts, CAST AI is an ISO 27001-certified, comprehensive cloud automation platform for optimizing Kubernetes environments. Companies across e-commerce and adtech are using CAST AI to cut their cloud bills by 50% to even 90%.

Harness is a continuous integration and delivery platform for engineering and DevOps teams. The platform includes a cloud cost management module that supports cost savings in Kubernetes and offers Business Intelligence tools for analyzing cloud costs.

CAST AI vs. Harness – quick feature comparison

FeatureCAST AI 🥇Harness
Supported platforms
AWS
Google Cloud Platform
Microsoft Azure
Cost allocation and visibility
Detailed cost allocation
Automated cost forecasting
Cost reporting
Cost view across multi-cloud
Real-time alerts
Cost optimization and automation
5-minute first optimization
Free recommendations
Automated rightsizing
Multi-shape cluster construction
Autoscaling
Pod parameter-based autoscaling
Node autoscaling
Automatic bin packing
Spot instances
Full lifecycle automation
Capacity fallback guarantee

Detailed feature comparison of Harness and CAST AI

  1. Cost visibility
  2. Cost optimization and automation
  3. Spot instance automation
  4. Pricing

1. Cost visibility

Cost allocation and reporting 

In CAST AI, cloud costs are split into project, cluster, namespace, and deployment levels. Teams may track expenses down to individual microservices and then produce a full estimate of their cluster costs. The platform uses industry-standard metrics that work with any cloud service provider.

CAST AI‘s cost allocation works on a per cluster and per node basis. The team intends to expand the cost dimensions disclosed to include control plane, network, egress, storage, and others.

In addition, users can take advantage of the Cost report to track historical data of the cluster and see how its cost fluctuated over the time period, what the normalized cost per provisioned CPU was, and how much they’ll have to pay as the month ends, among others.

Harness offers in-depth Kubernetes visibility by displaying the utilized, idle and unallocated resources per workload and cluster. This data is paired with ready-made insights to help teams make the right decisions when taking action. Users can correlate costs generated by deployments and change the in replica count, CPU, or memory configurations, with the help of cost events and specific lines of code.

Moreover, Harness visualizes cost information by projects, teams, business units, departments, and more. Users can also get periodic reports on cost and usage metrics that matter to them most. Moreover, governing usage is easier thanks to custom budgeting, spend forecasts, and accounts for cost showbacks and chargebacks.

Real-time alerting

The CAST AI team is now working on real-time warning capabilities that will tell customers when their cloud spending exceeds a certain level, reducing the danger of an out-of-control service bill.

Harness identifies anomalies in cloud usage and informs users every time their expenses are greater or lower than they should be. The platform achieves that by analyzing historical spend trends.

Cost view across multi cloud

Multi cloud support is a key part of cost optimization efforts since many companies nowadays use more than one cloud platform.

Allocating expenditures for multi-cloud setups is tricky, but CAST AI‘s enhanced multi cloud capabilities make it much easier. Thanks to universal metrics from Grafana and Kibana, the platform easily operates with any cloud service provider and enables cross-cloud visibility.

Harness allows users to keep track of their cost and usage across all of their cloud resources. The tool displays insights on custom, data-driven dashboards to help teams track resources across every cloud provider they use.

2. Cost optimization and automation

CAST AI – fully automated cost optimization

Rightsizing with automated instance selection 

CAST AI chooses the best instance types and sizes to meet an application’s requirements while reducing cloud costs. When a cluster needs extra nodes, the automation engine selects the instances with the highest performance at the lowest cost. Given that everything is automated, teams don’t have to do anything extra.

The platform enables multi-shape cluster formation as using the same instance shape for every node in a cluster can easily lead to overprovisioning. CAST AI offers the right mix of multiple instance types for the application’s requirements.

Pod and node autoscaling

CAST AI automates pod scaling settings to help companies avoid overprovisioning their infrastructures. 

The autoscaler calculates the optimal number of necessary pod instances based on business KPIs. If there is no work to be done, the feature gradually reduces the replica count of your pods until it reaches 0 and then removes all pods. 

CAST AI also ensures that the number of nodes in use always matches the application’s needs, scaling nodes up and down automatically.

Cluster scheduling and termination

CAST AI automatically pauses and resumes clusters formed inside the platform so that teams avoid paying for resources they don’t use.

Bin packing is done automatically.

Kubernetes presents a financial challenge since it distributes apps evenly throughout a cluster, regardless of how cost-effective this architecture may be.

CAST AI alters the default pod scheduling strategy and employs automated bin packing to maximize savings based on user preferences. The cost reductions are larger when there are fewer nodes.

Harness – recommendations combined with automation

Detailed reports and recommendations

Harness provides its users with in-depth reporting combined with practical suggestions for cluster utilization, rightsizing, autoscaling, cleaning underutilized or orphaned resources, and picking the optimal cloud service tier based on usage trends. Before implementing the suggestions, users can perform a what-if analysis.

Another Harness feature that helps to optimize costs is the option to set monthly, quarterly, and yearly budgets or keep track of expense variations. Thanks to precise forecasting, teams can see whether their present spend rate is within budget or on its way to surpassing it. The alerts sent at key thresholds offer some extra help in tracking the budget consumption.

Harness offers a range of automated features as well. 

Automation in Harness:

  • Since non-production resources are usually used during working hours, teams can use the AutoStopping feature to turn them off anytime they’re not in use. If workloads are running on Spot instances, there’s no risk of disruption if users execute them on coordinated spot instances.
  • Harness Continuous Delivery provides cost information about your apps, services, and environments without the need for human tagging. This cuts a lot of time and effort teams would dedicate to this task to improve cost allocation and reporting.

3. Spot instance automation

Spot instances offer massive cost savings when compared to the cost of On-Demand instances – even up to 90%. But providers may reclaim these resources back at any time. That’s why automation is so important for teams that want to take advantage of Spot instances.

In CAST AI, the replacement of interrupted spot instances is entirely automatic. Teams no longer have to worry about their apps running out of capacity. To achieve high availability, the platform constantly looks for the best instance alternatives and spins new instances in a fraction of a second.

Harness recognized the value of Spot instances as well. Its users can run workloads on fully orchestrated Spot instances without having to worry about the interruption. The platform handles this part smoothly. 

4. Pricing

Users of CAST AI can begin by using the free Cluster Analyzer to see whether they can save money on their cloud services. The read-only agent evaluates their infrastructure and shares helpful recommendations. Next, you can either apply these insights manually or use automated cost optimization features. In the latter scenario, users can choose between two plans: Growth and Enterprise. CAST AI helps to achieve cost savings of at least 50%.

Harness offers three plans: Free, Team, and Enterprise. Note that the first two aren’t publicly available yet. In the Team plan, Harness charges 2.25% of the customer’s annual cloud spend. In the Enterprise plan, the company charges 2.50% of yearly cloud expenses.

Summary: CAST AI vs. Harness


Both Harness and CAST AI are excellent cloud cost management platforms that improve cost visibility, allocation, monitoring, and optimization.  

While Harness offers in-depth recommendations and a limited number of automation features, CAST AI provides teams with a number of handy automation features that guarantee cost savings and streamline work with Kubernetes. 

The comprehensive automation features and cloud native architecture position CAST AI as the top cloud cost optimization platform.

  • Blog
  • CAST AI vs. Harness: Which Solution is Better for Kubernetes?

Leave a reply

0 Comments
Inline Feedbacks
View all comments
Recent posts