Which cloud cost optimization tools offer automated resource scheduling to minimize costs?

AWS Cost Explorer: Offers Cost Explorer, which provides visibility into AWS usage and spending. Utilizes AWS Budgets to set custom cost and usage budgets. Provides recommendations for rightsizing instances and Reserved Instance purchases. Google cloud cost management: Provides Cost Management tools including Billing reports, Budgets, and Forecasts. Offers recommendations for rightsizing instances through tools like Rightsizing Recommendations. Azue Cost Management: Provides Cost Management and Billing features including Cost Analysis and Budgets. Offers Azure Advisor for cost optimization recommendations. Azure Automation allows users to schedule start and stop times for virtual machines using Runbooks.

for aws users, the cudos and cost intelligence dashboards are a great (almost) free way to get started understanding your costs. other tools - like those offered by BlueArch - enable ad hoc reporting for specific misconfigurations that could be contributing to a higher-than-average cloud bill.

As a proposal to map costs and know where the money is going, we use TAGs per resource. This way, we can export the billing for each resource and charge according to usage.

Cloud costs got you feeling the squeeze? Don't worry, we can turn you into a cost-cutting superhero! Look for optimization tools that focus on Scheduling Automation, your secret weapon for minimizing cloud bills: Imagine your resources as a team of superheroes: They work hard when needed, but don't waste energy on standby. Scheduling automation ensures your resources (cloud instances) are automatically scaled up during peak times and powered down when not in use. Think of it as having a wise manager who optimizes your team's workload for maximum efficiency! This frees up unused resources and reduces your cloud bill dramatically. It's like having a bottomless utility belt full of cost-saving tricks!

If we talk about AWS and Azure AWS provides - AWS Trust Advisor - AWS Cost Explorer Azure provides - Azure Advisor - Microsoft Cost Management on top of that, there should be proper auto scaling policies\resource polices to be enforced for the best utilization of resources

Automated resource scheduling is key among many strategies for cloud cost reduction. From my AWS savings experience, I recommend: 1 Scheduling AWS Lambda functions via Event Bridge to power down resources like containers and RDS post-business hours. 2 Using AWS Instance Scheduler to shut off EC2 VMs after hours. 3 Opting for "provisioned capacity" with auto-scaling and scheduled actions in DynamoDB over "on-demand". 4 Implementing retention policies for transitioning CW Logs to S3. 5 Employing S3 Lifecycle policies to transfer data across S3 storage classes. For more AWS cost-saving insights, follow me on LinkedIn!

i disagree with this analysis; cost optimization is about architecture; automation is just a portion of the work that goes into building efficient (and secure) applications. look for solutions that incentivize the right developer behavior for your org, and you’ll see productivity increases alongside your cost savings.

Automated scheduling ensures resources are only active when needed, avoiding costs associated with idle instances. Instances can be scheduled to start before peak usage hours and shut down during off-peak times, optimizing resource utilization. By turning off non-essential resources when not in use, organizations can significantly reduce their cloud spending. Allows for dynamic scaling of resources based on demand, ensuring that resources are available when required without overspending. Eliminates the need for manual intervention in starting and stopping instances, reducing administrative overhead. Users can define custom schedules tailored to their specific workload patterns and business requirements.

load balancers, and serverless functions are great ways to automatically scale applications based on user demand; both have their pros and cons and each should be evaluated based on what is best for the business (and the people who support it).

A good alternative is to program non-productive environments to automatically turn off after working hours and on weekends and if you are thinking about turning them on, really check the need.

By using an autoscaling strategy (e.g. using cluster-autoscaler or Karpenter) that prefers cheaper spot instances over more expensive "on-demand" nodes, can result in a great reduction of costs. The scheduler will try to get as many spot nodes as possible, but when that capacity gets limit or even revoked, on-demand nodes are requested straight away, so that the availability of your applications is never threatened.

Auto-scaling dynamically adjusts resource allocation in real-time based on metrics such as CPU utilization, memory usage, or incoming request rates. Horizontal scaling adds or removes instances to distribute incoming traffic across multiple resources, maintaining performance during traffic spikes. Vertical scaling adjusts the size (e.g., CPU, RAM) of individual instances based on workload requirements, optimizing resource utilization. Utilizes predictive analytics and machine learning algorithms to forecast future workload patterns. Considers cost implications when scaling resources, balancing performance requirements with cost optimization goals.

?? Maximizing cost savings in the cloud requires enforcing policies for automated resource scheduling. Here's how to do it effectively: ?? Set resource deployment policies ?? - Define maximum spend limits ?? - Specify preferred instance types ?? - Establish start/stop times for resources ? ?? Leverage cost optimization tools ?? - Automated policy enforcement ?? - Centralized policy management ??? - Real-time monitoring and alerts ?? ?? Enforce policies organization-wide ??? - Ensure adherence across all departments ?? - Prevent unauthorized or excessive resource usage ? - Maintain consistency in cost-saving strategies ????

policies are preventative and should be used to incentivize ideal development behavior; tools like cloud custodian (or bluearch ??) are great ways to implement cloud policies. an example scenario could be removing dashboard access (making programmatic provisioning of resources the only accepted method) and then restricting the instance types that can be provisioned by team, region, or tag.

Policies define guidelines for resource utilization, ensuring that only necessary resources are deployed according to predefined schedules. Policies dictate when resources should be provisioned or decommissioned based on workload patterns, business hours, or other criteria. Policies ensure compliance with organizational standards, industry regulations, and security requirements. Policies provide a standardized framework for managing resource deployment across different teams, projects, and environments. Policies support auto-scaling strategies by defining thresholds and triggers for scaling events based on workload characteristics.

?? In the ever-evolving multi-cloud landscape, embracing cloud agnosticism is key to maximizing efficiency and flexibility. Here's why: ?? Cloud-agnostic tools ?? - Work seamlessly across different cloud service providers - Enable consistent management and optimization strategies - Minimize the risk of vendor lock-in ?? Unified management ??? - Apply the same scheduling and scaling policies - Streamline operations across various cloud environments - Simplify management and reduce complexity ?? Future-proof your strategy ?? - Adapt to changing cloud ecosystem with ease - Leverage the best services from multiple providers - Stay agile and responsive to evolving business needs

ParkyMyCloud tool is specifically designed to help automatically identify and eliminate wasted cloud spend. ParkMyCloud can schedule start/stop times for idle cloud resources across AWS, Azure, Google Cloud, and Alibaba Cloud, making it a good option for cloud-agnostic environments.

unpopular opinion here, I say, pick one cloud, and go all in. In certain scenarios where enterprises have acquired applications or companies and are forced into a multi cloud scenario, it’s important to focus on a common data model between your cloud, observability and optimization applications.

Cloud-agnostic tools are not tied to a specific cloud provider, allowing organizations to choose the best services and features from different providers without vendor lock-in. Cloud-agnostic tools offer a unified management interface that works seamlessly across multiple cloud environments. Centralized cost management and optimization tools provide visibility into spending across all cloud providers, enabling organizations to identify cost-saving opportunities and optimize resource usage holistically. Cloud-agnostic security and compliance tools offer consistent monitoring, policy enforcement, and threat detection capabilities across different cloud environments.

FinOps cost optimization is ongoing work that should be present in any project sprint. It is also worth mentioning that the Costs pillar is part of the Well Architect Framework, which adheres to good architectural practices.

Workloads and usage patterns in the cloud can change over time due to factors such as seasonality, business growth, or evolving user demands. Cloud providers frequently introduce new services, features, and pricing models, which may impact cost optimization strategies. Advancements in cloud technologies, automation, and best practices may present new opportunities for optimizing costs and improving efficiency. Organizational priorities, business objectives, and resource requirements can evolve over time, necessitating adjustments to cost optimization strategies. Optimization efforts should balance cost reduction with performance, scalability, and reliability requirements.