Kubernetes poses several cost-related challenges. Fortunately, teams are working hard to help engineers take care of cloud cost management without making their CFOs faint at the sight of the bill.
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.
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CAST AI – cloud-native cost management and monitoring |
Harness – automated cloud cost management |
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CAST AI is a comprehensive cloud automation and cost monitoring platform for optimizing Kubernetes environments. Companies across different industries use it to cut their cloud bills by even 90%. |
Harness is a continuous integration and delivery platform. It includes a cloud cost management module delivering savings and Business Intelligence tools for analyzing cloud expenses. |
CAST AI vs. Harness – quick feature comparison
| Feature | CAST 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 comparison of Harness and CAST AI
1. Cost visibility
Cost allocation and reporting
In CAST AI, cloud costs are split into project, cluster, namespace, and deployment levels. Teams can 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 per cluster and node basis. The platform gives you a better understanding of your CPU and memory provisioning by displaying the values of the requested, allocatable, and overhead resources.
In addition, the CAST AI team added a capability for ongoing cloud cost monitoring and reporting in mid-2022. This addition lets you break costs down by Kubernetes concepts such as clusters, workloads, and namespaces.
Harness offers in-depth Kubernetes visibility thanks to dashboards 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. 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.
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 added real-time warning capabilities that 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 vital 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 efficiently operates with any cloud service provider and enables cross-cloud visibility.
Harness allows users to keep track of their cost and usage across all 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 cloud cost management
Rightsizing with automated instance selection
CAST AI chooses optimal instance types and sizes to meet applications’ 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. Since 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 – cloud cost management recommendations and 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.
The platform offers two different sets of recommendations: performance-optimized and cost-optimized. The first can increase your expenses, while the latter may lead to system performance issues. Therefore, before using recommendations, Harness asks its users to ensure that they evaluate the recommendation’s impact thoroughly.
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. Additionally, cloud cost perspectives enable users to group their resources in ways that provide business insights.
Harness also offers a range of automated features.
Automation in Harness:
- Since non-production resources are mainly used during working hours, teams can use the AutoStopping feature to turn them off anytime they’re not in use. If workloads run on spot instances, users do not risk disruption if they 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 significantly cuts the time and effort teams would need to dedicate to improving cost allocation and reporting.
3. Spot instance automation
Spot instances offer massive cost savings compared to the cost of on-demand instances – 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. As a result, 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 recognizes the value of spot instances as well. Its users can run workloads on fully orchestrated Spot instances without worrying about interruptions. The platform handles this part smoothly.
4. Pricing
CAST AI users can begin using the free Savings Report to discover how much they can save money on their cloud services. The read-only agent evaluates their infrastructure and shares helpful recommendations. The free plan also includes cloud cost monitoring features which you can use for an unlimited number of clusters.
Next, they can either apply these insights manually or use automated cost optimization features. In the latter scenario, users can choose between three plans: Growth, Growth PRO, and Enterprise. CAST AI guarantees cost savings of at least 50%.
Harness‘ cloud cost management module offers three plans: Free, Team, and Enterprise. The free plan includes cost dashboards, budgets, optimization insights, anomaly tracking, and AutoStopping of unused resources. It is free for managing cloud spend of up to $250K cloud spend and two clusters.
Harness charges 2.25% of the customer’s annual cloud spend in the Team plan. In the Enterprise plan, the company charges 2.50% of yearly cloud expenses. Both premium plans provide custom dashboards, SLA, and increased security and governance options.
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 limited automation, CAST AI provides teams with many handy features that guarantee cost savings and streamline work with Kubernetes.
The comprehensive automation, cost monitoring, and cloud-native architecture position CAST AI as the top cloud cost optimization platform.
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